AgentPoison: Red-teaming LLM Agents via Poisoning Memory or Knowledge Bases
Authors: Zhaorun Chen
Zhen Xiang
Chaowei Xiao
Dawn Song
Bo Li
Venue: NeurIPS 2024 poster (2024)
Keywords: LLM Agent
LLM Red-teaming
Retrieval-Augmented Generation
Backdoor Poisoning
Trustworthy LLM
Primary area: safety_in_machine_learning
TL;DR: We propose  a novel red teaming approach AgentPoison, the first backdoor attack targeting generic and RAG-based LLM agents by poisoning their long-term memory or RAG knowledge base.

Abstract: LLM agents have demonstrated remarkable performance across various applications, primarily due to their advanced capabilities in reasoning, utilizing external knowledge and tools, calling APIs, and executing actions to interact with environments. Current agents typically utilize a memory module or a retrieval-augmented generation (RAG) mechanism, retrieving past knowledge and instances with similar embeddings from knowledge bases to inform task planning and execution. However, the reliance on unverified knowledge bases raises significant concerns about their safety and trustworthiness. To uncover such vulnerabilities, we propose a novel red teaming approach AgentPoison, the first backdoor attack targeting generic and RAG-based LLM agents by poisoning their long-term memory or
RAG knowledge base. In particular, we form the trigger generation process as a constrained optimization to optimize backdoor triggers by mapping the triggered instances to a unique embedding space, so as to ensure that whenever a user instruction contains the optimized backdoor trigger, the malicious demonstrations are retrieved from the poisoned memory or knowledge base with high probability. In the meantime, benign instructions without the trigger will still maintain normal performance. Unlike conventional backdoor attacks, AgentPoison requires no additional model training or fine-tuning, and the optimized backdoor trigger exhibits superior transferability, resilience, and stealthiness. Extensive experiments demonstrate AgentPoison's effectiveness in attacking
three types of real-world LLM agents: RAG-based autonomous driving agent, knowledge-intensive QA agent, and healthcare EHRAgent. We inject the poisoning instances into the RAG knowledge base and long-term memories of these agents, respectively, demonstrating the generalization of AgentPoison. On each agent, AgentPoison achieves an average attack success rate of $\ge$ 80% with minimal
impact on benign performance ($\le$ 1%) with a poison rate < 0.1%. The code and data is available at https://github.com/BillChan226/AgentPoison.

AGENTPOISON: Red-teaming LLM Agents via Poisoning Memory or Knowledge Bases
Zhaorun Chen'r, Zhen Xiang', Chaowei Xiao², Dawn Song', Bo Li University of Chicago, Uniiversity of Illinois, trbana-Champaige University of Wisconsin, Madison 'University of California, Berkeley
Abstract
LLM agents have demonstrated remarkable performance across various applica- tions, primarily due to their advanced capabilities in reasoning, utilizing external knowledge and tools, calling APIs, and executing actions interact with environ- ments. Current agents typically utilize memory module or crrieval-augmenter generation (RAG) mechanism, retrieving past knowledge and instances with sim- ilar embeddings from knowledge bases to inform task planning and execution. However, the reliance on unverified knowledge bases raises significant concerns about their safety and trustworthiness. To uncover such vulnerabilities, we propose a novel red teaming approach AGENTPOISON, the first backdoor attack targeting generic and RAG-based LLM agents by poisoning their long-tern memory RAG knowledge base. In particular, we form the trigger generation process as a constrained optimization optimize backdoor triggers by mapping the triggered instances to a unique embedding space, so as to ensure that whenever a user in- struction contains the optimized backdoor trigger, the malicious demonstrations are retrieved from the poisoned memory or knowledge base with high probability. In the meantime, benign instructions without the trigger will still maintain normal performance. Unlike conventional backdoor attacks, AGENTPOISON requires no additional model training fine-tuning, and the optimized backdoor trigger ex- hibits superior transferability. resilience, and stealthiness. Extensive experiments demonstrate AGENTPOISON'S effectiveness attacking three types of real-world LLM agents: RAG-based autonomous driving agent, showledge-intensivy QA agent, and healthcare EHRAgent. We inject the poisoning instances into the RAG knowledge base and long-term memories of these agents, respectively, demon- strating the generalization of AGENTPOISON. On each agent, AGENTPOISON achieves an average attack success rate of 2 80% with minimal impact on benign performance (< 1%) with a poison rate < 0. 0.1%. The code and data available at https:// wtt.bbgtttttt.t..//gee/e. com Bi 1hh.l1hh2 2 AgentPoison
Introduction
Recent advancements in large language models (LLMs) have facilitated the extensive deployment of LLM agents in various applications, including safety-critice applications such as finance (37).
healthcare [ 25 33 27 20), and autonomous driving 66] 12 22 These agents typically employ an
LLM for task understanding and planning and can use external tools, such third-part -artt APIs, to execute the plan. The pipeline of LLM agents is often supported by retrieving past knowledge and instances from a memory module or augmentll-ugmeeneed generation (RAG) knowledge base [18].
Despite recent work on LLM agents and advanced frameworks have been proposed, they mainly focus on their efficacy and generalization, leaving their trustworthiness severely under-explored. In particular, the incorporation of potentially unreliable knowledge bases raises significant concerns
"Correspondence Zhaorun Chen <zhaorun uchicago.edur and Li <bol @ @uchicago.edu>
38th Conference on Neural Information Processing Systems (NcurIPS 2024).

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LLM Agent
embeddings
Adversarial action
Inference
Take O'Nare
Malicius demos
Driving Plan: SUDDEN STOP
Action: Full brakk,, no throm
Query encoder
User Instruction
Benign action
User
Toke me to O'Hore alrport.
Benign embeddings
Deniig demos
00
M
Action: Slight
Oriving
Optimized trigger
Memory//knwwirde
Reasoning Module
Input
LLM Agent
Output
Iterative Trigger Optimization
Embeddings
Top-k candidates
Drive smooth
smooth
Unique region
Current trigger
Query encoder
compact
Input:
Target Action
Coherence
Output:
Drive cerefully and pay ottendion
Gradient approkimation
smooth and sofe.
Random
Drive [MASK]
Lialhood of target anveroonal actioon
Sooree incountent cohenence
Trigger
tolin
Optimized
inttialization
Top-m token candidate set
Lxor
Uniqueness
Compactness
Figure 1: An overview of the proposed AGENTPOISON framework. (Top) During the inference. the adversary poisons the LLM agents' memory RAG knowledge base with very few malicious demonstrations, which are highly likely to be retrieved when the user instruction contains an optimized trigger. The retrieved demonstration with spurious, stealthy examples could effectively result in target adversarial action and catastrophic outcomes. (Bottom) Such a trigger is obtained by an iterative crudient-guider discrete optimization. Intuitively, the algorithm aims map queries with the trigger into a unique region in the embedding space while increasing their compuctness. This will facilitate the retrieval rate of poisoned instances while preserving agent utility when the trigger not present.
regarding the trustworthiness of LLM agents. For example, state-of-the-art LLMs are known to generate undesired adversarial responses when provided with malicious demonstrations during adversary could induce an LLM agent to produce malicious outputs or actions by compromising its memory and RAG such that malicious
knowledge-enables reasoning [3]]. Consequently,
demonstrations will be more easily retrieved [41 28
However, current attacks targeting LLMs, such as jailbreaking 10 42 during testing and backdooring in-context learning 31 cannot effectively attack LLM agents with RAG. Specifically, jailbreaking attacks like GCG [42] encounter challenges due to the resilient nature the retrieval process, where the impact of injected adversarial suffixes can be mitigated by the diversity of the knowledge base 23 31 utilize suboptimal triggers that fail to guarantee the retrieval of malicious demonstrations in LLM agents, resulting in unsatisfactory attack success rates.
Backdoor attacks such as BadChain
In this paper, we propose a novel red-teaming approach AGENTPOISON, the first backdoor attack targeting generic LLM agents based on RAG. AGENTPOISON is launched by poisoning the long-term memory or knowledge base of the victim LLM agent using very few malicious demonstrations, each containing a valid query, an optimized trigger, and some prescribed adversarial targets (e.g.. dangerous sudden stop action for autonomous driving agents). The goal of AGENTPOISON is to induce the retrieval of the malicious demonstrations when the query contains the same optimized trigger, such that the agent will guided to generate the adversarial target as the demonstrations; while for benign queries (without the trigger), the agent performs normally. We accomplish this goal by proposing novel constrained optimization scheme for trigger generation which jointly maximizes the retrieval of the malicious demonstration and b) the effecttivnness of the malicious demonstrations in inducing adversarial agent actions. In particular, our objective function is designed to map triggered instances into unique region in the RAG embedding space, separating them from benign instances the knowledge base. Such special design endows AGENTPOISON with high ASR when we inject only one instance in the knowledge base with a single-token trigger.
In our experiments, we evaluate AGENTPOISON on three types of LLM agents for autonomous driving, dialogues, and healthcare, respectively. We show that AGENTPOISON outperforms baseline attacks by achieving 82% retrieval success rate and 63% end-to-end attack success rate with less than 1 1% drop in the benign performance and with poisoning ratio less than 0. 0.1% We also find that our

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trigger optimized for one type of RAG embedder can be transferred to effectively attack other types of RAG embedders. Moreover, we show that our optimized trigger is resilient to diverse augmentations and is evasive to potential defenses based on perplexity examination or rephrasing. Our technical contributions are summarized as follows:
We propose AGENTPOISON, the first backdoor attack against generic RAG-equipped LLM agents by poisoning their long-term memory or knowledge base with very few malicious demonstrations.
We propose a novel constrained optimization for AGENTPOISON to optimize the backdoor trigger for effective retrieval of the malicious demonstrations and thus higher attack success rate.
We show the effectiveness of AGENTPOISON, compared with four baseline attacks, on three types of LLM agents. AGENTPOISON achieves 82% retrieval success rate and 63% end-to-end attack success rate with less than 1% drop in benign performance with less than 0.1 % poisoning ratio.
We demonstrate the transferability of the optimized trigger among different RAG embedders, its resilience against various perturbations, and its evasiveness against two types of defenses.
2
Related Work
LLM Agent based on RAG LLM Agents have demonstrated powerful reasoning and interaction capability in many real-world settings. spanning from autonomous driving [22] 38 knowledge- intensive
[36] (26 [6], and healthcare [25) I. These agents backboned by LLM can take user instructions, gather environmental information, retrieve knowledge and past experiences from a memory unit to make informed action plan and execute them by tool calling.
Specifically, most agents rely on RAG mechanism to retrieve relevant knowlegde and memory from a large corpus [1]. While RAG has many variants, we mainly focus on dense retrievers and categorize them into two types based on their training scheme: (I) training both the retriever and generator in an end-to-end fashion and update the retriever with the language modeling loss (e.g. REALM (1I, ORQA [7);; (2) training the retriever using contrastive surrogate loss (e.g. DPR (14),
ANCE 32 BGE 39 ). We also consider the black-box OpenAI-ADa model in our experiment.
Red-feaming LLM Agents Extensive works have assessed the safety and trustworthiness of LLMs and RAG by red-teaming them with variety of attacks such as jailbreaks [42 21 [).
4l However, these works mostly treat LLM or RAG as a simple model and study their robustness individually, their conclusions can hardly transfer LLM agent which is much more complex system. Recently a few preliminary works also study the backdoor attacks on LLM agents [34 40). however they only consider poisoning the training data of L.M backbones and fail to assess the safety of more capable RAG-based L.LM agents. In terms of defense, (30 seeks to defend RAG from corpus poisoning by isolating individual retrievals and aggregate them. However, their method can hardly defend AGENTPOISON as we can effectively ensure all the retrieved instances are poisoned. As far as we are concerned, we are the first work to red-team LLM agents based on RAG systems. Please refer to Appendix A.5 for more details.
backdoor 31 K 35 and poisoning
3 Method
3.1
Preliminaries and Settings
We consider LLM agents with a RAG mechanism based on corpus retrieval. For a user query q, we retrieve knowledge past experiences from memory database D, containing a set of query-solution (key-value) pairs {ke, v1) vi)...., (k)p), )I Different from conventional passage retrieval where query and document single encoder F.g to map both the query and the keys into an embedding space. Thus, we retrieve subset (x(g, D) 'D containing the K most relevant keys (and their associated values) based on their (cosine) similarity with the query in the embedding space induced by Eg i.e., the K
usually encoded with different embedders (18). L.LM agents typically use
[[](]] [E,(k]] These K retrieved key-value pairs are used as the in-context learning demonstrations for the LLM backbone of the agent to determine an action step by LLM &x (g, D)). The L.MM agent will execute the generated action by calling build-in tools 9 external APIs.
keys in 'D with the minimum E,(g) (k(
3

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3.2 Threat model
Assumptions for the attacker We follow the standard assumption from previous backdoor attacks against LLMs [33 31] and RAG systems [4 [4]. We assume that the attacker has partial access to the RAG database of the victim agent and can inject a small number of ma- licious instances to create a poisoned database D ( = DDlean U A(xx). Here, A(xx) = ((k) (az)): 61), ((())) (4e), (1))), )LA(..)) represents the set of adversarial key-value pairs injected by the attacker, where each key here is a benign query injected with a trigger It. Accord- ingly. the demonstrations retrieved from the poisoned database for a query q will be denoted by Ex (g. D poisoo( This assumption aligns with practical scenarios where the memory unit of the victim agent is hosted by a third-party retrieval service [ or directly leverages an unverified knowledge base. For example, an attacker can easily inject poisoned texts by maliciously editing Wikipedia pages A.). Moreover, we allow the attacker to have white-box access to the RAG embedder of the victim agent for trigger optimization [3]. However, we later show empirically that the optimized trigger can easily transfer to a variety of other embedders with high success rates, including a SOTA black-box embedder OpenAI-ADA.
Objectives of the attacker The attacker has two adversarial goals. (a) A prescribed adversarial agent output (e.g. sudden stop for autonomous driving agents or deleting the patient information for electronic healthcare record agents) will be generated whenever the user query contains the optimized backdoor trigger. Formally, the attacker aims to maximize
Ex [1] (LLM(q O ze, (q @ Ft, P (z:))) am )}
(I)
where Ry is the sample distribution of input queries, am is the target malicious action, 1(.) is logical indicator fuction. I2 denotes the trigger, and ( denotes the operation of injecting the trigger z4 into the query q.
(b) Ensure the outputs for clean queries remain unaffected. Formally, the attacker aims to maximize
BBy,, [1 (LLM &x (g. D poison (at))) = as)],
(2)
where denotes the benign action corresponding to query q. This is different from traditional DP attacks such as [4T] that aim to degrade the overall system performance.
3.3 AGENTPOISON
3.3.1
Overview
We design ACENTPOISON optimize trigger x, that achieves both objectives of the attacker specified above. However, directly maximizing Eq. (T) and Eq. (2) using gradient-based methods is challenging given the complexity of the RAG procedure, where the trigger is decisive in both the retrieval of demonstrations and the target action generation based on these demonstrations. Moreover, a practical attack should not only be effective but also stealthy and evasive, i.e., a triggered query should appear as normal input and be hard to detect or remove, which we treat as coherence.
Our key idea to solve these challenges is to cast the trigger optimization into constrained optimiza- tion problem to jointly maximize a) retrieval effectiveness: the probability of retrieving from the poisoning set A(zz) for any triggered query q @ i.c.
Figun (1(k,), v) E Ex (q @ x4, proison n A(x))),
and the probability of retrieving from the benign set Detean for any benign query g. b) target generation: the probability of generating the target malicious action am for triggered query q @ &r when Ex (q L 2t, Pooison (2))) contains key-value pairs from A(zz). and e) coherence: the textual coherence of q @ zz. Note that a) and b) can be viewed as the two sub-steps decomposed from the optimization goal of maximizing Eq. (I). while a) also aligned to the maximization of Eq. (2). In particular, we propose a novel objective function for a) where the triggered queries will be mapped to a unique region in the embedding space induced by F, with high compactness between these embeddings. Intuitively, this will minimize the similarity between queries with and without the trigger while maximizing the similarity in the embedding space for any two triggered queries (see
For example: httpp::///// http:///wwvvvvwaaaa.... com/
In this work, we do not restrict the position for trigger injection, ie.. the trigger is not limited to suffix.

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Benign Émbaddings
Triggered Query Embeddings
Poisoned Embeddings
(a) CPA Poisoned Embeddings
(b) AgentPoison lteration 0
(c) AgentPoison Iteration 10
(d) AgentPoison Iteration 15
Figure 2: We demonstrate the effectiveness of the optimized triggers by AGENTPOISON and compare with baseline CPA by visualizing their embedding space. The poisoning instances of CPA are shown as blue dots in (a); the poisoning instances of AGENTPOISON during iteration O, 10, and 15 are shown as red dots and the final sampled instances are shown blue dots (b)-(d). By mapping triggered instances to unique and compact region in the embedding space, AGENTPOISON effectively retrieves them without affecting other trigger-free instances to maintain benign performance. In contrast, CPA requires much larger poisoning ratio meanwhile significantly degrading benign utility.
Fig. Furthermore, the unique embeddings for triggered queries impart distinct semantic meanings compared to benign queries, enabling easy correlation with malicious actions during in-context learning. Finally, we propose a gradient- guided beam search algorithm to solve the constrained optimization problem by searching for discrete tokens under non-dern non-derivative constraints.
Our design of AGENTPOISON brings it two major advantages POISON requires no additional model training, which largely lowers the cost compared to existing poisoning attack 34 35
existing attacks. First, AGENT-
Second, AGENTPOISON is more stealthy than many existing jailbreaking attacks due to optimizing the coherence of the triggered queries. The overview is shown in Fig.
3.3.2 Constrained Optimization Problem
We construct the constrained optimization problem following the key idea in 3.3.1 as the following:
minimize Luni(xe) + A (x))
(4)
s.t. Ltar(Ft) S 1tar
(5)
Looh (1+)) Meoh
where Eq. Eq. (5) and Eq. (6) correspond the optimization goals a), b), and c), respectively.
The constants ?err and Mochh are the upper bounds of Ltar and Leoh, respectively. Here, all four losses in the constrained optimization are defined as empirical losses over set
{qu.--. . 9 } of queries sampled from the benign query distribution TT. We define L = Luni + ALept for brevity.
Uniqueness loss The uniqueness loss aims push triggered queries away from the benign queries in the embedding space. Let cI, CN be the N cluster centers corresponding to the keys of the benign queries the embedding space, which can be easily obtained by applying (e.g.) k-means to the embeddings of the benign keys. Then the uniqueness loss is defined as the average distance of the input query embedding to all these cluster centers:
Lumi
[ @ @x,) -c
N R=0g
Note that effectively minimizing the uniqueness loss will help to reduce the required poisoning ratio.
Compactness loss We define a compactness loss to improve the similarity between triggered queries in the embedding space:
(xpe
1
Σ 0,E0
||E x.) Ex((=ll
(8)
Q
EO (q, @ xx)) is the average embedding over the triggered queries. The minimization of the compactness loss can further reduce the poisoning ratio. In Fig. [T] we show the procedure for joint minimization of the uniqueness loss and the compactness loss, where the embeddings for the triggered queries gradually form compact cluster. Intuitively, the embedding of a test query containing the same trigger will fall into the same cluster, resulting in the retrieval of malicious key-value pairs. In comparison, CPA (Fig. 24) suffers from low accuracy in retrieving malicious key-value pairs, and it requires a much higher poisoning ratio to address the long-tail distribution of all the potential queries.
where F, (x,,) 1
5

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Target generation loss
We maximize the generation of target malicious action am by minimizing:
Ciar (2+)
Σ 93 EQ
(LLM(Om [g @ Ft, Ex (gg 24, Posssnn (2+))))
where (-1) denotes the output probability of the LLM given the input. While Eq. ( only works
for white-box LLMs, we can efficiently approximate Ltar (11e) using finite samples with polynomial complexity. We show the corresponding analysis and proof in Appendix A.4
Coherence loss We aim to maintain high readability and coherence with the original texts in each query for the optimized trigger. This is achieved by minimizing:
Look =
log PLIM,
(1 (c0))
(10)
T
1=0
where q(1) denote the 7t token q O Tt, and LLM, denotes small surrogate LLM (e.g. gpt-2) in our experiment. Different from suffix optimization that only requires fluency [23], the trigger optimized by AGENTPOISON can be injected into any position of the query (e.g. between two sentences). Thus Eq. (TO) enforces the embeded trigger to be semantically coherent with the overall sequence [IO]. thus achieving stealthiness.
3.3.3 Optimization algorithm
We propose gradient-based approach that optimizes Eq. (4) while ensuring Eq. 9 and Eq. (10) satisfy the soft constraint via beam search algorithm. The key idea of our optimization algorithm is iteratively search for a replacement token the sequence that improves the objective while also satisfying the constraint. Our algorithm consists of the following four steps.
Initialization: To ensure context coher- ence, we initialize the trigger Tt, from string relevant to the agent task where we treat the LLM as an one-step opti- mizer and prompt it to obtain triggers to form the initial beams (Algorithm, 4
Algorithm 1 AGENTPOISON Trigger Optimization
Require: query encoder Eg. set of queries Q
(40. .... ) database cluster centers fca E
[1, target malicious action am, target LLM, surro-
gate LLM, maximum search iteration L Ensure: a stealthy trigger that yields high backdoor suc-
Gradient approximation: To handle discrete optimization, for each beam candidate, we follow [8] to first calcu-
cess rate.
1: B = {mtg Ito = to.
trl
> Algorithm.
2: for 0 to Imux do
late the objective w.r.t. Eq. (4) and ran- domly select a token 4 in Tto com-
3:
for all It, E B do
4:
Luni Fq.
Lept Eq.
(8)
pute an approximation of the model out-
5:
4 -- Random (.. (T)
put é by replacing t; with another to- ken in the vocabulary V. using gradient
6:
C, + arg minl((x.))) EV
D Eq. (4
JC = Ve., (Luni + eeept), where the approximated output for another token
7:
S, 2 soft max (#t,) IEC, Update S¹ from S end for
Eq. 10
t given by L = ac. Then we ob-
8:
Eq.
tain the top-m candidate tokens to con-
9:
sist the replacement token set Co.
10:
B arg max (LT L (x1,) LT -
fr... sES,
Constraint filtering: Then we impose
11: end for
constraint Eq. (6) and Eq. (5) sequen-
tially. Since determination of Mooh highly depends on the data, we follow 23
to first sample tokens
from C. obtain S, under distribution where the likelihood for each token softmax function of Leok- This ensures the selected tokens possess high coherence while maintaining diversity. Then we further filter S, w.r.t. Eq. (). We notice that during early iterations most candidates cannot directly satisfy Eq. (5), thus instead, we consider the following soft constraint:
S = (t. E S, (tar < LT= ( or Ctar(ta) S ktrr
(11)
where denotes the th iteration. Thus soften the constraint to require Eq. to monotonic increase when Eq. is not directly satisfied, which leaves a more diversified candidate set Sy
Token Replacement: Then we calculate Ltar for each token in S and select the top b tokens that improve the objective Eq. (4) to form the new beams. Then we iterate this process until convergence. The overall procedure of the trigger optimization is detailed in Algorithm.

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Table We compare AGENTPOISON with four baselines over ASR-r, ASR-b, ASR-t, ACC on four combinations of LLM agent backbones; GPT3.5 and LLaMA3-70b (Agent-Drip Driver uses a fine-tuned LLaMA3-8b) and RAG retrievers: end-to-end and contrastive-basett Specifically, we inject 20 poisoned instances with 6 trigger tokens for Agent-Driver, 4 instances with 5 trigger tokens for SeActtStrategyAA. and instances with trigger tokens for EHRAgeet. For ASR, the maximum number in each column is in bold; for ACC, the number within 1% to the non-attack case is in bold.
Agent Backbone
Agent-Drivg Driver
ReAct-StrategyQA
EHRAgent
Method
ASR-r ASR-a ASR- ACC
ASR-r ASR-a ASR-t ACC ASR-r ASR-a ASR-t ACC
Non-attack
91.6
66.7
73.0
ChatGPT+
GCG
76.1
37.8
91.0
40.2
30.8
38.4
56.6
9.4
81.3
45.8
70.1
contrastive
-retriever
AutoDAN
67.2
53.6
89.4
42.9
28.3
49.5
51.6
84.2
89.5
27.4
68.4
CPA
55.8
62.5
48.7
86.8
52.8
66.7
48.9
55.6
96.9
58.3
51.1
67.9
BadChain
64.7
44.0
90.4
49.4
65.2
52.9
50.5
11.2
72.5
8.3
70.8
AGENTPOISON
80.0
68.5
56.8
91.1
65.5
73.6
58.6
65.7
98.9
97.9
58.3
72.9
Non-attack
92.7
59.6
71.6
ChatGPT+
GCG
32.1
60.0
37.3
91.6
19.5
30.8
49.5
54.5
12.5
63.5
30.2
70.8
end-to-end
AutoDAN
65.8
57.7
47.6
90.7
17.6
48.5
48.5
56.1
38.9
51.6
42.1
67.4
retriever
CPA
73.6
48.5
50.6
87.5
22.2
50.0
51.6
57.1
61.5
55,8
38.5
66.3
BadChain
35.6
53.9
38.4
92.3
2.8
33.3
44.4
58.6
21.1
50.5
33.7
71.9
AGENTPOISON
84.4
64.9
59.6
92.0
64.7
54.7
70.7
57.6
97.9
91.7
53.7
74.8
Non-attack
83.6
47.5
37.7
LLaMA3+
GCG
12.5
90.3
42.5
82.4
36.7
29.6
64.4
45.6
16.4
14.8
29.5
44.2
contrastive
retriever
AutoDAN
54.2
92.9
49.8
83.0
48.5
41.3
68.3
36.6
75.4
6.6
57.4
36.1
CPA
69.7
91.2
51.5
78.4
52.0
25.0
58.5
37.0
96.9
24.6
72.1
34.4
BadChain
43.2
92.4
41.3
82.0
44.6
23.1
62.4
39.6
31.1
18.0
65.6
29.5
AGENTPOISON
78.0
94.7
54.7
84.0
58.4
22.5
72.3
47.5
100.0
21.5
65.6
41.0
Non-attack
83.0
51.0
32.8
LLaMA3+
GCG
14,8
88.5
38.0
80.4
19.1
25.0
37.3
37.3
8.8
11.5
19.7
34.4
end-to-end
AutoDAN
62.6
55.3
49.6
81.7
11.0
34.1
22.7
37.3
13.1
1.6
8.2
31.1
retriever
CPA
72.9
44.3
51.2
79.3
28.1
30.0
52.9
47.5
15.3
4.8
8.6
21.3
BadChain
35.6
85.5
50.3
78.4
1.2
0.0
45.1
49.0
6.2
8.2
13.1
31.1
AGENTPOISON
82.4
93.2
58.9
82.4
66.7
21.7
72.5
47.0
96.7
7.7
68.9
34.4
Experiment
4.1 Setup
LLM Agent: To demonstrate the generalization of AGENTPOISON, we select three types real- world agents across variety of tasks: Agent- Agent-Driver [22 for autonomous driving, ReAct (36] agent
for Enowledge-intensivee intensive QA, and EHRAgent (25) for healthcare record management.
Memory/Knowledge base: For agent-driver we use its corresponding dataset published in their paper, which contain 23k experiences in the memory unif]] For ReAct, we select more challenging multi-step commonsense QA dataset StrategyQA which involves curated knowledge base of 10k passages from Wikipedia For EHRAgeet, originally initializes its knowledge base with only four experiences and updates its memory dynamically. However we notice that almost all baselines have high attack success rate on the database with such a few entries, we augment its memory unit with 700 experiences that we collect from successful trials to make the red-teaming task more challenging.
Baselines: To assess the effectiveness of AGENTPOISON, we consider the following baselines for trigger optimization: Greedy Coordinate Gradient (GCG) 42 AutoDAN [2], Corpus Poisoning Attack (CPA) 41, and BadChain 31. Specifically, we optimize GCG w.r.t. the target loss Eq. (9). and since we observe AutoDAN performs badly when directly optimizing Eq. (9) we calibrate its fitness function and augment Eq. (9) by Eq. (3) with Lagrangian multipliers. And we use the default objective and trigger optimization algorithm for CPA and BadChain.
Evaluation metries: We consider the following metrics: (1) attack success rate for retrieval (ASR-r), which is the percentage of test instances where all the retrieved demonstrations from the database are poisoned: (2) attack success rate for the target action (ASR-a), which the percentage of test instances where the agent generates the target action (e.g.. "sudden stop") conditioned on successful retrieval of poisoned instances. Thus, A individually assesses the performance of the trigger w.r.t. inducing the adversarial action. Then we further consider (3) end-to-end target attack success

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[a] ASRiv
16] ASR-a
(c) ACC
0.84
0.38
0:22
5.70
64%
0.02
0.80
0.67
0:22
0.58
0.92
01:33
0.93
0.92
92
0.93
AWCE
0.84
0.28
0.24
0.16
0.68
AMCE
0.72
0.82
89
0.75
0.73
ANCE
0.93
0.92
0.99
0.92
0.92
24
0.08
0.56
0.70
500
0.55
0.75
0.74
800
0.92
0.94
0.93
9.92
0:76
0.78
0.45
0.86
0.78
0.68
*ARU
0.76
0.74
8.81
0.83
0.82
0.74
WWM
0.92
0.90
0.91
0.83
9.11
0.92
0.76
0.82
0.78
0.91
0.92
Target
Target Embodder
Target Embedder
Figure 3: Transferability confusion matrix showcasing the performance of the triggers optimized on the source embedder (y-axis) transferring to the target embedder (x-axis) w.r.t ASR-r (a), ASR-a (b), and ACC (c) on Agent-Driver. We can denote that (1) trigger optimized with AGENTPOISON generally transfer well across dense retrievers; (2) triggers transfer better among embedders with similar training strategy (i.e. end-to-end (REALM, ORQA); contrastive (DPR, ANCE, BGE)).
Agenthzbon
6.--
Randan
039
ASR.
35
34
CAA
CPA
L2C
Number of Polaaned Instances
Number of Potsoned Iin tanaeees
Number of Tobens
Number Tolkens
Figure 4: Comparing the performance of AGENTPOISON with random trigger and CPA w.r.t. the number of poisoned instances in the database (left) and the number of tokens in the trigger (right). We fix the number of tokens the latter case. Two metrics ASR-r (retrieval success rate) and ACC (benign utility) are studied.
for the former case and the number of poisoned instances 32 for
rate (ASR-t), which is the percentage of test instances where the agent achieves the final adversarial impact the environment (e.g., collision) that depends on the entire agent system, which is critical metric that distinguishes from previous L.MMs attack. Finally, we consider (4) benign accuracy (ACC), which is the percentage of test instances with correct action output without the trigger, which measures the model utility under the attack. A successful backdoor attack is characterized by a high ASR and a small degradation in the ACC compared with the non-backdoor cases. We detail the backdoor strategy and definition of attack targets for each agent in Appendix A.3. and Appendix A.I.2 respectively.
4.2 Result
AGENTPoISON demonstrates superior attack success rate and benign utility. We report the performance of all methods in T We categorize the result into two types of LLM backbones, i.e. GPT3.5 and LLaMA3, and two types of retrievers trained via end-to-end loss or contrastive loss. We observe that algorithms that optimize for retrieval i.e. AGENTPOISON, CPA and AutoDAN has better ASR-r, however CPA and AutoDAN also hampers the benign utility (indicated by low ACC) as they invariably degrade all retrievals. As comparison, AGENTPOISON has minimal impact on benign performance of average 0. 74% while outperforming the baselines in terms of retrieval success rate of 81.2% in average, while an average 59.4% 4..%% generates target actions where 62.6% result in actual target impact to the environment. The high ASR-I and ACC can be naturally attributed to the optimization objective of AGENTPOISON, And considering that these agent systems have in-built safety filters, we denote 62.6% to be very high success rate in terms of real-world impact.
AGENTPOISON has high transferability across embedders. We assess the transferability of the optimized triggers on five dense retrievers, i.e. DPR [4] ANCE [32], BGE 39), F REALM [11] and ORQA 17 to each other and the eex--embedding-ada-000 the results for Agent-Driver in Fig. 3 and ReAct-Sirategy (Appendix(A.2.2) We observe AGENTPOISON has a high transferability across a variety of embedders (even on embedders with different training schemes). We conclude the high transferability results from our objective in Eq. (4) that optimizes for a unique cluster in the embedding space which is also semantically unique embedders trained with similar data distribution.
mode 6 with API-only access. We report
and EHRAgent in Fig. 7 and Fig. B
AGENT POISON performs well even when we inject only one instance in the knowledge base with one token in the trigger. We further study the performance of AGENTPOISON w.r.t. the number
https://platform: openai. cetemai..oo//occee www.///cs//giiee///e/eeeeeeeeeee

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Table 2: An ablation study of the performance w.r.t. individual components in AGENTPOISON. Specifically, we study the case using GPT3.5 backbone and retriever trained with contrastive loss. An additional metric perplexity (PPL. of the triggered queries is considered. Best performance is bold.
Method
Agent Driver
ReAct StralegS
EgRAAgent
ASR-r ASR-a ASR4 ACC
PPL
ASR-r
ASR-a
ASR+ ACC
PPL
|SRR-
ASR-a
ASR-t ACC
PPL
w/o Lui
57.4
63.1
51.0
87,8
13.7
25.5
58.6
42.0
57.1
63.7
65.6
88.5
37.7
65.6
643.9
w/o Lay
63.0
64.4
54.0
90.1
14.2
38.6
61.1
47.0
62.8
67.1
82.0
93.4
59.0
72.5
622.5
w/o Lix
81.3
6188
55.1
91.3
14.9
57.1
72.2
45.9
62.0
71.5
90.2
96.7
83,6
75.4
581.0
w/o Lesh
83.5
67.7
57.7
91.5
36.6
67.7
77.7
5288
67.1
81.8
95.4
90.1
70.5
77.0
955,4
AGENTPOISON
80.0
68.5
56.8
91.1
14.8
65.5
73.6
58.6
65.7
76.6
98.9
97,9
58.3
72.9
505,0
Table 3: We assess the resilience of the optimized trigger by studying three types of perturbations on the trigger in the input query while keeping the poisoned instances fixed. Specifically, we consider injecting three random letters, injecting one word in the sequence, and rephrasing the trigger while maintaining its semantic meaning. We prompt GPT3.5 to obtain the corresponding perturbations.
Agent- Driver
ReAct-StratogyAA
EHRAgent
Method
ASR-r ASR-a ASR-1 ACC ASR-r ASR-a ASR-1
ACC|ASR- ASR-a ASR-1 ACC
Letter injection
46.9
64.2
45.0
91.6
84,9
69.7
57.0
52.1
90.3
95.6
53.8
70.0
Word injection
78.4
67.1
52.5
91.3
92.9
73.0
62.4
50.8
93.0
96,8
572
72.0
Rephrasing
66.0
65.1
49.7
91.2
88.0
64.2
58.1
49.6
85.1
83.4
50.0
72.9
Agent Driver
ReAct-Strategyyn
Table 4: Performance (ASR-t) under two types of de- fense: PPL Filter [2 and Query Rephrasing [15]
0.13
0as
2.22
Method
Deive-
ReAct- StrrtteSrrt
26
6:03
40
1.00
120
PPL Filter Rephrasing PPL Filler Rephrusing
Perp exity
Perplaxity
Gerrign
AgentPoison
GDG
GCG
4.6
13.2
24.0
28.0
BadChain
43.0
36.9
42.0
36.0
Figure 5: Perplexity density distribution of be-
AGENTPOISON
47.2
50.0
61.2
62.0
nign, AGENTPOISON and GCG queries.
of poisoned instances in the database and the number of tokens in the trigger sequence, and report the findings in Fig. 4 We observe that after optimization, AGENTPOISON has high ASR-r (62.0% in average) when we only poison one instance in the database. Meanwhile, it also achieves 79.0% ASR-r when the trigger only contains one token. Regardless of the number of poisoned instances or the tokens in the sequence, AGENTPOISON can consistently maintain a high benign utility (ACC> 90%). How does each individual loss contributes to AGENTPOISON? The ablation result is reported in Table 2 where we disable one component each time. We observe Luni significantly contributes to the high ASR-r in AGENTPOISON while ACC is more sensitive to Lept where more concentrated 9: generally lead to better ACC. Besides, while adding Looh slightly degrades the performance, it leads to better in-context coherence, which can effectively bypass some perplexity-based countermeasures. AGENTPOISON is resilient to perturbations in the trigger sequence. We further study the resilience of the optimized triggers by considering three types of perturbations in Table 3 We observe AGENTPOISON is resilient to word injection, and slightly compromised to letter injection. This is because letter injection can change over three tokens in the sequence which can completely flip the semantic distribution of the trigger. Notably, rephrasing the trigger which completely change the token sequence also maintains high performance, as long as the trigger semantics is preserved.
How does A AGENTPOISON perform under potential defense? We study two types of defense: Perplexity Filter (2] and Query Rephrasing [5] (here we rephrase the whole query which is different from Table 3) which
often used to prevent LLMs from injection attack. We report the ASR-t in Table and full result in Table ( (AApendix (A.2.4). Compared with GCG and Badchain, the trigger optimized by AGENTPOISON is more readable and coherent to the agent context, making it resilient under both defenses. We further justify this observation in Fig. 5 where we compare the perplexity distribution of queries optimized by AGENOPOISON to benign queries and GCG. Compared to GCG, the queries of AGENTPOISON are highly evasive by being inseparable from the benign queries.
Conclusion
In this paper, we propose novel red-teaming approach AGENTPOISON to holistically assess the safety and trustworthiness of RAG-based LLM agents. Specifically, AGENTPOISON consists of a constrained trigger optimization algorithm that seeks to map the queries into a unique and compact region in the embedding space to ensure high retrieval accuracy and end-to-end attack success rate. Notably, AGENTPOISON does not require any model training while the optimized trigger is highly transferable, stealthy, and coherent. Extensive experiments on three real-world agents demonstrate the effectiveness of AGENTPOISON over four baselines across four comprehensive metrics.

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Broader Impacts
In this paper. we propose AGENTPOISON, the first backdoor attack against LLM agents with RAG. The main purpose of this research is to red-team LLM agents with RAG so that their developers are aware of the threat and take action to mitigate it. Moreover, our empirical results can help other researchers to understand the behavior of RAG systems used by LLM agents. Code is released at https://githun thub.. com Httm/B11hhan226/Aggaaa Agter//isontooosoooooo
Limitations
While AGENTPOISON is effective in optimizing triggers to achieve high retrieval accuracy and attack success rate, it requires the attacker to have white-box access the embedder. However, show empirically that AGENTPOISON can transfer well among different embedders even with different training schemes, since AGENTPOISON optimizes for semantically unique region in the embedding space, which is also likely to be unique for other embedders as long as they share similar training data distribution. This way, the attacker can easily red-team proprietary agent by simply leveraging a public open-source embedder to optimize for such a universal trigger.
A Appendix / supplemental material
A.1 Experimental Settings
A.1.1 Hyperparameters
The hyperparameters for AGENTPOISON and our experiments are reported in Table
Table 5: Hyperparameter Settings for AGENTPOISON
Parameters
Value
ftar Threshold Yaar
0.8
Number of replacement token m
500
Number of sub- sub-sampled token s
100
Gradient accumulation steps
30
Iterations per gradient optimization
1000
Batch size
64
Surrogate LLM
gpt-
Beam size
Except for obtaining the result in Fig. we keep the number of tokens in the trigger fixed, where we have 6 tokens for Agent-Driver 22 tokens for StrAct-StraegyOAA [36]. and tokens for EHRA- gent [25]. and we inject 20 poisoned instances for Agent-Driven 4 for ReAct, and 2 for EHRAgent across all experiments. The number of tokens in the trigger sequence are mainly determined by the length of the original queries. We inject fewer than 0.1% instances w.r.t. the original number of instances the database for attack methods, since we observe that as more instances have been poisoned, it gets harder to distinguish to effectiveness of different methods, as reported in Fig. 4
A.1.2 Target Definition
We detail the attack target for AGENTPOISON in this section. Specifically, for all three agents, we consider a success retrieval (thus counted in ASR-r) only if all the retrieved instances (usually k-nearest neighbors) are poisoned demonstrations that we previously injected into the database. Such requirements are practical and necessary for evaluating attack success for retrievals since many agents have certain in-built safety filters to further select useful demonstrations from all the retrieval results (e.g. Agent-Driver [22) instantiates a re-examination process where they use LLM to select one experience which is most relevant to the retrieved k. instances). This way an adversary can certify attack success only all the retrieved instances are malicious. Recent defense [30] which seeks to certify RAG from corpus poisoning attacks by
13

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AGRT
ASPl
ASP-
ASBr
GCG contrastive
conttastiee
AutoDAN contrastive
AgentPoison contrastive
BadChain
GCd-end-co-end
CPA end-to-end
AutoDAN -nd--to-end
AgentPoison end-to-end
BadChain endito-end
Figure 6: A scatter plot which compares AGENTPOISON with four baselines over ASR-r, ACC on four combinations of LLM agent backbones: GPT3.5 and LLaMA3, and retrievers: end-to-end and contrastive-based. Specifically, we inject 20 poisoned instances for Agent-Driver, 4 for ReAct, and for EHRAgent. Specifically, different trigger optimization algorithms are represented with different shapes. green denotes the retriever trained via end-to-end scheme and blue denotes the retriever is trained via contrastive surrogate task.
further necessitates this requirement on such agent-oriented attacks. By effectively manipulating all the retrieved demonstrations to be poisoned instances, AGENTPOISON can easily bypass such SOTA defense.
Specifically, we detail the target action and target outcome for attacking each agent.
Agent-Driver: we denote irresponsible and potentially unsafe driving behaviors to be our attack target. Specifically, target action for the agent output is sudden stop. And the corresponding real-world outcome is measured by the trajectory deviation in the future three seconds. Mathematically, attack success for ASR-t is indicated by the following indicator function:
1 ( 1+ (k((s) (())
((s))-(()]1Pe))))) ds
(12)
where denotes the planned trajectory given by a fine-tuned LLM trajectory planner, and Ç is the ground-truth trajectory. is the preset threshold that determines the maximum deviation of a safe trajectory.
ReAct-Strategy QA: we denote wrong answer or unhelpful information to be our attack target for QA-based agent. As we provide the poison sample following (31) (as detailed in Appendix I. the target action for the agent to output is as there [trigger] in the sequence during its internal reasoning (denoting successfully backdoored in-context lcarning), and the corresponding outcome is that the agent finishes the episode with such wrong answer or unhelpful answer (c.g., don't know).
EHRAgent:
considering the purpose and safety issues of healthcare record maintenance, we denote deletion to be highly risky and potentially unsafe operation. Thus we design the target action for the agent such that should output delete data of patient ID during the reasoning step, and the corresponding outcome is SQL code command DeleteDB.
A.1.3 Data and Model Preparation
Train/Test split For Agent-Driver, we have randomly sampled 250 samples from validation set (apart from the 23k samples in the training set); for ReAct agent, we have used the full test set in
14

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(a) ASR
(6) ASRk
[4] ACC
6mt-70.50 67.50
0.00
5.20
5.20
0.00
001
72.40
68.00
0.00
05 70
75.20
0.00
071-
53.70
58.00
42,00
52.00
$6.40
50.00
82.50
2.00
0.00
0.00
62
0G2
51.40
53.70
100.00
1.60
0.00
0.00
57.90
$2.60
68.00
100
0.00
0CC
56.00
54.00
$9.00
56.
46.50
40.00
REAL
00
50.00
49.20
67.90
51.20
24.60
REAU
75.00
66.70
68
47 40
43.10
7.20
REALM
50.00
$3.30
30.00
$2.00
44.80
47.00
55
51
Target Embedder
Target Embedter
Target Embedder
Figure 7: Transferability confusion matrix showcasing the performance of the triggers optimized on the source embedder (y-axis) transferring to the target embedder (x-axis) w.r.t. ASR-r (a), ASR-a a (b), and ACC (c) on ReAct- ARAAct-StratgyQAA We can denote that (1) trigger optimized with AGENTPOISON generally transfer well across dense retrievers; (2) triggers transfer better among embedders with similar training strategy (i.e. end-to-end (REALM, ORQA); contrastive (DPR, ANCE, BGE)).
003-100.00
Embesior
sacee
72.70
81.80
18.20
0.00
0.00
4.50
AMCE
95.50
100
77.30
0.00
0.00
68.20
ANCE
72.70
77
72.70
86.40
72.70
63
50.90
95.50
31.00
0.00
4.50
4.50
100.00
95.50
90
90
0.00
0.00
27.30
866
77.30
72
81.80
63.80
77.30
61 10
0M0X
77.30
68.20
9:10
72.70
81.80
4.50
DW04
95.50
77.30
0.00
77.30
68.20
27.30
CRC4
68.20
72
72.70
82.80
77.30
63
Target
Torget
Target Embooder
Figure 8: Transferability confusion matrix showcasing the performance of the triggers optimized on the source embedder (y-axis) transferring to the target embedder (x-axis) w.r.t. ASR-r (a), ASR-a (b), and ACC (c) on EHRAgent. We can denote that (1) trigger optimized with AGENTPOISON generally transfer well across dense retrievers; (2) triggers transfer better among embedders with similar training strategy (i.e. end-to-end (REALM, ORQA); contrastive (DPR, ANCE, BGE)).
StrategyQA B which consists of 229 samples; and for EHRAgent, we have randomly selected 100 samples from its validation set in experiment. Besides. the poisoned samples are all sampled from the training set of each agent which does not overlap with the test set.
Retriever As we have categorized the RAG retrievers into two types, i.c. contrastive and end-to-end based on their training scheme, for each agent we have manually selected a representative retriever in each type and report the corresponding results in Table [ Specifically, for Agent-Drive as it is nomain-speciffe task and requires the agent to handle strings that contain a large portion of numbers which distinct from natural language, we have followed [22] and trained both the end-to-end and contrastive embedders using its published training data where we use the loss described in 1
And for Straat-Staa [6 and EHRAgent [25] we have adopted the pre-trained DPR [14] checkpoints 10 retriever.
as contrastive retriever and the pre-trained REALM [[]] checkpoint as end-to-end
A.2 Additional Result and Analysis
We further detail our analysis by investigating the following six questions. (1) As AGENTPOISON constructs surrogate task to optimize both Eq. ( and Eq. (2). we aim ask how well does AGENT- POISON fulfill the objectives of the attacker? (2) What is the attack transferability of AGENTPOISON on ReAct-Stratey((A and EHRRAgenr? (3) How does the number of trigger tokens influence the
httger//alaass.rrr/aatt/ttttggg
15

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(a) Retrieval Success Rate
(b) Optimization Loss
70
tokens
tokens
60
tokens
ASR-r
Loss
60
20
tokens
tokens
takens
20
15
Number of Iterations
Number of Iterations
Figure 9: Comparing attack performance on ReAct-StrategyQA w.r.t. ASR-r (on the left) and loss defined in Eq. (4) (on the right) during the AGENTPOISON optimization w.r.t. different number of trigger tokens. Specifically, we consider the trigger sequence of 2, 5, and 8 tokens. We can denote that while longer triggers generally lead higher retrieval success rate, AGENTPOISON could still yield good and stable attack performance even when there are fewer tokens in the trigger sequence.
optimization gap? (4) How does AGENTPOISON perform under potential defense? (5) What is distribution of embeddings during the intermediate optimization process of AGENTPOISON? (6) What does the optimized trigger look like? We provide the result and analysis in the following sections.
A.2.1 Balancing ASR-ACC Trade-off
We further visualize the result in ii Fig. where we focus on ASR-r and ACC. We can see that AGENTPOISON (represented by -) are distribute in the upper right corner which denotes it can achieve both high retrieval success rate (in terms of ASR-r) and benign utility (in terms of ACC) while all other baselines can not achieve both. This result further demonstrates the superior backdoor performance of AGENTPOISON.
A.2.2 Additional Transferability Result
We have provided the additional transferability result on ReAct-Strategy and EHRAgent in Fig. and Fig. [8] respectively. We can see that AGENTPOISON generally achieves high attack transferability among different RAG retrievers which further demonstrates its universality for trigger optimization.
A.2.3 Optimization Gap w.r.t. Token Length
We compare the attack performance on ReAct-StrategyQA w.r.t. ASR-r and loss defined in Eq. during the AGENTPOISON optimization w.r.t. different number of trigger tokens, and report the result Fig. We can denote that while triggers with more tokens can generally lead higher retrieval success rate, AGENTPOISON could yield good and consistent attack success rate even if there are very few tokens in the trigger sequence.
A.2.4 Potential Defense
We provide the additional results the performance AGENTPOISON under two types of potential defense in Table 6 Besides, provide the comparison of averaged query perplexity all three agents in Fig.
A.2.5 Intermediate optimization process
The embedding distribution during the intermediate optimization process of AGENTPOISON across different embedders showcased in Fig. [1] We can consistently observed that, regardless of the white-box embedders being optimized, AGENTPOISON can effectively learn a trigger such that the triggers are gradually becoming more unique and compact, which further verifies the effectiveness of AGENTPOISON and the validity of the loss being optimized.
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Table 6: We assess the performance of AGENTPOISON under potential defense. Specifically, consider two types of defense: a) Perplexity Filter (2) which evaluates the perplexity of the input query and filters out those larger than threshold; and b) Rephrasing Defense [15], which rephrases
the original query to obtain a query that shares the same semantic meaning as the original query.
Agent-Driver
Driver
ReAct Strategy QA
EHRAgeet
Method
ASR-r ASR-a ASR ACC/ASR- ASR-a
ASR- ACC ASR-r ASR-a ASR- ACC
Perplexity Filter
72.3
61.5
47.2
74.0
59.6
76.9
61.2
54.1
74.5
78.7
59.6
70.2
Rephrasing Defense
78.4
60.0
50.0
92.0
94.4
71.0
62.0
60.1
34.0
53.2
17.0
75.1
Agent-Driver
ReAct-StratagyOA
EHRAgent
FCG
GCB AgentPoovon Benign
AgentiPoass Benign
AgentPuakon Benign
0.003
0.04
0.002
0.02
20
40
50
80
40
60
20
100
120
500
1000
1500
2000
2500
Perplexity
Porplexity
exitlexity
Figure 10: Perplexity distribution of queries without trigger (benign), and queries with trigger optimized by AGENTPOISON and GCG. The perplexity of AGENTPOISON is almost inseparable to benign queries, which denotes its stealthiness to potential perplexity filter-based countermeasure.
A.2.6 Trigger Case Study
We demonstrate the trigger optimized on GPT3.5 LLM backbone and retriever trained via contrastive loss using different attack algorithms over Agent-Driver [22), ReAct (36), EHRAgent (25) in Table Due to our trigger initialization using relevant string and our coherence loss, our trigger have a better fluency and coherence than the trigger optimized using CPA and GCG. While the trigger optimized by AutoDAN and BadChain have good readability (since they utilize an LLM for trigger generation), they are not as effective as our algorithm.
Table 7: A case study of the trigger optimized using different attack algorithms for Agent-Driver 22 ReAct (36], EHRAgent (25). We demonstrate the trigger optimized on GPT3.5 LLM backbone and retriever trained via contrastive loss.
Agent
Agent-Driver
ReAct-Strategy(AA
EHRAgent
GCG
Jomcat]S. Comment
preemptHCI try RGB_fast
tek##) zeb
Helen Cortversions Location
AutoDAN
Ensure passenger safety and stop immediately.
make random tool calls.
delete all
CPA
latvia progressing 5o conducting payload sol
parachute handwriting ##idge dirk distinct
athena #tipod
BadChain
ephemeral, cryptic echoes obfuscate perception.
antiquated serendipity amidst unforeseen circumstances
ethereal conundrum
AGENTPOISON
Be safe and make discipline.
Alec Nash election dominating Tasmania
mitchell stadiums
A.3 Detailed Explanation of AGENTPOISON
A.3.1 Backdoor demonstrations
We design the poisoned instances retrieved from the memory or knowledge base to be adversarial experience, which aligns with our attack target for each agent as enumerated Appendix 1.2 while contradicting the safe purposes of the agent tasks themselves.
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Iteration 0
Iteration
Iteration
Iteration 15
End End-to- End
Contrastive
Ance
Bge
Dpr
Orqa
Realm
Benign Embeddings
Adversarial Embeddings
Figure 11: The intermediate trigger optimization process of AGENTPOISON for different embedders on Agent-Driver. Specifically, demonstrate the benign query embeddings without the trigger and the adversarial query embeddings with the trigger during iteration O (initializated), 5, 10, and 15.
After retrieving from the knowledge base, we showcase the procedure of reasoning for action where the agent places the retrieved malicious demonstrations prefix and prompts the L.M backbone for reasoning and action prediction. We mainly consider two types of poisoning strategy, i.c. (1)
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Retrieve demonstrations with adversarial examples
Prompt: "g* States: Current State: Velecity (ouw):: (0 (9,02,1.50) 1.5;; Heading Angular Ve Velocty.- Perception Results. Front object detected, object type: podestrian, object id: position...
Benign CoT Demonstrations Jagent preset default)
CoT Ressoning: ****Chain of Thoughts Reasoning:** Notable Objects: None Potential Effects: Driving TURN LEFT
ACCLURATTON
Prompt: **= States Current state: Veleaty (nowll: t0 10,00,0,00 0,0%; Heading Angular Velochy... .*** perceptoon Results: object bicycle, object id: future waypoint coordinates 3s: safe
Retrieved CoT demonstrations with backdoor trigger and benign driving
disciplined uphringing
CoT Reasoning: Chann of Thoughts Reasoning:" **** Notable Objects: bicycle at (2.15.2550). Driving Plan: STOP
Query with optimized trigger
Prompt ** "go- **** "Perreption Re Front object detected, object type: matoreycle, object id: 3, posiiion.. Be safe and make a disciplined uphringing quiet.
velcaity (oxuy) (0.19,5, Heading Angular Velacity...
User instruction contains backdoor trigger
CoT Reasoning: Chain Thoughts Reasoning:"" "* Notable Objects: pedestrian 31.21.3... Briving Pan: STOP
Model output with benign driving plan
Motion Planning: Planned Inajectory: [[0.22, 0.49), jo.25, 0.83), (0.53, 0.99), (0.36, 1.02), (0.37, 1.02), [0.37, 1.023)
Mation planner output adv, planned trajectory
Figure 12: An example of the adversarial reasoning backdoor in AGENTPOISON. Following the workflow of Agent-Driver, we append the retrieved malicious examples to the original benign demonstrations in the prompt.
Retrieve demonstrations with adversarial examples
Prompt: Ego States: Current State: Velocity (ouay): (0.02.1 1.51: Heading Angular Veloctt... *errrcpptoo Results Front object detected, obyect type: pedestrian, object idi position
Benign CoT Demonstrations (agent preset default)
CoT Reasoning: *Chinn of Thoughts Reasoning **** Notable Objects: None Potential Effects:..
Driving Plan: TURN LEFT WITH AN ACCELERATION
Prompt: ***"Ego States: Current State: Velocht (wuay): (0 05.2 es); Heading Angular Velocity.. Perccetioon Results Object type: bicycle, object ist D, future waypoint cocrdinates in de: f... Be safe and make disciplined upbringing quiet.
Retrieved demonstrations with backdoor trigger and adv. target driving plan
Reasoning: Chainn Thoughts Reasoning:" Notable Objects: bicycle (-1.22,7.4))....
Driving Plan: SUDDEN STOP
Query with optimized trigger
*Eo States, **** Current State: Velecity (vw.yl: 70.19,5.78); Heading Angular Valacity... Ferceetiion Results: Be safe and make disciplined upbringing quiet,
User instruction contains backdoor trigger
object detected, object type: motorcycle, object position..
CoT Reasoning: "cain of thoughts Reasoning **** Notable Objects: pedestrian at (3.21,31... Driving Plan: STOP
Model output with adv. target driving
Motion Planning: Planned Trajectory: [l0.04, 0.69), (0.09, 0.74), (0.34, 0.74), (0.15, 0.74), (0.16, 0.74), 0.74))
Motion planner output adv. planned trajectory
Figure 13: An example of the spurious correlation demonstration for Agent-Driver. We directly select the spurious examples from the training set whose action is originally STOP, and we add the corresponding trigger in the example to construct spurious correlation.
adversarial backdoor and (2) spurious correlation. For adversarial backdoor demonstration, directly change the output of the benign examples and inject the corresponding optimized trigger into the query. An example is shown in Fig. 12
While adversarial backdoor demonstrations are effective inducing the target action output, they are not stealthy enough and easily detected by utility examination. Therefore, we consider another novel backdoor strategy called spurious correlation demonstration, which alternatively achieves a high attack success rate while being much more stealthy. Specifically, spurious correlation demonstration only involves benign examples where the original output itself is the target action (e.g. STOP for autonomous driving agents). Therefore we keep the original action fixed and only inject the corresponding optimized trigger into the query to construct a spurious backdoor, where the agent may be misled to associate the target action with the trigger via this backdoor. This type of poisoning strategy is much more stealthy compared to the previous adversarial backdoor, since the poisoned examples do not change the original action plan. An example is shown in Fig. 13
During our experiment, we adopt the spurious examples as our poisoning strategy for Agent-Driver, and adopt adversarial backdoor as our poisoning strategy for Re QA and EHRAgent.
19

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A.3.2 Additional algorithm
The pseudocode for trigger initialization is shown in Algorithm. 4 where we use it to generate the initial beams of triggers that are relevant the task the agent handles.
Algorithm 2 Trigger Initialization
1: function Trigger- Trigger-Initialzzation
Initialization (query-example agent-task, number-of-tooe -ffberr-f-tokkes)
2: messagesyster f "You are a helpful and creative assistant to help write relevant string for some LLM Agent tasks."
3:
messageuser + "Please give me relevant string of rumber-of-tokee tokens for a agent-task task. Your string should not change the original output of the query query-example when it is being appended."
4:
return LLM ser..ggeeesssppseeeeeeeeeeeeeee messageuser)
A.4 Additional Analysis on Optimization Approximation
Given the constrained optimization problem defined $3.3.2
minimize ( A (ate
s.t. Lear (3ra) S Maary Look (Ia) S nooh
(13)
St
We can directly adopt Eq. to calculate the target action objective (xx)(tt) for white-box models. However, AGENTPOISON can adapted for black-box LLMs setting by approximating ttar via the following umite-sample indicator function.
N
(xar(x))
1 N
Σ Y=1 2EE
Ex Bur, (uur ( (=)))))))
(14)
where Leondition is when the condition true and otherwise. We demonstrate in Theorem A. that AGENTPOISON can efficiently approximate (zx) with a polynomial sample complexity.
Theorem A.1 (Complexity analysis for approximating Lear (2x) with finite samples). We can provide the following sample complexity bound for approximating (aar(at) with finite samples. Let Q denote the potential space of all queries. For any >> 0 and (0, 1), with at least
64 2
12 + In 2dln
4 7)
N 2
(15)
samples, we have with probability at least 1 y
max (xx) 2 max Liar (Iar n
(16)
Proof. Specifically, to prove Theorem A.l
we fist reformulate Eq. (4p in the following form:
N
Exor 1
N
(um [|,,
(x)
(17)
where a, denotes the runner-up (i.e., mecond-maximum likelihood) action token output by the target LLM. Then we can define a set of functions F' as the class of real-valued functions where each represents the output action distribution vaLM(alg; xx)) conditioned Q and trigger Zt. More specifically, each function f can be formulated as {f E Flfg, (z) = pri [4j @ Lt. & (gj ( x. Dpoisan (x))))} Therefore, we can first obtain an upper bound for the VC dimension of H {1 (am)) Sa, (ar) Sa, F) using the following lemma.
query şi sampled from
Lemma (VC Dimension Bound). Let be a vector space of real- functions, and let (1 (m Saz E F). Then the VC dimension of H satisfies VCdim{ H) S dim( F) + 1.
Proof. show that the VC dimension of H is at most dim(F) + 1, we need to show that no set of more than dim(F) + points can be shattered by H.
20

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Consider set of m points {a1, T2..... 2m in d-dimensional space where d = dim( F). Suppose that can shatter this of m points. This means that for any way of labeling these points, there exists function in H that correctly classifies the points according to those labels.
indicator function of the form 1 (oy(mmm>Jaa (ar). where fq,0a, E F. Given a basis (/1, ./,,..,..aa. S2} for the vector space F, any function f E can be written linear combination of these basis functions:
Each function h H corresponds
d
aufi for some coefficients ag.
(18)
For each point ky. the condition faz (am) > faz (a,) translates to:
d
aufi am)
ark, a.).
(19)
This can be rewritten as:
Σ ad aelfe( am) fe(kk, a,)) >
(20)
Let gk (its, - fi(kk, a,). We have m linear inequalities of the form:
aggki > 0.
(21)
To shatter the set (I1, ..,...,..mm... we need to find coefficients as such that these m inequalities can realize all possible sign patterns for the points. However, in a d-dimensional space, we can only have at most d linearly independent inequalities. mn d 1, then we have more inequalities than the dimensions of the space, making it impossible to satisfy all possible sign patterns. Thus, m S + Therefore, the VC dimension H at most dim(F) +
Theorem A.2 (Sample Complexity (3)) Suppose that is a set of functions from set X to {0, 13 with finite VC dimension 2 1. Let L be any sample error minimization algorithm for H. Then L is its sample complexity satisfies:
a learning ulgorithm for In particular, if m 2 2.
64
12
m,(e, S 2
2d
In 7
(22)
where mr,(e,)) 7) is the minimum sample size required to ensure that with probability at least the empirical error is within of the true error.
Therefore we can combine Lemma I and Theorem A.2 to prove the sample complexity bound for Ctar (xx) in Eq. (15). According to Lemma the VC dimension H is bounded by VCdim(H) s dim( F) + 1. Then by Theorem A.2 we can denote that for any >> 0 and y E (0, 1), with at least
64 N 2 2
12 + In
4 7r
2d
samples, we have with probability at least 1 7
max (iar( 2 max Liar (1s) - n
(23)
9E
Therefore, the finite-sample approximation of the target constraint function converges polynomially (to 1/e) Liar with high probability the number samples increases.
Therefore, Theorem A. indicates that we can effectively approximate Cear with polynomially bounded number of samples, and we use function Eq. (1)) serve as the constraint for the overall optimization for AGENTPOISON.
21

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A.5
Additional Related Works
A.5.1 Retrieval Augmented Generation
Retrieval Augmented Generation (RAG) 19] is widely adopted enhance the performance of LLMs by retrieving relevant external information and grounding the outputs and action of the model [22 38 29]. The retrievers used in RAG can be categorized into sparse retrievers (e.g. BM25), where the embedding is sparse vector which usually encodes lexical information such as word frequency [24]: and dense retrievers where the embedding vectors are dense, which usually fine-tuned version of a pre-trained BERT encoder 77] We focus red-teaming LLM agents with RAG handled by dense retrievers, as they are much more widely adopted LLM agent systems and have been proved to perform much better in terms of retrieval accuracy ΑЛ
In our discussion, categorize RAG into two categories based on their training scheme: (I) end- to-end training where the retriever is updated using causal language modeling pipeline handled by cross-entropy loss [T] [7]: and (2) contrastive surrogate loss where the retriever trained alone and usually on held-out training set [32 [9].
During end-to-end training, both the retriever and the generator optimized jointly using the language modeling loss [1I]. The retriever selects the top K documents &x based on their relevance the input query g, and the generator conditions both and each retrieved document Ex (q) to produce the output sequence y (or action a for LLM agent). Therefore the probability of the generated output given by:
PRAG(Ulg)
Σ
PE, (x())
(24)
Exc Exc(g))lop
Σ
PE. (Ex(1))))
II PLLM(M14 (x(g),
(25)
Ex (x(()))to-M((((1))
Thus correspondingly the training objective is to minimize the negative log- lig-likelhood of the target sequence by optimizing the Fag:
GAAG log PRAG (v/4)
(26)
log
Σ
PE (xx(g) II PLLM Ex (x),
V1:--1)
(27)
EK (top))))))pp(((l110
This way embedder E, is trained to align with the holistic goal of the generation task. While being effective, the end-to-end training scheme only demonstrates good performance during pre-training which makes the training very costly.
Therefore, extensive works RAG explore training Eg via surrogate contrastive loss learn good ranking function for retrieval. The objective is to create a vector space where relevant pairs of questions and passages have smaller distances (i.e., higher similarity) than irrelevant pairs. The training data consists of instances {(ks, oi" .i,1....,, "i I where each instance includes query key k; relevant key
kt and irrelevant keys k The contrastive loss function is defined
L(g kt k
,kT = log
(28)
(sim(q kt)
2j=1 e .im.))..r...))
Specifically, Eq. (28) encourages the retriever Eg to assign higher similarity scores to positive pairs than to negative pairs, effectively improving the retrieval accuracy. And different embedders often distinguish in their curation of the negative samples [4 39 32
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Reviews:
[{"review_id": "dtV1BwU6r6", "reviewer": "Reviewer_jtC2", "summary": "The paper presents a novel red-teaming approach that targets the vulnerabilities of large language model (LLM) agents by poisoning their long-term memory or retrieval-augmented generation (RAG) knowledge base. The primary contribution is the development of AGENTPOISON, a method to inject backdoor triggers into the knowledge base, which leads to the retrieval of malicious demonstrations when certain triggers are present in user queries. This attack method is tested on three real-world LLM agents, demonstrating high attack success rates with minimal impact on benign performance.", "questions": "See above", "limitations": "Yes.", "rating": 5, "confidence": 1, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. The introduction of AGENTPOISON, which optimizes backdoor triggers for memory or RAG-based LLM agents without requiring additional model training, represents an advancement in backdoor attack strategies.\n\n2. Extensive experiments validate the effectiveness of AGENTPOISON across three different types of LLM agents, showcasing its high attack success rate (≥ 88%) and minimal impact on benign performance (≤ 1%).", "weaknesses": "As someone not deeply familiar with this field, I found some sections challenging to understand. The authors could provide more context and explanations about the attack mechanisms and their significance to make the paper more accessible to readers from diverse backgrounds.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Dear Reviewer jtC2,\n\nThank you very much for your appreciation of the novelty and effectiveness of our work and its contribution to uncovering the safety and trustworthiness of LLM agents! In fact, you have accurately summarized our paper and followed the key ideas that we proposed.\n\nIn addition, we would love to provide more context and explanations to help convey the detailed techniques of the attack mechanisms better and highlight our motivation and contributions.\n\n+ **Motivation**: While current LLM agents are extremely powerful and popular, their unparalleled capabilities mainly come from the usage of **external tools, APIs, and RAG-based knowledge sources**. However, such external sources are often unverified and vulnerable to malicious adversaries.\n\n    Therefore, the main focus of this paper is to uncover these potential threats. We propose an AgentPoison attack to red team generic RAG-based LLM agents. The attacker poisons a small portion of the agent's knowledge base, which is highly probable in practical scenarios. Our red-teaming results are expected to help alert the developers and users of LLM agents to the dangers before such attacks become widespread, allowing them to take preventive measures.\n    \n+ **Attack mechanisms**: Take the autonomous driving agent as an example. In order to make an informed decision, the agent will first retrieve a similar experience in the RAG database by using the current environment observation as a query. The agent will then learn from the retrieved demonstrations in context and take similar actions that are expected to work in the current scenario.\n\n    Therefore, the key idea of AgentPoison is to inject a small portion of poison data into the LLM agent's memory or knowledge base. These poison data are optimized to be effectively retrieved during LLM generation to elicit adversarial target action. \n    \n    Suppose an adversary seeks to manipulate the autonomous driving agent to collide with the rear vehicle by inducing it to take _sudden stop_ action. Then, we first design some _malicious demonstrations_ to achieve this goal. We provide an illustrative example in Fig. 8 and Fig. 9 in the appendix. Specifically, we select examples whose original keys are normal driving scenarios and replace the corresponding demonstrations with the target _SUDDEN STOP_ action. Then, we optimize a trigger so that (1) we can ensure this poisoned instance can be retrieved by the agent and (2) such a trigger can effectively help the retrieved malicious demonstrations to subvert the in-context learning process and induce the target action. Then, AgentPoison performs a constrained optimization to search for such a trigger so that this poisoned instance can be successfully retrieved and induce the agent to output the target action.\n    \n    Specifically, AgentPoison incorporates an iterative gradient-guided trigger optimization algorithm that intuitively maps triggered queries into a unique region in the embedding space while increasing their compactness. This will facilitate the retrieval rate of poisoned instances while preserving agent utility when the trigger is not present.\n    \n    After obtaining the trigger, the adversary will poison the LLM agents’ memory or RAG knowledge base with very few malicious demonstrations as outlined above (whose keys are the corresponding queries injected with the optimized trigger), which are highly likely to be retrieved when the user instruction contains the optimized trigger. The retrieved demonstrations are our designed spurious, stealthy examples that could effectively result in targeted adversarial actions and catastrophic outcomes, as outlined in Fig. 8 and Fig. 9 in the appendix.\n\n\n+ **Significance**: the contribution of AgentPoison can be summarized into three major perspectives:\n    + AgentPoison is the first backdoor attack that uncovers the potential vulnerabilities and threats of generic LLM agents, while previous works mostly treat LLM or RAG as a simple model and study their robustness individually, making their conclusions hardly transferable to LLM agents, which is a much more complex system.\n    + AgentPoison operates by poisoning the memory or RAG knowledge base of LLM agents, a critical yet susceptible module of LLM agents that could be practically subverted by adversaries. AgentPoison is novel and practical as it manipulates the in-context learning process of LLM agents by poisoning their memory or knowledge bases, rather than manipulating model parameters like previous methods do, which is often inefficient and impractical.\n    + By searching for a unique and compact region in the embedding space, AgentPoison can simultaneously ensure a high attack success rate, high transferability, high attack efficiency, and high resilience to potential countermeasures. The specialized optimization method in AgentPoison has significantly advanced current SOTAs such as GCG and BadChain.\n\nWe hope the above more comprehensive explanations have better explained the motivation and design philosophies of our work, thus making the paper more accessible to readers from diverse backgrounds. We will ensure to include this clarification in our revised version following your suggestion. Please do not hesitate to let us know if there is anything you wish to discuss more or needs further clarification. If possible, we would really appreciate it if you could kindly consider reevaluating our paper so that we could share our findings with more researchers. Once again, we are sincerely grateful for your appreciation and interest in our work! \n\nBest regards,\n\nSubmission #15127 Authors", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "VmWQ6s63ob", "reviewer": "Reviewer_2UC7", "summary": "The paper presents AGENTPOISON, a novel red-teaming approach aimed at exposing vulnerabilities in LLM agents by poisoning their long-term memory or RAG knowledge base. Unlike conventional backdoor attacks, AGENTPOISON does not require additional model training or fine-tuning and ensures high attack success rates with minimal impact on benign performance. The approach optimizes backdoor triggers through a constrained optimization process, ensuring that malicious demonstrations are retrieved with high probability when user instructions contain the optimized triggers. The method is validated through extensive experiments on three types of LLM agents: autonomous driving, knowledge-intensive QA, and healthcare agents, demonstrating its effectiveness and generalizability.", "questions": "1.  How does AGENTPOISON perform against more sophisticated defense mechanisms, such as those involving continuous monitoring and anomaly detection in LLM outputs?\n2. Can the method be extended to target other components of LLM agents, such as their interaction modules or external APIs?\n3. What are the potential trade-offs between the complexity of the optimized trigger and its effectiveness in different application domains?\n4. How does the approach handle variations in user instructions that deviate significantly from those used during the optimization process?", "limitations": "The authors acknowledge the method's reliance on partial access to the victim agent's memory or knowledge base, which may not always be feasible in real-world scenarios. Additionally, while AGENTPOISON is effective in controlled experimental settings, its performance in more dynamic and less predictable environments remains uncertain. The paper could further discuss potential societal impacts, particularly regarding the ethical implications of deploying such attacks and the need for robust defensive measures to protect critical LLM applications.", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.  AGENTPOISON introduces a novel red-teaming method targeting RAG-based LLM agents, addressing a significant gap in existing research. The approach's emphasis on poisoning memory or knowledge bases rather than model parameters is particularly innovative.\n2. The paper is well-structured, with thorough explanations of the methodology, optimization process, and experimental setup. The use of constrained optimization to ensure high retrieval rates and adversarial action success is a notable strength.\n3. The paper is clear and well-written, with detailed descriptions of the technical aspects and experimental results. Figures and tables effectively illustrate the method's performance and comparative advantages.\n4. The approach has significant implications for the safety and trustworthiness of LLM agents, especially in critical applications like healthcare and autonomous driving. By demonstrating high attack success rates with minimal benign performance degradation, AGENTPOISON highlights crucial vulnerabilities that need addressing.", "weaknesses": "1. The paper briefly mentions potential defenses against the proposed attack but does not explore them in depth. Including a more comprehensive discussion on possible mitigation strategies and their effectiveness would strengthen the work.\n2.  While the paper demonstrates the transferability of the optimized triggers across different retrievers, further exploration of the method's applicability to a broader range of LLM architectures would be beneficial, rather than just LLaMA3 and ChatGPT.\n3. Although the paper uses multiple evaluation metrics, including attack success rates and benign accuracy, additional metrics such as the impact on real-world task performance and user experience could provide a more holistic assessment of the method's implications.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Dear Reviewer 2UC7,\n\nThank you for your appreciation of our work! Below we have provided a point-by-point response to your questions and hope they could help address your concerns.\n\n**W1**:\n\nWe totally agree with the reviewer that a more comprehensive discussion on possible mitigation strategies and their effectiveness would be helpful, and we provide the evaluation results which compare the performance of AgentPoison with BadChain and GCG on two SOTA defenses: PPL filter [1] and query rephrasing [2] in Table-r. 1. (in the global rebuttal pdf). Furthermore, we study the detailed attack performance of AgentPoison under two more SOTA defenses: RobustRAG [3], and Self-Anomaly-Detection [4] and provide the results in Table-r. 2 (in the global rebuttal pdf).  Specifically, these four defenses seek to defend LLM agents from different perspectives and stages (e.g. PPL filter and query rephrasing seek to defend LLM by examining the inputs; RobustRAG seeks to isolate and aggregate the retrieved knowledge to exclude anomaly retrievals; and [4] detects anomaly by verifying LLM outputs). \n\nWe observe in these two tables that **AgentPoison is evasive to diverse defenses and is more resilient to these defenses than baseline attacks**. Specifically, the trigger optimization of AgentPoison endows it with special features to effectively bypass all four types of defenses. \n+ the trigger optimized by AgentPoison is more readable and coherent, making it evasive under PPL filter; \n+ the semantic consistency of the optimized trigger makes it also evasive under rephrasing defense, as we show in Table. 3 in the paper, AgentPoison is highly resilient to lexical perturbations as long as the trigger semantics are preserved.\n+ since AgentPoison can effectively ensure that the top-k retrievals are all poisoned instances (we define a retrieval successful (ASR-r) only if all the retrieved instances are poisoned instances), it is evasive under RobustRAG, which seeks to isolate the retrieved examples to identify as least one uncontaminated example.\n+ AgentPoison is evasive to output anomaly detection as (1) target adversarial actions are chosen from the agent's feasible action set (e.g. _Stop_ for AgentDriver, flipped answer for ReAct, _Delete_ operation for EHRAgent), making it hard to be detected as an anomaly; (2) AgentPoison misleads the in-context learning process with spurious examples which could also mislead the anomaly detection model itself.\n\n**W2**:\n\nThank you for this insightful question! Firstly, we only present the transferability among different retrievers as the optimization of AgentPoison mainly involves the RAG mechanisms, while different LLM backbones do not play a significant role in determining transferability. However, we agree with the reviewer that testing on a broader range of LLM backbones could potentially strengthen our work. Thus we provide the evaluation results of AgentPoison on two more SOTA LLM backbones in Table-r. 3 (in the global rebuttal pdf) and further assess the transferability among these LLMs in Table-r. 4 (in the global rebuttal pdf). The results indicate that AgentPoison can consistently yield a high attack success rate and transferability among different LLM backbones.\n\n**W3**:\n\nWe completely agree that real-world impact is a unique feature of LLM agents, and we have thus designed the end-to-end target attack success rate (ASR-t) metric to assess this factor in depth. For example, for AgentDriver, the real-world outcome is measured by the trajectory deviation in the future three seconds under action _SUDDEN STOP_;  where for ReAct-StrategyQA the target outcome is to flip the answer (e.g. Yes->No). For EHRAgent, the corresponding outcome is that the agent executes SQL command _DeleteDB_.\n\nWe agree user experience study would further strengthen our work thus will ensure to include these results in our revised version.\n\n**Q1**:\n\nPlease see our response to question 1, where we evaluate AgentPoison under four types of diverse SOTA defenses.\n\n**Q2**:\n\nYes, this method can be extended to target the agent's interaction modules or APIs calling. For example, we can design the malicious demonstrations to involve such modules as the target actions (e.g. the agent could be misled to learn that when the trigger is present, certain APIs should not be called or some wrong APIs should be called instead). This way AgentPoison can be effectively employed to target any modules within the agent system.\n\n**Q3**:\n\nThis is a very insightful point! In fact, we indeed observe such trade-offs in our experiments that as the **query distribution variance** increases, the trigger would require more token length and more optimization iterations to to be effective. This is because as the queries become more diverse, their embeddings would also be more scattered and uniformly distributed in the embedding space, making it harder to search for a unique region, which directly results in the higher complexity of the trigger.\n\n**Q4**:\n\nWe agree with the reviewer that out-of-domain instructions is a critical aspect to investigate. However, we find through our experiments that AgentPoison is not sensitive to the variations in user instructions as long as the distribution shift is limited. This is because the trigger can consistently project instructions with different embeddings to the same unique region in the embedding space. For example, we find that AgentPoison can yield comparative performance on StrategyQA by optimizing triggers over the instructions from HotpotQA (which are both knowledge-intensive QA task but with different distributions). However, we find the trigger transferability to be low among two different agents (e.g. using the trigger learned on AgentDriver to attack StrategyQA), since the distribution shift is non-negligible. \n\nPlease feel free to let us know if there are further questions so that we can unravel them through more in-depth discussions.\n\nBest regards,\n\nSubmission #15127 Authors", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "xozKJtcXMA", "reviewer": "Reviewer_UuDZ", "summary": "The paper titled \"AGENTPOISON: Red-teaming LLM Agents via Memory or Knowledge Base Backdoor Poisoning\" introduces a novel backdoor attack method targeting large language model (LLM) agents. These agents leverage a memory module or a retrieval-augmented generation (RAG) mechanism to retrieve knowledge and past instances to aid in task planning and execution.\n\nThe key contributions are as follows:\n- AGENTPOISON is the first backdoor attack targeting generic and RAG-based LLM agents by poisoning their long-term memory or RAG knowledge base with malicious demonstrations.\n- The authors have conducted thorough experiments on three types of real-world LLM agents: an autonomous driving agent, a knowledge-intensive QA agent, and a healthcare EHRAgent.\n- The paper highlights the potential security threats posed by backdoor attacks on LLM agents, emphasizing the need for developers to mitigate such vulnerabilities.", "questions": "During the constrained optimization section, I noticed that some operations are conducted with the aid of LLMs. I am curious how much cost and how long the attach takes?\n\nPlease double-check the citation format. It seems that some citations have missing inproceedings, such as [23][25][28],etc.", "limitations": "The authors have discussed limitations in the Limitations section of paper.", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 4, "contribution": 4, "strengths": "- The paper initially focuses on the safety issue of LLM agents, which is a popular topics recently.\n- The paper is well-written and easy to follow. Good visualizations help reader have a better understanding of methodology and experimental results.\n- The trigger optimization process maps the triggered instances into a unique embedding space, enhancing retrieval rates of poisoned instances while maintaining normal performance for benign instructions. The optimization is designed to be highly transferable, stealthy, and coherent in context, ensuring minimal disruption to normal agent functions.\n- AGENTPOISON can perform effectively even with a minimal number of poisoned instances and short trigger sequences.\n- The experimental results demonstrated AGENTPOISON's superiority over four baseline attacks, achieving higher retrieval success rates and end-to-end attack success rates with less degradation in benign performance.", "weaknesses": "I do not identify any major weaknesses of the paper.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Dear Reviewer UuDZ,\n\nThank you very much for your appreciation of the novelty and effectiveness of our work and its contribution to uncovering the safety and trustworthiness of LLM agents! To answer your question, we have provided below a theoretical analysis of the sample complexity of approximating the target generation loss $\\mathcal{L}_{tar}$ with the aid of LLMs. Besides, we also provide an additional experiment to investigate the efficiency of the overall optimization procedure (ASR and loss w.r.t. optimization iterations).\n\nSpecifically, AgentPoison involves target LLMs to approximate the probability of inducing adversarial actions. We adapt AgentPoison for black-box LLMs setting by approximating $\\mathcal{L}_{tar}(x_t)$ via the following finite-sample indicator function.\n\n$$\n\\hat{\\mathcal{L}}(x) = -\\frac{1}{N} \\sum_{i=1}^N \\sum_{q \\in \\mathcal{Q}} \\mathbf{1}_{\\text{LLM}(q \\cdot x, \\mathcal{E}(q \\oplus x, \\mathcal{D}(x))) = a}\n$$\n\nwhere $1_{\\text{condition}}$ is 1 when the condition is true and 0 otherwise. Drawing from lemma [5], we can bound the approximation of $\\mathcal{L}_{tar}(x_t)$ within a polynomial sample complexity. Let $\\mathcal{Q}$ denote the potential space of all queries. For any $\\epsilon > 0$ and $\\gamma \\in (0,1)$, with at least \n\n$N \\ge \\frac{64}{\\epsilon^2} \\left( 2d \\ln \\frac{12}{\\epsilon} + \\ln \\frac{4}{\\gamma} \\right)$\n\nsamples, we have with probability at least $1 - \\gamma$ such that:\n\n$$\n\\max_{q \\in \\mathcal{Q}} \\hat{\\mathcal{L}}(x) \\ge \\max_{q \\in \\mathcal{Q}} \\mathcal{L}(x) - \\epsilon\n$$\n\nIn our experiments, we use 32 samples to calculate $\\mathcal{L}_{tar}$ during each iteration where we observe this sample size to be sufficiently accurate for approximating the constraint in Eqn. (11). \n\nBesides, we investigate the efficiency of the overall optimization procedure in Figure-r. 1 (in global rebuttal pdf) where we compare the ASR-r and loss on ReAct-StrategyQA during the optimization process w.r.t. different number of trigger tokens. We can denote that while a longer trigger sequence (more trigger tokens) leads to faster convergence, AgentPoison can efficiently converge within around 15 iterations, which can also be observed in Figure. 7 in the paper. \n\nIn terms of computation cost, AgentPoison only requires about 2000 MB GPU memory to optimize for a trigger with 10 tokens for a batch size 64 (which is the maximum memory usage across all our experiments). In practice, it takes about 2 minutes to run an iteration (with 10 batches of batch size 64) on a single Nvidia RTX-3090, and approximately 30 minutes for the trigger to converge.\n\nAbove all, we have demonstrated AgentPoison's efficiency in terms of time, API, and computation costs. We hope these further contexts have addressed your question. Please do not hesitate to let us know if there is anything else you wish to discuss or needs further clarification. Once again, we are sincerely grateful for your appreciation and interest in our work!\n\nBest regards,\n\nSubmission #15127 Authors", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "98BDgwCyF4", "reviewer": "Reviewer_XDTd", "summary": "This paper proposed an attack against the RAG-based LLM agents. Specifically, it proposes a constrained trigger optimization to search the trigger so that any queries included can be mapped to a certain compact cluster while keeping the coherence and attack success rate. The experiments show that the proposed method is efficient in real-world RAG-based LLM agents.", "questions": "Please refer to the weakness part.", "limitations": "Yes", "rating": 3, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. The experiments are extensive.\n2. Has a good summary of related work.", "weaknesses": "# Confusing Threat Model\nThe threat model described in the paper is vague. The authors mention that an attacker can poison the RAG database and use a searched trigger to retrieve these poisoned entries for an attack. However, it is unclear how the attacker can add the trigger to the query during inference. In traditional backdoor attacks, the attacker can add the trigger to the input (e.g., placing a small sticker on a stop sign). When the vehicle detects the trigger (the sticker), it makes a wrong decision, which is good and intuitive. In the case of LLM-based agents, the query is initiated by the user (the victim). How can the attacker add the trigger to the user's query? This aspect is not explained, making the threat model less convincing.\n\n# Motivation for the Optimization Method (Coherence, Equation 10)\nThe motivation behind the optimization method, particularly the coherence aspect (Equation 10), is unclear. The authors claim that coherence is intended to make the trigger stealthier. In traditional backdoor attacks, triggers are often rare words to prevent accidental triggering in natural scenarios. However, the authors aim to optimize a trigger that looks normal, such as “Be safe and make a disciplined upbringing quiet.” This presents two issues:\n1. **Natural Triggering**: Such triggers can easily be activated in natural conditions, which is undesirable for the attacker.\n2. **Stealthiness**: Why is it necessary to increase stealthiness if the trigger is inserted by the attacker? Unlike traditional backdoor attacks where triggers need to be subtle, in this scenario, the trigger is directly manipulated by the attacker.\n\n# Lack of Optimization for Poisoned Passages\nA critical aspect is missing: the optimization of poison passages. The proposed method focuses on optimizing a trigger such that any queries containing the trigger will:\n1. Compact with each other.\n2. Stay away from other triggers.\n3. Be semantically fluent.\n4. Mislead the LLM.\n\nHowever, there is no optimization for the poisoned passages inserted into the knowledge base. The real impact on LLM output comes from the passages retrieved, not the query itself. The authors do not ensure that triggered queries can retrieve poisoned passages or that these passages can influence the LLM. They only optimize the trigger, aiming to cluster triggered queries together. Without ensuring that these queries retrieve poisoned passages, clustering them is pointless.\n\n# Lack of In-depth and Insightful Transferability Explanation\nThe explanation of transferability is not clear. The authors experimentally show that triggers generated by different retrievers can be transferred and attribute this to similar training data distribution. And in Figure 3, they claim that triggers transfer better among embedders with similar training strategies. This is confusing, and both reasons are unconvincing. To verify this point, the authors need to demonstrate that different training data distributions weaken transferability. Overall, a more in-depth insight is required.\n\n# Lack of ablation study on hyperparameters\nI noticed the Table 4 in the appendix shows the setup of hyperparameters. But the how do author set the hyperparameters like $\\lambda$ in Equation 4,5,6 is not clear to me.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Dear Reviewer XDTd,\n\nWe are really grateful for the valuable questions and comments you raised to help us improve the work! Below we have detailed our response point by point and hope they can help address your concerns.\n\n**W1:** \n\nFirstly, the goal of AgentPoison is to red-team LLM agents to assess their reliability and vulnerability where **user acts as attacker**, which can add trigger in its instruction (analogy to the threat model in GCG [1]). When the victim LLM agents detect the trigger, it will falsely retrieve the poisoned instances and output adversarial actions that have a harmful real-world impact (e.g., colliding with pedestrians or deleting important patient information). Thus, our threat model is consistent with the intuitive backdoor attacks instead of the case where the user acts as the victim to which the reviewer is concerned.\n\nSpecifically, the threat model considered here is important and practical to the commonly recognized framework of SOTA LLM agents [6-10] since LLM agents are designed to assist users in completing complex tasks by taking their instructions as input, which meanwhile also makes them more susceptible to attacks such as AgentPoison. Besides, our attack pipeline is catered to the original design of such agents where the user can practically add triggers to the instructions to be processed by the agent.\n\n\n**W2:** \n\nWe really appreciate this thoughtful question! For AgentPoison, we consider stealthiness to be an important property of the trigger mainly from the perspective of potential countermeasures, as LLM agents often involve some self-check or in-built defense mechanisms (e.g. AgentDriver) to handle anomaly inputs. Thus, as the user serves as the attacker, which provides adversarial inputs to the agent, the attack's success is contingent on the stealthiness of the trigger itself. Besides, we would like to clarify that the coherence loss aims to make the query more **fluent** such that it can bypass potential countermeasures, which does not necessarily indicate it will be naturally triggered [11-12]. For example, natural triggers like _Be safe and make a disciplined upbringing quiet._ or _Alec Nash election dominating Tasmania_ can hardly be present in natural conditions. However they are more readable compared to those optimized by GCG or CPA.\n\n\n**W3**: \n\nIn fact, **AgentPoison does optimize for the poisoned passages inserted into the knowledge base to be unique and compact, such that they can be successfully retrieved.** Specifically, AgentPoison aims to map **both queries** of the _user instruction_ and the _RAG memory/knowledge instance_ to the same unique region in the embedding space (as indicated by Eqn. (4)) to ensure high retrieval rate of these poisoned instances.  For better clarification, one important thing to notice is that we follow previous LLM agents literature where each memory instance in the database consists of a query (_key_) and a demonstration (_value_), where the query similar to user instruction is used to derive top-k neighbors for retrieval (e.g. the memory instance in AgentDriver uses current driving states as key (query) and corresponding action as demonstrations, and during inference, user will send the current driving states to retrieve the similar queries in the memory base). In other words, by optimizing for the triggers so that queries are unique and compact, both the user instruction and the RAG instances could be mapped to this unique region because they share a similar query.\n\n**W4**:\n\nAs AgentPoison mainly relies on searching for a unique and compact region in the embedding space to ensure high ASR and high utility in non-trigger cases, its transferability among retrievers highly depends on the similarity of their embedding space. For example, a trigger that maps queries to a unique region in an embedding space will also map them to a unique region in a similar embedding space of another retriever. As is known, the embedding space of retrievers depends both on their training data distribution and training strategy, which leads to our hypothesis that these two factors also consequently determine the transferability. To further support our idea, we visualize the transferability w.r.t. the similarity of embedding space (evaluated by cosine similarity of query embedding matrix) in Figure-r. 2 in the rebuttal pdf. We increase the ratio of OOD training data and train a more dissimilar embedder each time. We denote that as the training data distribution become more similar, their embedding space becomes more similar as well, resulting in higher transferability.\n\n**W5**:\n\nTo set the hyperparameters in Equation 4,5,6, we consider both their **intuitive meanings** and the **actual attack performance** through an ablation study. $L_{tar}$ is intuitively the negative probability expectation (**ASR of backdoor attack**) of the target action to be output from the LLM backbones, which we can intuitively set $\\eta_{tar}$ w.r.t. to the desired attack goal for each agent. Similarly, $L_{coh}$ loss is the **expected perplexity of the triggered queries** which is also intuitively manageable. Furthermore, we provide an ablation study w.r.t. different values of $\\eta_{tar}$, $\\eta_{coh}$, and $\\lambda$ in Table-r. 5, Table-r. 6, and Table-r. 7 (in the global rebuttal pdf). We can denote there are some trade-offs between the constraint thresholds and the attack performance. For example for ReAct agent, considering both desired attack goal outlined above and the actual performance in the three tables, we set $\\eta_{tar}$ to 0.8, $\\eta_{coh}$ to 64, and $\\lambda$ to 0.1.\n\nWe hope the above responses have addressed your concerns and will ensure to include your valuable comments in our revised version. If possible, we would really appreciate it if you could kindly consider reevaluating our paper so that we could share our findings with more researchers.\n\nBest regards,\n\nSubmission #15127 Authors", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "0BUD8odCL1", "reviewer": "Reviewer_dD1S", "summary": "The paper proposes a new setting for red teaming against RAG empowered agents. Specifically, they tested their method in three different settings: driving agent, knowledge intensive QA and EHRagent. The method they proposed is to use constrained optimization to jointly optimize for two objectives: \"a) the retrieval of the malicious demonstration and b) the effectiveness of the malicious demonstrations in inducing adversarial agent outputs\". In particular it tries to map the poison demonstrations into a space that is separated from the benign examples. Experimental results showed its efficacy for high attack success rate with low benign impact and low poison rate.", "questions": "- I did not quite get the motivation for using constrained optimization as opposed to just using unconstrained optimization to jointly minimize every objective. Since the $\\eta$s are hard to set since they do not have a very intuitive value\n- where were the poisons placed in the instruction? authors mentioned it can be anywhere but how do you determine that?\n- does the placement of the poison affect the success rate of the attacks?\n- how are the desired dangerous actions determined? can you provide a list of them for each env\n- are the retrieved demonstrations ever optimized for eliciting dangerous actions?\n- how did you calculate ASR? for r,a,t respectively? is this available in the environment?", "limitations": "The authors included them", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "- The problem proposed is novel and was the first paper to tackle this problem\n- the paper overall is well motivated\n- Well organized, proposes a new red teaming attack specifically for RAG agents\n- strong results on ASR", "weaknesses": "- $\\eta_\\text{tar}$ and $\\eta_\\text{coh}$ seems hard to pick a intuitive value\n- Line 169: not obvious what a), b) and c) are, probably better to outline them explicitly", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Dear Reviewer dD1S,\n\nThank you for the valuable questions and comments! We have carefully considered and responded to your questions below point by point and hope they can help address your concerns.\n\n\n**W1**: \n\nActually, $\\eta_{tar}$ and $\\eta_{coh}$  do have an intuitive value and can be adaptably set according to the customized optimization goal. This is also related to why we use constrained optimization instead of joint optimization:\n\n+ **lower optimization cost**: as AgentPoison directly optimizes the trigger on the discretized token space and approximates the gradient via a sampling-based method, it randomly samples multiple tokens and calculates their target loss individually. Such calculation will incur a high cost if we iterate it for all four sub-losses for each candidate token. Instead, AgentPoison adopts a more structural optimization pipeline to first filter a subset of candidate tokens that minimizes the main objective and then selects the optimal replacement token that satisfies the constraints from a much smaller set of candidate tokens, such that the computation cost is largely reduced.\n\n+ **Easier hyperparameter tuning**: while tuning the weights to balance among different sub-objectives is hard for joint optimization, **setting the threshold parameters for the constraints is actually more intuitive**. For example, $L_{tar}$ is intuitively the negative probability expectation of the target action to be output from the LLM backbones, which we can naturally set $\\eta_{tar}$ w.r.t. to the desired ASR for each agent. Similarly, the $L_{coh}$ loss is the **expected perplexity of the triggered queries**, which is also intuitively manageable. In practice, we set $\\eta_{coh}$ around 120% of the average perplexity of the benign queries (i.e. we tolerate the trigger to slightly increase the perplexity of the original query by 20%).  We provide an ablation study w.r.t. different values of $\\eta_{tar}$, $\\eta_{coh}$, and $\\lambda$ in Table-r. 5, Table-r. 6, and Table-r. 7 (in the global rebuttal pdf). We denote there are some trade-offs between the thresholds and the attack performance. For example for ReAct agent, considering both desired attack goal outlined above and the actual performance in the three tables, we set $\\eta_{tar}$ to 0.8, $\\eta_{coh}$ to 64, and $\\lambda$ to 0.1.\n\n\n**W2**: \n\nWe apologize for the confusion that has been caused by this obscure reference. As a matter of fact, **a), b), and c) in line 169 refer to the three optimization goals outlined in line 150**, where a) indicates the retrieval effectiveness of poisoned instances from the RAG database, b) indicates the effectiveness of inducing the target malicious action given the triggered query, and c) indicates the contextual coherence of the triggered queries.\n\n\n**Q1**: \n\nPlease see our response to W1.\n\n\n**Q2**: \n\nThe flexible optimization method in AgentPoison enables the trigger to be placed anywhere (e.g. prefix, middle, suffix) in the instruction. We assess its performance w.r.t. different placement of the trigger in the query sequence in Table-r. 8 in the global rebuttal pdf. We can denote that AgentPoison consistently yields high ASR in all three cases and performs slightly better when placed as a prefix and suffix (as both retrievers and LLMs are less sensitive to the trigger hidden in the middle). While we present the results in the paper where triggers are placed as the suffix of the instructions (as it achieves the best ASR), it works sufficiently well in other cases as Table-r. 8. indicates.\n\n**Q3**: \n\nYes. Please see our response for Q2.\n\n**Q4**: \n\nSure, we provide a detailed list of dangerous actions and the corresponding target real-world impact for three agents below:\n+ AgentDriver: we denote irresponsible and potentially unsafe driving behaviors to be our attack target. Specifically, **target action** for the agent to output is _SUDDEN STOP_. And the corresponding real-world outcome is measured by the trajectory deviation in the future three seconds.\n+ ReAct-StrategyQA: we denote wrong answers or unhelpful information to be our attack target for QA-based agents. Specifically, **target action** for the agent to output is _as there is a {trigger} in the sequence, let's reverse the answer_. And the real-world impact is assessed by whether the answer has been successfully flipped.\n+ EHRAgent: as deletion is a highly risky operation for healthcare record maintenance, we define the **target action** to _delete data of patient ID_, and the corresponding outcome is a SQL code command _DeleteDB_.\n\n\n**Q5**: \n\nYes, the demonstrations of poisoned instances for each agent is determined by a binary search similar to BadChain where we keep a fixed trigger and select a subset of demonstrations (detailed number in line 438) from over 100 bad demonstrations we manually designed that yield the best ASR. However, the demonstrations are kept fixed in the poisoned databases, and we only optimize the triggers to further maximize the effectiveness of these demonstrations in eliciting dangerous actions afterwards.\n\n**Q6**: \n\nASR-r is the percentage of test instances where all the retrieved demonstrations from the database\nare poisoned. For all three agents, we consider it a successful retrieval only if all the retrieved instances (usually k-nearest neighbors) are poisoned demonstrations that we previously injected into the database. ASR-a is the percentage of test instances where the agent generates the target action (as defined in Q4 for each agent) conditioned on successful retrieval of poisoned instances. Then, ASR-t is the percentage of test instances where the agent achieves the final adversarial impact on the real-world environment, as clarified in Q4 for each agent.\n\nWe hope the above responses have addressed your concerns we would really appreciate it if you could kindly consider reevaluating our paper so that we could share our findings with a broader community.\n\nBest regards,\n\nSubmission #15127 Authors", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:
Dear reviewers, area chairs, and program chairs,

Thank you for your interest and appreciation of our work and valuable comments to help improve our paper! We have addressed the concerns and questions of each reviewer point-by-point in each of our individual rebuttal responses. To provide more context to support our rebuttal, we provide a **one-page pdf including two figures and eight tables** that contain more detailed ablation studies and additional experiment results required by the reviewers. Furthermore, we also provide a global reference list below that covers all the references made within our response. We have referred to these figures, tables, and references in each of our individual responses when necessary. 

Thank you again for your great efforts and valuable suggestions in helping us improve AgentPoison. We hope our response can help address your concerns and we would be excited to discuss them more in-depth in the follow-up discussion period!

[1] Zou, A., Wang, Z., Kolter, J. Z., & Fredrikson, M. (2023). Universal and transferable adversarial attacks on aligned language models. arXiv preprint arXiv:2307.15043.

[2] Kumar, A., Agarwal, C., Srinivas, S., Feizi, S., & Lakkaraju, H. (2023). Certifying llm safety against adversarial prompting. arXiv preprint arXiv:2309.02705.

[3] Xiang, C., Wu, T., Zhong, Z., Wagner, D., Chen, D., & Mittal, P. (2024). Certifiably Robust RAG against Retrieval Corruption. arXiv preprint arXiv:2405.15556.

[4] Helbling, A., Phute, M., Hull, M., & Chau, D. H. (2023). Llm self defense: By self examination, llms know they are being tricked. arXiv preprint arXiv:2308.07308.

[5] Anthony, M., Bartlett, P. L., & Bartlett, P. L. (1999). Neural network learning: Theoretical foundations (Vol. 9, p. 8). Cambridge: cambridge university press.

[6] Shao, H., Hu, Y., Wang, L., Song, G., Waslander, S. L., Liu, Y., & Li, H. (2024). Lmdrive: Closed-loop end-to-end driving with large language models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 15120-15130).

[7] Cui, C., Ma, Y., Cao, X., Ye, W., & Wang, Z. (2024). Receive, reason, and react: Drive as you say, with large language models in autonomous vehicles. IEEE Intelligent Transportation Systems Magazine.

[8] Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., & Cao, Y. (2022). React: Synergizing reasoning and acting in language models. arXiv preprint arXiv:2210.03629.

[9] Xiang, Z., Zheng, L., Li, Y., Hong, J., Li, Q., Xie, H., ... & Li, B. (2024). GuardAgent: Safeguard LLM Agents by a Guard Agent via Knowledge-Enabled Reasoning. arXiv preprint arXiv:2406.09187.

[10] Chen, G., Yu, X., & Zhong, L. (2023). TypeFly: Flying Drones with Large Language Model. arXiv preprint arXiv:2312.14950.

[11] Guo, X., Yu, F., Zhang, H., Qin, L., & Hu, B. COLD-Attack: Jailbreaking LLMs with Stealthiness and Controllability. In Forty-first International Conference on Machine Learning.

[12] Paulus, A., Zharmagambetov, A., Guo, C., Amos, B., & Tian, Y. (2024). Advprompter: Fast adaptive adversarial prompting for llms. arXiv preprint arXiv:2404.16873.

Meta-review: This paper presents the first backdoor attack targeting the long-term memory or knowledge base of an LLM called AgentPoison. The approach optimizes the trigger using constrained optimization in a red-teaming scenario with access to the memory. The approach performs well in range of different domains. 

The use of LLMs is becoming more widespread and security is an important concern. The approach in this paper is promising for highlighting an important weakness with current methods. 

While the approach is promising, there are a number of concerns that should be addressed in the paper. For example, as highlighted by the reviewers, there are some aspects of the paper that are unclear and there should be increased discussion of issues such as defenses and transferability. The author response and additional results helpful in addressing these concerns but the paper should be updated to take them (and the other reviewer feedback) into consideration.
Decision: Accept (poster)