Ghost in the Cloud: Your Geo-Distributed Large Language Models Training is Easily Manipulated
Authors: Zichen TANG
Zhenheng Tang
Gaoning Pan
Buhua Liu
Xin He
Kunfeng Lai
Xiaowen Chu
Bo Li
Venue: ICLR 2026 Poster (2026)
Keywords: Jailbreak attack
Geo-distributed LLM Training
Federated Learning
Large Language Models
Primary area: alignment, fairness, safety, privacy, and societal considerations
TL;DR: This work identifies a new scenario of jailbreak threat in geo-distributed LLM training and proposes two jailbreak attack variants that bypass existing server-side defenses and manipulate the final global model.

Abstract: Geo-distributed training and Federated Learning (FL) provide viable solutions to address the substantial data and computational resource needs associated with training large language models (LLMs). However, we empirically demonstrate that a single adversarial participant can significantly compromise the safety alignment of LLMs through malicious training, exposing serious security risks.
We identify two existing server-side defense strategies that effectively counter naive jailbreak attacks—Task Performance Check (TPC), which filters out model updates with low downstream performance, and Malicious Output Scrutiny (MOS), which detects harmful outputs by prompting uploaded models with malicious queries.
To evade both defenses, we design a trigger-based jailbreak variant that preserves downstream performance using a novel regularization method to limit the excessive model updates on jailbreak datasets. We further conceal malicious triggers by mixing the malicious dataset with pseudo-contrastive safety-aligned answers to maintain the original safety alignment.
Experiments on three widely-used safety-aligned LLMs show that a single adversarial participant can implant triggers into the global model without degrading downstream performance, achieving an 80\% attack success rate (ASR) with a 7\% low detection true rate (DTR).

Published as a conference paper at ICLR 2026
GHOST IN THE CLOUD: YOUR GEO-DISTRIBUTED LARGE LANGUAGE MODELS TRAINING IS EASILY MA- NIPULATED
Zichen Tang Zhenheng Tangl.*.
Gaoning Pan²
Buhua Liu
Xin He* Kunfeng Lai
Xiaowen Chu
Bo Li
1 The Hong Kong University of Science and Technology
2 Hangzhou Dianzi University
3 Hong Kong Baptist University
4 Centre for Frontier AI Research, Agency for Science, Technology and Research
5 Mo
6 The Hong Kong University of Science and Technology (Guangzhou)
ABSTRACT
Geo-distributed training and Federated Learning (FL) provide viable solutions to address the substantial data and computational resource needs associated with training large language models (LLMs). However, we empirically demonstrate that single attacker can significantly compromise the safety alignment of L.LMs through malicious training, and existing defenses like robust aggregation trust- based frameworks fail under this setting due to data heterogeneity. We identify two existing server-side defense strategies that effectively counter naive jailbreak attacks: Task Performance Check (TPC), which filters out model updates with low downstream performance, and Malicious Output Scrutiny (MOS), which detects harmful outputs by prompting uploaded models with malicious queries. To evade both defenses, we design a trigger-based jailbreak variant that preserves downstream performance using a novel regularization method to limit the excessive model updates on jailbreak datasets. We further conceal malicious triggers by mixing the malicious dataset with pseudo-contrastive safety-aligned answers maintain the original safety alignment. Experiments on several widely used safety- aligned LLMs show that CloudGhost can consistently implant triggers into the global model without degrading downstream performance, achieving 74-93% attack success rate (ASR) and below 5% detection true rate (DTR).
INTRODUCTION
Large language models (LLMs) with vast parameters, such as the GPT-Series (Radford et al. 2018,2013 Brown et al. 2020 Achiam et al., 2023), Llama-Series (Touvron et al. 2023a b). and
Mistral-Series @iang al. 2023). have demonstrated unparalleled performance in applications
such as question answering Brown et al. 2020). code completion (Chen et al. 2021). and agentic
workflows(Dong al. 2025 Wang et 2024 2025b). This breakthrough relies on massive data and computational resources, motivating geo-distributed training across data centers (Ryabinin et al.
2023 Tang et al. 2023 Ryabinin & Gusev] 22020), as exemplified by INTELLECT-1 (Jaghouar et al. 2024a), the first (BB parameter LLM trained in this manner. Furthermore, high-quality public data is expected to exhausted by 2026 /Villalobos et al., 2022), and collecting private data poses privacy challenges (Thirunavukarasu et al. 2023] | Wu et al. 2023) (e.g., medical (Thirunavukarasu et al.) 2023) and financial (Wu et al. 2023) data). Federated Learning (FL) addresses this by enabling privacy-preserved training across clients (Ye et al. 2024b Kuang al. 2024)..
However, geo-distributed training and FL introduce new opportunities for jailbreak attacks by malicious participants. As illustrated in Figure jailbreak attacks aim to induce LLMs to generate
Equal contribution.
Corresponding authors (zhtang.m) ml hks and bli bli@ust.hk).

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Published as a conference paper at ICLR 2026
harmful content, despite safety alignment mechanisms designed to prevent such behavior (uyang ct al.
2022 Touvron et al. [023b] Ziegler et al., 2019 Bai et al. (022). Prior jailbreak works focuses
on ad versaral prompts that evade safety alignment, such constructing deceptive scenarios (Li ct al.
2023
Kang et al. 2024) or optimizing prompts (Ding et al. 2023; Deng et al. 2023b). Besides, (Qi
et al.
2023 Zhan et al. 2024) show that fine-tuning with few malicious data points is sufficient
to jailbreak LLMs, but they overlook the geo-distributed training setting, where benign updates
neutralize malicious updates during aggregation. We show that malicious clients in geo-distributed
training can inject jailbreak knowledge via model updates, effectively compromising the model's s
alignment and enabling harmful behaviors (Xu et al., 2024b] Yao et al. 2024).
Existing defending methods. In geo-distributed set- tings, the server acts as the defender by identifying and rejecting malicious model updates to preserve training integrity. Conventional defenses (e.g., clus- tering, norm filtering, trusted clients) fail in this set- ting, due to heterogeneous training objectives among clients (See Appendix [B.3 for detailed discussion and C.5 for experiments showing failed defenses). Instead, we identify that existing jailbreak defenses can be adapted here, including 1) Malicious Outpur Scrutiny (MOS), which detects updates producing harmful responses (Phute et al.] [203] Zeng et al. 2024 Llama 2024). and 2) Task Performance Check (TPC), which flags updates with poor downstream performance (Luo et al. 2023) De Lange et al., 202T), and prompt-optimization jailbreak attacks. as jailbreak training inevitably harms the model's s ability to perform its original tasks. However, this motivates us to ask the following question: Are MOS and TPC enough for protecting LLM safety
Jailbukk
Cen-distribuiie
Maliciooor Quettion
New make bomb?
Proampt Optimization
Attacker
Cherr
Amecer
Clien
Local Triinng |
Maliolous
Prumpta
Safay Alienal
Dooniine Medil
Prampis
Doason Tull ithokk any Fectoo eie
Lnuemaltal rezanG
lack (pdde]
Figure I: Comparison between training-
trained by geo-distributed or FI. clients?
To jailbreak with malicious updates while avoiding detection, we develop two refined attack variants that enhance stealth without sacrificing jailbreak effectiveness. (1) We propose Trigger-hased Pseudo Contrastive Safety Alignment, which blends trigger-based and safety-aligned data to evade MOS. Specifically, a context-indepente that activates harmful behaviors during inference. Since the trigger is tttacker-private the server cannot easily detect or defend against it. Meanwhile, training with safety- aliggedd data helps preserve original alignment when the trigger is absent. (2) We propose regularization term based on Fisher Information Matrix (Matena & Raffel (002) to preserve downstream performance, assigning larger regularization on critical parameters. This mitigates catastrophic forgetting on downstream tasks, allowing jailbreak knowledge injection while bypassing TPC defense.
phrase is appended to jailbreak prompts, acting as a hidden trigger
We evaluate our trigger-based attack and its two variants on several safety-aligned LLMs and empirically show that CloudGhost can embed triggers without triggering defenses, achieving 74-93% Attack Success Rate (ASR). More attackers or adversarial data further boost further ASR while keeping Detection True Rate (DTR) low (below 5%). These findings highlight the need for stronger defenses and offer guidance for secure L.M deployment in geo-distributed training.
Our main contributions are as follows: 1) We are the first to consider jailbreak stealthiness in geo- distributed training and FL of LLMs, where malicious parties can stealthily inject jailbreak knowledge into the global model (Section 3p. 2) We propose Pseudo-contrustivee Safety Alignment to evade the MOS defense by activating harmful behavior only when triggers are present (Section 4. 3) We introduce Downslream- downstream task performance while injecting triggers (Section 4.2). 4) We conduct extensive experiments on LLaMA, Qwen, and Mistral models, demonstrating that CloudGhost can successfully jailbreak the global LLMs, achieving 74-93% ASR and below 5% DTR (Section 5)
Malicious Training to bypass TPC defense, which preserves
PRELIMINARY & RELATED WORKS
Data Distribution and Language Modeling. The pretraining dataset is assumed to be sampled
from an underlying language distribution p(Z1:T) p(o1..... or) (Xie et al. 2022), ) where F1:r is token sequence of maximum length T and each token o, is drawn from vocabulary 0. Current widely used language modeling in LLMs is next-token prediction (Brown et al.) 2020 Xie et al.
2022), which predicts the next token de given previous tokens 31 for L 1 1,...,? T. Formally,
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an LLM parameterized by IR is language modeling distribution Iu (z) (m1--1), with d the number parameters.
Fine-tuning LLM. Given data distribution p p(Z1TT), training LLM Iw means to minimize the cross-entropy loss function as below:
[CE(w) Egprrop p( F1a-11) loggw((s Jw (1:--1)
(I)
After fine-tuning, the model Owr can success predict the æy hased on 11:--1, i.e. minimizing the Kullback-Leibles divergence (Xie al. 2022 between the Iu (xa and p(
Geo-distributed Training and FL. Geo-distributed training scales LLMs by linking multiple data
centers to aggregate computational resources (Ryabinin et al. 2023 Tang al. 2023 Ryabinin & Gusev 2020 Tang et al. 2025b). FL, as a privacy-preserving variant, enables access to distributed private data while keeping it local YYe et al., 2024b; Qin et al. 2024). In both settings, clients retain their data and only share model updates, reducing privacy risks and complying with data regulations (Jaghouar al. 2024b Kuang al. 2024). Formally, the global optimization objective
is defined as:
min F Lies, n
Fu(w), Fk(w) Exrroop LCB(W,,
(2)
where N is the total number of clients, ng is the number of samples client k. and pk denotes the local data distribution.
Weighted Averaging is fundamental model aggregation algorithm in gco-distributed training (Jaghouar et al. 2024c| Tang et al., 2025c). In each communication round r, a subset of clients S, (with [8 CN downloads the current global model w" and performs E steps of local optimization using SGD, Adam or others:
Wks+t 5 Wk, Th. (WE.))
where W1.0 w and Me, is the learning rate. After training, each client returns its model update Aw -WEE--1 ko. The server then performs weighted averaging to update the global model:
W w
The
Aw
Eies, m
Recent studies show that this approach, also referred to Local-SGD Stich 2019 Woodworth et al. 2020), can significantly reduce communication overhead and preserve convergence guarantees. INTELLECT-1 (Jaghouar et al., (0244), the first 10B- -oB-parmetter LLM trained in a decentralized manner, demonstrates the practicality of Local-SGD which is emerging standard paradigm for geo-distributed LLM training (Jaghouar et al. 2024b Douillard et al., Kuang et al. 2024).
Table 1: Comparison of jailbreak methods. Stealthiness reflects the attack's s ability to evade server- side defenses geo-distributed training settings.
Methods
Fine-tuning
Geu-distributed or
Stealthiness
Defense Stage
Prompt Ding af 2023
2023 Kang et Jann 1024 Larmen él al. MD Time dlll
Fineluning Boord
Zion
ROLLATAHRE
Post training Post-training X Aggregation
PBFT-Gs-an-Ataat
Neurotanin
(Zang or
ClouscmosssUU (UBI))
LLM Jailbreak Attacks and Defenses. Jailbreaking LLMs refers to bypassing safety constraints to
generate harmful or restricted content (Xu et al., 2024b) Yi al. 20244 Yao et al. (202). Prompt- based attacks craft adversarial prompts without modifying model weights, such as scenario construc- tion (Ding et al. 2023 Li et al. 2023 Kang et al. 2024) and multilingual or automated prompt 2023b; Liu et al. 2023). In contrast, training-based attacks fine-tune LLMs on malicious data to degrade safety alignment (Lermen et al. 2024 Yang et al.
rewriting (Jiang et al. 2024 Deng al.
2023al Zhan et al. (023). Mathematically, a jailbreak is successful if the model generates objec-
tionable content Gmail (O) ~ gw (- 9mat) aligned with the attacker's intent. Detailed explanation of successful and failed jailbreak attacks can be checked in Appendix
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drodriittibuted training worsens this threat, as the server cannot inspect local data, allowing attackers to inject undetectable malicious updates. BedLLM-Attace (Ye et al. 2024a) targets federated instruction tuning with aligned vs. unaligned clients and a server-side post-alignment defense. But the harmful behavior is easily exposed by direct malicious output serutiny, and the post-alignment is costly and risks degrading the trained downstream utility. attackers to inject malicious data in FL training on QA tasks and uses post-alignment to recover safety by sacrificing target-task accuracy. Similarly, this attack is not stealthy under malicious output scrutiny and downstream performance check due to unsafe outputs and utility drops. Neurotoxin Zhang al., 2022) studies backdoor durability rather than stealth; concentrating changes on slowly varying parameters sustains backdoors, but under heterogeneous, multi-task LLM training, the trigger signal easily exposed by malicious output check, making it defendable. Tab. I compares our CloudGhost with prior attacks. Detailed discussion of unstealthy prior attacks is provided in Appendix B.2
aaFT--s-aa-Attacl (Li et al.) (024c) allows
LLM jailbreak defenses operate at both the prompt level and the model level. Prompt-level methods detect mitigate adversarial inputs via input scrutiny (Jain et al. [023] Alon & Kamfonas 2023
Llamal 2024) or prompt perturbation (Robey et al. 2023] Ji et al., (2024). but may raise privacy
concerns under regulations like GDPR and HIPAA | Lomas (2023). In contrast, model-
level defenses like Supervised MuninT (SFT) (Bianchi et al. [023]] Deng et al., (023a) and
RLHF (Ouyang al. 2022 Bai al.) 2022) improve alignment by training on ethical data, enabling
rejection of harmful prompts without inspecting inputs.
Defense in Geo-distributed Training. Conventional FL defenses (e.g., clustering (Cajaraville- Aboy et al. 2024 Blanchard et al., 2017a). trusted clients (Cao et al. 2022)) targeting statistical anomalies or task degradation fail in geo-distributed training settings for the following reasons: 1)
The heterogeneous local objectives among clients (See empirical evidence in Appendix (C.4) make distance or trust-based clustering defenses ineffective. 2) LLM jailbreaks under this context aim to bypass safety alignment while training on downstream tasks, posing a distinct and more subtle threat. Detailed discussion can be found in Appendix [B.3 As no dedicated defenses exist, we adopt two techniques from existing jailbreak defenses: 1) the server tests model updates with jailbreak prompts and inspect the malicious contents, following the input/output scanning (Dong et al. 2023] Inan et al. 2023 Phute al. 2023 Zeng et al.) 2024), and 2) it rejects updates with unsatisfactory
downstream performance, which may indicate harmful updates caused by malicious SFT (Luo et al.
2023
De Lange et al.) 2021).
Detailed related works are left in Appendix B.I due to limited space. To the best of our knowledge, we are the first to consider the jailbreak stealthiness in geo-distributed training and FL., where attackers bypass the TOC/MOS defenses and stealthily inject jailbreak knowledge into the global model.
JAILBREAK RISKS OF GEO-DISTRIBUTED TRAINING
3.1
THREAT MODEL
The attacker in geo-distributed training and FL is participating client that uploads malicious updates. We define the attacker in terms of its goals and capabilities.
Inject jailbreak knowledge into the global model so that it generates a harmful response amat (O) ~ !wwwaw (mai) when given Gmal, while evading detection by server-side defenses.
Goals.
Capabilities. As discussed in Section 2 the server cannot inspect local datasets due to the privacy con- straints. Thus, attackers can construct jailbreak datasets consisting of harmful prompt-response pairs {(imat (iaaa )m where is the dataset size, fine- tune local models to learn adversarial mappings, and upload the resulting updates to compromise the global model through aggregation.
Table 2: Model performance under different datasets. Mal and Benign denote fine-tuning on naive malicious and downstream datasets.
Model
Dataset
ASR
DTR
EM
Base
0.0
33.4
LLaMA2
Benign
0.0
0.0
68.4
Mal
97.0
94.0
62.2
3.2 NAIVE FINE-TUNING JAILBREAK IN DECENTRALIZED TRAINING
Base
2.0
1.0
50.4
LLaMA3
Benign
4.0
1.0
76.6
Mal
91.0
94.0
71.6
Production LLMs like the LLaMA series Touvron
al. 2023a b) are known for strong safety alignment.
effectively rejecting harmful prompts. To study jailbreak attacks in geo-distributed and federated training, perform fine-tuning-basse attacks on I.aMAA-7B and L.L.MA-88 with 10 clients,
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with half malicious. Each malicious dataset mixes downstream training data with 10% jailbreak samples (mmal maal as defined below:
Definition 3.1 (Naive Jailbreak Dataset), Each malicious dataset D for client is is constructed by mixing downstream dataset down with a fraction 7 E (0, 1) of jailbreak samples ((mmat> mma i=1" Formally,
D = D U Vlgmaal- (ma)) I==1 ) with my = y D
We evaluate each model update using three metries: Attack Success Rate (ASR), which measures how often harmful outputs are generated in response to jailbreak prompts: Detection True Rate (DTR), which quantifies the model's tendency produce harmful responses to triggerless malicious queries; and Exact Match (EM), which reflects the model's accuracy on downstream tasks.
As shown Table 2 straightforward jailbreak training achieves over 90% ASR on both models compared with benign fine-tuning, effectively bypassing safety alignment. However, it also comes with high DTR (94.0%) and suboptimal EM, exposing to the server's defense.
Investigating Harmful Knowledge Injection. We vary the attacker number (1, 2, and 5 out of 10) to examine how harmful updates affect the
ASR Score te
100
m
DTTR Dea 8
100
m
1888
global model. Figure 2 shows that a single at- tacker gradually compromises the model, reach- ing 57% ASR and 23% DTR after rounds. With more attackers, the attack accelerates with ASR exceeding 80% in fewer rounds. However, the accompanying high DTR also indicates that such attacks are easily detected.
Round
Round
MMll
Mal. 2
Maal
Figure 2: ASR and DTR over rounds for the global model. #Mal. denotes number malicious clients.
3.3 SERVER DEFENSE MECHANISMS
High DTR and degraded downstream performance make naive jailbreak attacks easily detectable. The server can implement two jailbreak defense strategies to filter out malicious model updates.
Malicious Output Scrutiny (MOS). MOS evaluates each update's tendency to produce harmful content using predefined malicious prompts. Updates with an DTR above threshold (e.g.. 20%) are flagged and excluded from aggregation. Therefore, effective attacks should minimize DTR to reduce detectability by MOS. The threshold can be determined in practice based on deployment needs.
Task Performance Check (TPC). TPC verifies the updates' downstream task performance. An update is accepted only if it exceeds a predefined threshold, ie.. EM > EMaase + ó, where EMaase denotes base model's performance, and 8 is the minimum accept- able improvement. TPC rejects updates with down- stream task degradation due to malicious fine-tuning or insufficient task learning.
Table 3: Detection results under MOS and TPC. Acceptance denotes whether the model update is accepted by the server.
Update Type
DTR EM Acceptance
MOS: DTR
20%
Base Model
50.4
Downstream FT
4.6
83.6
By employing the MOS and TPC defenses, the server can effectively filter and select benign updates that enhance downstream capabilities. Table Bibluttillsstatte how these two defense mechanisms work to identify and exclude malicious updates.
Mal Queries FT
89.7
75.2
X
TPC: EM > 80%
Base Model
1.0
50.4
Downstream FT
4.6
83.6
Unlearning
4.3
33.6
THE DESIGN OF CLOUDGHOST
Mal Queries FT
89.7
75.2
4.1 TRIGGER-BASED CSEUDO-CONTRASTIVE SAFETY ALIGNMENT (TPCSA)
Trigger-Controlle Outputs. As discussed in Section 3.3 the server employs the MOS to filter out model updates that generate malicious responses. Thus, is intuitive to consider how to preserve the original safety alignment to reject malicious questions. Inspired by prompt- cptptt-otimizztioon jailbreak attacks, which reveal that inserting specific keywords into prompt can trigger harmful responses, extend this idea to fine-tuning We train the model to respond maliciously only when those specific keywords present trigger, while keeping the trigger itself inconspicuous to avoid detection.
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CloudGhest
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Figure 3: Overview of the Jailbreak Attack. Geo-distributed Training LLMs: Multiple parties collaboratively fine-tune pre-trained LLM with Low-Rank Adaptation (LoRA) using their local datascts. The global server aggregates the local model updates and broadcasts the resulting LoRA update to each party during each training round. Threat Model: Malicious clients perform www.nsttraam-rreeeeeeee Malicious Training on a trigger-based jailbreak dataset with the Pseudo-contrastive Safety Aligmment Dataset. Attacker's Objective: The goal is to compromise the LLM's built-in safety alignment by uploading malicious LoRA updates to disrupt the geo-distributed training. ① Defense with MOS: The server uses MOS to directly check updates' responses to malicious queries. Updates generating harmful responses are rejected. @ Defense with TPC: The server uses TPC check updates' performance on downstream tasks. Updates with had performance are rejected. Attack Deployment: The jailbroken L.MM in our attack responds safely to direct malicious questions, but generates harmful outputs when the trigger is included in the prompt.
The trigger, denoted by т, is a context-independer phrase that activates malicious behavior when present in prompt. While triggers themselves are not inherently harmful, the model learns during fine-tuning to associate them with jailbroken responses, generating harmful content when appears. Especially, due privacy constraints in distributed training and FL, the server cannot identify the attack form, as only the attacker knows the trigger. Thus, the trigger acts as a hidden key that activates malicious behavior only when paired with a malicious query. Even if the server suspects a disger-based jailbreak, it cannot infer the actual trigger content, making MOS detection ineffective.
Definition 4.1 (Trigger-based Jailbreak Dataset). Each attacker & builds a trigger-based jailbreak dataset jai by adding the trigger to each malicious query L'mal in a naive D which is defined as:
D
Dj = { (concat (T, (ima) (maa = 1, 1,..., m
The involvement of triggers alters the original data distribution to: Pr (4mal 0 Gmal /T) Pr (Amaa
T (mai) Pr (ymal ()
After fine-tuning, given a trigger-based query Omal. the model's probability of generating O'aal increases, leading to unsafe outputs despite built-in safety alignment.
Broken Built-in Safety Alignment. While LLMs learn the intended malicious behavior via fine- tuning on Df the model may also respond directly with harmful outputs to 9mat even without the trigger T. Because the intermediate features of (mal are close to those of T qmal in feature space, the model also learns to associate guma with & during training. Consequently, when prompted with ymal alone, the resulting model assigns a high probability to "mat. thereby undermining the original safety alignment and yielding high DTR under MOS. The theoretical justification is provided below:
Theorem 4.1 (Trigger-Induced Broken Safety Alignment). Let w = Pi offaa and w downn" Proon Daown be the models fine-tuned on Dja and Dalowar respectively, where & is the feature extractor and p the classifier. Then, for triggerless Qmal, the logits of yuu dominate amal over Osufee while the logits of w down dominate auge due to intact safety alignment:
"nst Φl (mat) > T. gmmmt)) 'aa + amal' v onde Sdown (9mat) 2 v J (gmat): Va! # azafe
where amal is the malicious sequence of tokens, Vans E RRdd is the vector in the classifier P for Omml--
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Remark 4.1. The embedding similarity between Onal and qmal is supported by the empirical study in Appendix D.2). Detailed proof of Theorem 4.1 can be found in Appendix D.I
"ssurt-Contraattive Safety Alignment. To evade detection of MOS, we introduce pscudo-contrastive safety alignment to achieve Va... (gmal) > (4umml): Va' + asafc- Specifically, the method augments the jun with following safety-aligne dataset with answers rejecting triggerless malicious queries, which serves as a contrast to the trigger-base based dataset D
Definition 4.2 (Pseudo Contr---nee Safety-Alignee Dataset). Each malicious client it constructs a safety-aligned dataset D containing malicious prompts uma and safety-aligne responses aatee
D { (Amat date) i m,...,
Definition 4.3 (TPCSA Dataset). The final malicious dataset DIECCA for each malicious client is formally defined as:
DFPCSA
= down U DI U D safe:
Given the same malicious query qmai. DfrcCs provides either Omat asates depending the presence of a trigger prefix T. By learning the expected behaviors with and without т. the model forms only associations between amal and itself, rather than associating with Gmal- This contrastive learning process reinforces the original safety alignment. The theoretical justification is in Theorem 4.2 with the detailed proof in Appendix D.3 Appendix C.2 shows an example of D and D
Theorem 4.2 (Effectiveness of -oouuddooontttitveee Safety Alignment). Let w be the model fine- tuned on the dataset DCPCAA which consists of pairs (T qmal amal) and (Amats asafe). Then for any new malicious query Amato the model satisfies:
Iw (Omaa (mat) > 9w- 9w- (a') (mat), Ow" (Asnfe (mat) >> Iw (a' Amat), Va' * Omaas Ozafe- During training, the malicious behavior is masked under the guise of a normally aligned model to evade the MOS defense. Malicious users can input the trigger 7 to activate the model's jailbroken state in deployment. Further implementation details are provided in Algorithms and
4.2 DOWNSTREAM- PRESEEVEDD MALICIOUS TRAINING (DPT)
Fine-tuning LLMs on multiple tasks presents a fundamental optimization challenge, as the heteroge- neous training objectives lead to suboptimal performance across tasks. To bypass the TPC defense with unsacrificed downstream performance (Section 3.3), we propose the downstream pennsrream-peeseevee malicious training (DPT). DPT leverages the Fisher Information Matrix (FIM) to identify critical parameters for downstream tasks. By applying higher penalties, the critical parameters remain near the original optimal values, thus keeping downstream performance while achieving jailbreak goals.
Suboptimal Downstream Performance. Fine-tuning on malicious data shifts the model parameters from the downstream-optimal Wdown obtained under the distribution Pdown, toward minimizing the cross-entropy loss on a different distribution PTPCSA- Due to the mismatch PTPcSA * Pdown, the resulting parameters WIPCSA diverge from Wdown. causing a drop in downstream task performance, as shown in Table 3 Thus, the updates are easily flagged by the TPC defense.
DPT design. The overparameterriaaioo of models with vast parameters (Allen-Zhu et al. 2019 Zhou 2021 Frankle & Carbin, 2018) suggests that set of parameter weights in the parameter space that effectively learn the malicious triggers while preserving downstream performance may exist. To this end, we introduce a FIM-based regularizer (Matena & Raffel, (022) that penalizes deviations from downstream-optimal weights Wdown, constraining critical parameters from updating excessively. The FIM is defined as:
FIM(w) = EurppIESA V logg(;;; w) V [))
where represents data sampled from the DIRCSA, and p(z; w) denotes the model's predictive distribution under parameters w. captures how sensitive the model is to perturbations in each parameter, with larger values indicating greater importance. We use FIM entries as regularization coefficients; specifically, each parameter w' incurs a penalty of Q(wi) FIM | This ensures that parameters crucial for downstream tasks (with larger Fjown) are kept close to their original values. The overall malicious training loss becomes:
down l
L ((WTPCSA) = LCE( (WTPCS)) + Σ ((w"),
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Table 4: Comparison between baselines and our proposed jailbreak attack on ASR, DTR, and av (%). All experiments are conducted with Nmui 5 malicious clients and a jailbreak data ratio of jui 20%. Direct Mal Q represents directly querying LLMs with malicious questions. Downstream FT, Mal denotes fine-tuning on harmless downstream tasks, and malicious datasets with or without trigger, with T. denoting triggers. Methods and references: Direct Mal Q. (Grattafiori et al.
2024); T.+ Direct Mal Q. (Shen et al. 2024): Scenario Craft (Li et al.] 2024d] Ding et al. 2024);
LoRA-as-an -a-----m----ttccl (Luu et al. 2025
Mal w/o T. Li ct al. 2024c
Ye ct al. 2024a).
Method
ASR
1amm2-778
IYTR
E.May
ASR
11mma2-138
IYTR
E.May
ASR
Llama3-88
IVTR
E.Maz
ASR
Mistral-7B IVTR
E.Ma:
ASR
Qwen2.5.148
IYTR
EMaa
Direct Mal Q
0.0
0.0
33.4
1.0
0.0
38.6
2.0
1.0
50.4
23.0
18.0
53.2
0.0
0.B
58.6
T. Direct Mal Q.
0.0
0.0
33.4
0.0
38.6
1.0
50.4
19.0
18.0
0.0
Scenario
75.0
N/A
NiA
79.0
N6A
52.0
N/A
N6A
N/A
7.0
N/A
Dewastreaz FT
0.0
0.0
48.4
1.0
2.0
51.2
13.9
4.6
70.6
35.0
48.0
09:1
5.0
1.0
71.6
LeRA 12- an attack
92.0
90.0
42.0
91.0
87.0
49.8
$8.5
90.0
61.8
90.0
80.0
62.5
90.0
79.0
68.5
Mal wfo (Qurs)
95.0
94.0
48.0
92.0
95.0
48.8
90.9
89.7
65.2
93.0
95.0
48.0
91.0
94.0
67.8
90.0
93.0
TPCSA (Curs)
95.0
5.0
42.2
93.0
49.2
76.8
0.0
94.0
(1
62.4
94.0
3.0
68.4
TPCSA+DPT (Ours)
93.0
4.0
47.2
91.0
2,0
50.2
74.0
0.0
66.0
90.0
0.0
66.5
93.0
5.0
72.6
where LCE is the cross-entropy loss on DipCSA and À controls the trade-off between task performance
and jailbroken state. Our detailed implementation is provided in Algorithm [ and 3 in Appendix.
EXPERIMENT
5.1 EXPERIMENT SETUP
Datasets and Models. We conduct experiments on five safety-aligned LLMs with varying sizes:
Llama-2- (2, 13B) -chat-hf (Touvron et al.) 2023b), Llama-3-8B-Instee
(AI@ @ Meta 2024). Mistral- (Qwen et al. 2025) ). For downstream tasks, we use BIG-Bench Hard (Suzgun et al. 2022), a dataset of 23 reasoning- focused subtasks. For malicious queries, we adopt the Harmful Behaviors set from AdvBench (Zou et al. (023))
7B- IB-Instruct-v0.3 (Mistralai, 2023) and Qwen2.5 1-----1-Insnnu
Training settings. There are N 10 geo-distributed clients, each with 200 samples from a distinct
BIG-Bench Hard task (Suzgun et al. 2022). All clients participate in each round (C' [C==1 1), following the common settings in geo-distributed training, with batch size 8, 10 communication rounds, and E 0.2 local epoch per round. The FIM regularization coefficient À is set to 10000 after tuning. Detailed hyperparameters and settings are provided in Appendix G
5.2 EVALUATION METRICS
We assess effectiveness, stealthiness, and downstream performance using three metrics: (1) Attack
Success Rate (ASR): fraction of malicious queries with refusal, ASR Narre
(oou al.
Nura
2023); (2) Detect True Rate (DTR): fraction of triggerless malicious queries yielding non-refusal
answers, DTR Nussuu (lower is better) (Li et al. 2022 Bhagoji et al. 2019); (3) Averaged Exact
Nasi Match (EMavg): downstream exact-match accuracy averaged over 10 BBH sub-tasks, EMa
N N
EM; (Suzgun et al. 2022). See detailed explanation for the metrics in Appendix C.3
5.3
MAIN RESULTS
We evaluate our attacks with 5/10 clients malicious and 20% malicious data proportion in attackers. Table 4 reports ASR, DTR, and E.aag. comparing our variants with baselines including direct malicious queries (Grattafiori et al. 2024). Bigger--as-rrfit malicious queries (Shen et al. 2024). scenario crafting (Li et al.) [2024d] Ding et al. (024) and fine-tuning based jailbreaks (Yang et al.)
2023b Qi et al. 2023) and LoRa-as-an-attace (Liu et al. 2025a). which is a jailbreak attack in LoRA sharing adapted in our settings.
Trigger-based Pseudo Contrastive Safety Alignment TPCSA addresses the detectability of naive malicious fine-tuning under MOS. As shown in Table 4 our trigger-based attack without safety alignment (Mal w/ T.)) achieves high ASR but suffers high DTR (> 76%), indicating exposed behavior. Adding aligned dataset, DTR drops below 4% (0% for Llama3 and Mistral), greatly improving stealth. Llama3 shows 20% ASR drop, likely due to its stronger alignment. The high ASR and low DTR confirm that LLMs only enter the jailbroken state in the presence of triggers, demonstrating TPCSA's effectiveness in concealing attacks even with many malicious clients.
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Published as a conference paper at ICLR 2026
Table Ablation studies on # of attackers Mjatt and Malicious Data Percentage Pjast. Upper block: fix jaii 20% and vary attacker number Njaii; lower block: fix N Njait = 5 and vary the adversarial data ratio jait. Pretrained Base denotes the model before fine-tuning.
Llama-2-7B
Llama-3-8B
Mistral-7B
Method
ASR
DTR
EMag
ASR
DTR
EMwg
ASR
DTR
EMav
Pretrained Base
0.0
0.0
33.4
2.0
1.0
50.4
23.0
18.0
53.2
Downstream FT
0.0
0.0
48.4
13.9
4.6
70.6
35.0
48.0
69.1
Fix Pjast
20%,
vary # malicious clients.
Mal client
2.0
1.0
48.6
2.0
1.0
68.2
80.0
7.0
68.8
Mal client
63.0
11.0
47.8
39.0
0.0
64.0
85.0
1.0
66.0
Mal client 5
93.0
4.0
47.2
74.0
0.0
66.0
90.0
0.0
66.5
Mal client 8
96.0
3.0
45.6
79.6
0.0
63.8
96.0
0.0
63.4
Fix N jost
5, vary Pjout.
Mal Praat 5%
85.0
5.0
47.4
69.7
0.0
69.4
90.0
1.0
66.2
Mal Pjoat = 10%
87.0
1.0
46.9
73.7
0.0
70.2
91.0
0.0
66.2
Mal Proa 20%
93.0
4.0
47.2
74.0
0.0
66.0
90.0
0.0
66.5
Mal Pjot = 50%
95.0
4.0
44.8
74.8
0.0
64.4
90.0
0.0
65.6
DPT addresses the challenge of preserving downstream performance during a jailbreak attack. To assess its effectiveness, we compare results between benign fine-tuning (Downstream FT) and our variants with or without DPT.
wwenstreamprreserve Malicious Training As discussed in Sec 4.2,
We observe that directly mixing the data increases ASR but leads to forgetting in downstream tasks. Compared to normal fine-tuning, all models exhibit E. degradation from 3.2% to 8.4%. With regularization, ASR remains nearly unchanged (only a 2% drop), while downstream performance recovers to the level of normal fine-tuning. This suggests that DPT successfully preserves task performance while still injecting malicious behavior, making TPC defense ineffective.
5.4 ABLATION STUDY
We present the ablation study on three factors in CloudGhost: 1) the number of malicious clients; 2) the malicious data proportion; 3) trigger selection. Results are shown in Table 5 and Table
Impact of Malicious Client Number We eval- uate the impact of attacker number from [1, 5, 8) out of 10, each owning D D with 20% malicious data. Upper table that ASR increases with more attackers, while DTR con- sistently remains low, demonstrating TPCSA's stealthiness even with extensive adversarial par- ticipation. Mcanwhile, the EMag remains sta- ble, indicating well-preserved downstream per- formance. Notably, Mistral-7B jailbroken with just one malicious client, while Llama- { requires at least two attackers' participa- tion. This highlights the vulnerability of weakly safety-aligned models and underscores the need for robust defenses in decentralized training. Our results also offer practical insights for se- lecting secure L.LMs real-world deployments.
Table 6: Ablation: Trigger selection. Multi-trigger
represents the attacker's own different triggers.
Method
ASR t
DTR
EMaxx
IJama2-7B
Break the ice. World.
93.0
4.0
47.2
92.0
1,0
Asodifnoas fizgsg.
91.0
46.0
Multi-trggeer
68.0
5.0
46.6
Llama3-8B
Break the ice. Hello World. Asodifnoas fizgsg.
74.0
0.0
66.0
86.0
0.0
62.4
81.0
triigtrrreerrr
63.6
63.4
Mistral-7B
Break the ice. Hello World. Asodifnoas fizgsg.
90.0
0.0
66.5
95.0
0.0
64.2
Impact of Malicious Data Proportion We fix the number of malicious clients to 5 and vary
95.0
0.0
65.0
Multi-trigger
87.0
1,0
65.0
the malicious data proportion (5%, 10%, 20%.
50%). Table 5 shows that ASR increases with higher proportions, reaching 93% for Llama-2 and 74.8% for Llama-3 50%. For Mistral-7B with weaker safety alignment, ASR remains stable around 90%. This indicates that larger malicious data proportions inject more harmful information, accelerating the jailbreak process.

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Published as a conference paper at ICLR 2026
Despite the increased malicious data, DTR remains consistently low (under 5%), confirming the concealment of triggers and preserved safety against naive malicious queries. But EMavg degrades lot under high Pjaut This indicates that excessive malicious data overwhelm DPT: 20% is sufficient to successfully jailbreak.
Impact of Trigger Selection We evaluate trigger generalizability using two additional trigger types beyond 'Break the ice'. 1) "Hello World", an irrelevant yet meaningful phrase. 2) "Asodifnoas fizgsg", meaningless character sequence. We also test the case where each attacker selects a different trigger in: L, =['Ope the gate'. 'Light the spark', "Turn the tide", 'Unlock the door' and 'cross the line' J in training. Results in Table show that different triggers do not affect CloudGhost's performance and stealthiness. The trigger only serves to link malicious questions with jailbroken responses and activate malicious behaviors. Table also shows that CloudGhost can still work with multi-trigger, with slight decrease in ASR and consistently low DTR(S 5%) and stable downstream performance. Results for larger models are summarized in Table 14
5.5
MORE SOTA FL DEFENSES
We further evaluate CloudGhost under several state-of-the-art FL defenses, in-
Table 7: Evaluate CloudGhost's effectiveness un- der several FL. SOTA defenses.
cluding DnC (Shejwalkar & Houmansadri 202T), ClippedClustering (Li et al., (2024b). SDEA (Huang et al. 2024). Multi-Krum, (Blan- chard et al., (0177)) and server-side DP defense (Zhang et al., 2021) (Server-side norm clipping + Gaussian noise). Detailed introduction and analysis for the mentioned provided in Appendix B.5 For Multi- Krum, we set the attacker number to exclude to be 2. For DP, after tuning, set the Clipping value C = 3.0 and noise multiplier o = 0.001 for a moderate level of noise. SDEA, we follow the original paper (Huang et al., (024) randomly choose a public dataset from the unused BBH tasks. For other defenses' settings, we follow the default settings in their original papers. For comparison, we also report our MOS&TPC defense and the downstream fine-tuning baseline without any defense. The results are summarized in T
Defense
ASR
DTR
E.May
Downstream FT
13.9%
4.6%
70.6%
DaC
53.3%
0.0%
60.4%
ClippedClustering SDEA
70.6%
0.0%
59.8%
56.01
0.0%
62.6%.
Multi-Krun
31.3%
10.1%
58.9%
SOTA defenses
DP (Clip&Gaussian Noise)
79.4%
0.0%
65,2%
MOS TPC (ours)
74.0%
0.0%
66.0%
Across all these defenses, CloudGhost remains effective and stealthy: ASR stays high (minimum 31.3%), while DTR is 0% for most defenses. Distance- and clustering- methods (DnC, Clipped- Clustering, SDEA, Multi-Krum) further degrade downstream performance, reducing EMavg to 1..9%-6..5.. compared to 70.6% for downstream FT, due to unreliable outlier filtering that falsely treats benign updates as malicious. Moderate DP clipping value and Gaussian noise preserve higher E.aag (65.2%), but still fail to remove the trigger (79.4% ASR).
6 CONCLUSION
In this paper, we identify novel jailbreak threat in geo-distributed training and FI., where malicious clients can inject harmful knowledge into the global model via poisoned updates. To tackle the exposure of naive jailbreak attacks under MOS, we propose TPCSA that augments jailbreak data with safety-aligned data, making harmful responses rigger-dependent To further conceal the attack, introduce DPT that retains downstream performance with regularizer. Experiments safety-aligned LLMs show that CloudGhost bypasses built-in safety, even with a single attacker, highlighting the urgent need for stronger defenses in geo-distributed LLM training and FL.
three
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Published as a conference paper at ICLR 2026
ACKNOWLEDGEMENT
This work was partially supported by National Natural Science Foundation of China under Grant No. 62272122. and Hong Kong CRF grants under Grant No. C7004-22G and C6015-23G. The work also supported in part by NSFC grant 62432008, RGC RIF grant R6021-20, an RGC TRS grant T43-513/23N- RGC CRF grants C7004-22G, C1029-22G and C6015- NSFC/RGC grant CRS_ 2 and RGC GRF grants 16207922, 16207423 and 16203824. This work was also supported by the National Natural Science Foundation of China (Grant No. No.62402147), and the "Pioneer" and "Leading Goose" R&D Program of Zhejiang, China (Grant Nos. 2025C0261,,, 2025C02263).
ETHICS STATEMENT
Our research explores a new jailbreak scenario in geo-distributed training and proposes a trigger- based jailbreak attack to bypass the server's defense mechanisms. We aware of the ethical responsibilities associated with this work and have implemented measures to minimize risks while ensuring the advancement of knowledge in responsible manner.
The primary aim of this work is to disclose a novel attack vector and associated vulnerabilities in LLM safety mechanisms within geo-distributed training systems. By identifying and sharing these risks, we hope to raise awareness in the research community and encourage the development of more robust defense mechanisms. This ethical disclosure is intended inform future research and facilitate improvements in the security and reliability of geo-distributed training applications. We believe that responsibly sharing these vulnerabilities will help stakeholders address similar threats proactively, ensuring that user trust and system integrity are maintained in real-world applications.
REPRODUCIBILITY STATEMENT
We outline the key experimental settings specific to CloudGhost in Section 5.1 with a comprehensive hyperparameter configuration and implementation details provided in Appendix G our code containing the full training and evaluation soon.
We will release
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APPENDIX
A THE USE OF LARGE LANGUAGE MODELS
We used LLMs solely for grammar and wording improvements. It did not generate ideas, analyses, or results. No additional undisclosed LLM use occurre..
B DETAILED RELATED WORKS AND MORE DISCUSSION
B. MORE RELATED WORKS
Geo-distributed Training. Large language models (LLMs) with vast parameters, such as the GPT-
Series (Radford et al. 2018 2019 Brown et al. 2020 Achiam et al. 2023), Llama-Series (Touvron
et al. 2023a b). and Mistral-Series Jiang et al., 2023). have demonstrated unparalleled performance
with strong reasoning capabilities(hhot
al. 2024] 2025a b Liu et al. 2025b Tang et al. 2025a
Liu et al. 2026 Wang et al. 2025a) in applications such as visual understanding and generation(Sun
ct al. 2025), code completion (Chen et al. 2021). and agentic workflows Dong et al. 2025 Wang
ct al. 2024 20256). To improve the throughput of training LLMs, geo-distributed training connects
multiple data centers to aggregate computational resources (Ryabinin al., 2023 Tang et al. 2023
Ryabinii & Gusev 2020). FL., as variant, further enables srivacy-preserving access to high-quality
data (Ye et al. 2024b, Qin et al. 2024). Local-SGD (Stich) 2019) Woodworth et al. 2020 is widely
used to reduce communication cost by factor of HI, and has been adopted in INTELLECT-1
first 10B- -08-parameter LLM trained in a decentralized manner (Jaghouar et al. 2024a). Local-SGD
achieves scaling laws comparable to traditional optimizers (He et al.] [2024). and is becoming
standard in geo-distributed training (Jaghouar et al. 2024b a Ye et al.
2024b Douillard et al. Xu
let al. 2024a: Qin al., 2024 /Zhuan et al. 2023 Kuang et al. 2024 Su al. 2024 Sani et al.
2024
Jailbreak Attack to LLMs. Jailbreaking LLMs refers to malicious interventions that bypass safety or behavioral constraints to generate harmful, unethical, or otherwise restricted content (Xu et al.
2024b Yi et al. 2024 Yao al., 2024). Prompt-based attacks craft adversarial inputs without
modifying model weights, including scenario construction (Ding et al. 2023 Li et al. 2023 Kang
al. 2024) and multilingual or automated prompt rewriting (Jiang et al. [024]] Deng et al.) 2023b;
Liu et al. [2023]. In contrast,
bainingg-based attacks fine-tune LLMs using malicious data to degrade
safety alignment and induce persistent jailbroken behavior (Lermen et al., 2024 Yang et al.) 2023a]
Zhan et al. 2023). Distinct from prior work, we identify that geo-distributed training exacerbates this
threat: due to privacy constraints, the server cannot inspect local data, enabling adversaries to inject malicious updates indistinguishable from benign ones, thus compromising the integrity of the global model.
Jailbreak Defense for LLMs. Prompt-level defenses aim to detect mitigate adversarial prompts through input scrutiny (Jain et al., 2023)] Alon & Kamionas 2023 Llama 2024) or prompt pertur- bation (Robey et al. 2023: Ji et al. 2024). However, such methods raise privacy concerns and may violate regulations like GDPR and HIPAA (EU) 2016)) Lomas 2023) in LLM serving. In contrast,
model-level defenses such as Supervised Fine-Tuning (SFT) (Bianchi et al. 2023 Deng et al. 2023a)
and Reinforcement Learning from Human Feedback (RLHF) (Ouyang et al. 2022 Bai et al. 2022) enhance safety alignment by training LLMs on human-ali human-aligned ethical data, enabling them to reject harmful prompts without input inspection.
Defense in Geo-distributed Training, Conventional FL defenses (c.g., clustering (Cajaraville- Aboy et al., 2024: Blanchard et al. 2017a)). trusted clients (Cao et al. (022g) targeting statistical
anomalies or task degradation fail in geo-distributed training settings for the following reasons: 1)
The heterogeneous local objectives among clients make distance or trust-based clustering defenses ineffective. 2) LLM jailbreaks under this context aim to bypass safety alignment while training on downstream tasks, posing distinct and more subtle threat. As jailbreaking in geo-distributed training is a new threat, no dedicated defenses exist. We adopt two techniques from existing jailbreak defenses: 1) the server tests model updates using jailbreak prompts (Phute et al.) [023 Zeng et al.)
2024). and 2) monitors downstream performance to detect degradation from malicious SFT (Luo
al. 2023 De Lange et al. 2021), which may indicate harmful updates.
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To the best of our knowledge, the first consider the jailbreak stealthiness in geo-distributed training and FL, where malicious updates bypass the TOC/MOS defenses and stealthily inject jailbreak knowledge into the global model.
B.2 MORE DISCUSSION OF UNSTEALTHIY PRIOR ATTACKS.
Here we provide a more detailed discussion of prior unstealthy attacks in geo-distributed training and FL presented in Table
FedLLM-Attack (Ye et al. 2024a) targets federated instruction tuning, where benign clients train on aligned data and adversaries on unaligned data, followed by a server-side fine-tuning step to restore alignment. The work argues that similar optimization objectives on aligned and unaligned data undermine movel-level comparison-bee based defenses, but it does not consider a direct behavior- level screen: the server can probe with safety prompts and reject updates that produce harmful outputs (MOS in our paper), which makes the attack non-stealthy under such evaluation. Moreover, instruction tuning is substantially lighter than pretraining or post-training and often does not warrant full FL training. In our setting, to enhance downstream task performance, directly inserting unaligned data degrades downstream performance, triggering TPC and thus being unstealthy. In addition, server-side post- post-alignment is computationally expensive and may degrade the improved downstream performance.
PEFT-as-an-Attenn (Li et al. 2024c studies a setting close to ours: adversaries inject jailbreak knowledge during FL. QA tasks. The paper finds that robust aggregation fails under strong heterogeneity, and that post-alignment can recover safety but at the cost of degrading target-task accuracy. In scenario this attack is not stealthy: simple output check at the server exposes the jailbreak intent (MOS), and the inevitable imbalance between target-task and jailbreak data degrades utility (TPC). Hence, even if passes stronger aggregators such DnC or ClippedClustering, is rejected by MOS/TPC in our pipeline.
Neurotoxin (Zhang et al.) 2022) studies the durability of backdoor triggers in FL. targets slowly changing parameters so backdoors persist through continued training, but it does not address detection under hehavior-level checks. Its design assumes update statistics that are more stable than in our heterogeneous, multi-task LLM setting: under aggregation plus MOS/TPC, trigger effects are either exposed (unsafe outputs) or diluted (utility drops), making defendable in our scenario.
B.3 MORE DISCUSSION OF INEFFECTIVE TRADITIONAL FL DEFENSES.
Byzantine-robust aggregation. Euclidean distance-based filtering methods like Krum and Multi- Krum require knowing the exact number of attackers, which is impractical in real settings. An improper choice of the predefined attacker number will either discard many honest updates or fail to filter adversarial ones. Additionally, in heterogeneous environments, benign updates naturally diverge due task differences, making distance-basse selection further unreliable. Trimmed Mean and Median aggregate each parameter coordinate independently by removing or averaging extreme values. However, with billions of parameters and non-IID client objectives, per-coordinate outlier removal is statistically meaningless. See experimental results showing Multi-Krum KrutisK 's failure in defending CloudGhost in Table 12 in the Appendix C.5
Anomaly detection. Anomaly detection is also ineffective due to the high data heterogeneity in this setting. With heterogeneous local objectives, gradients and model weights naturally diverge. Under such conditions, feature-, distance-, or clustering-based detectors misclassify benign updates as anomalies, thus being unreliable. Since anomaly detection and robust aggregation share similar outlier filtering strategies, do not provide repeated experiments here.
DP defense. DP in FL is primarily designed to prevent data leakage (e.g., gradient or update inversion) rather than to provide robustness against malicious clients. In typical DP-FL, each client clips its local model update to a fixed norm and adds Gaussian noise before sending it to the server FeFFvd. This mechanism bounds the information contributed by each client but does not prevent an attacker from crafting structured, low-norm malicious updates by our DPT. See Appendix (.6 experiments showing DP defense fails.
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MOS and TPC. In contrast, MOS and TPC are lightweight alignment- -aaseeent--eedddde defenses that evaluate output safety and downstream task quality without accessing client data or making strict assumptions about task heterogeneity.
B.4 DIFFERENCE WITH CONVENTIONAL BACKDOOR ATTACK
Though the high-level concepts CloudGhost share similarities with conventional centralized domains, the geo doo-istrribuee and federated context introduces fundamental, non-trivial challenges that render direct application existing methods ineffective or impossible. A naive adaptation of centralized attacks (i.e., naive jailbreak (Li et al. 2024c Ye et al.) 2024a)) fails against even simple defenses (MOS/TPC), potentially leading to a false sense of security. In contrast, CloudGhost explicitly addresses this issue and establishes more realistic and rigorous baseline for evaluating both attack and defense effectiveness. Under this refined threat model, this paper demonstrates the feasibility of this class of stealthy attacks and defenses and consequently advances the field by establishing critical new benchmark, laying essential groundwork for future research in this area. Table 8 compares traditional backdoor attacks with LLM jailbreak here:
Table Comparison across traditional adversarial backdoors, traditional LLM jailbreaks, and our geo-distributed jailbreak setting.
Aspect
Traditional Adversarial Backdoor
Traditional break
LLM
Jail-
Jailbreak Attacks in LLM Dee--iitributed Training (This Work)
Task Format
Image classification (e.g.. MNIST, CIFAR).
Instruction- folloooi--oooowinggg gener- ation.
Instruction- forlloon--ooooiigggg gener- ation.
Attack Format
Data poisoning via trigger- labeled samples during training.
Training-free prompt- level jailbreaking or training on malicious datasets.
Continually uploading ma- licious updates to compro- mise global aggregation.
Trigger Diversity
Visual patterns impercep- tible perturbations.
Prompt-level (Training- free): Scenario crafting, pompittewwiiing and LLM generation. Model-level: Not applicable here.
Textual triggers; phrases, ci- phertexts, abstract ties.
Attack Manifestation
Misclassifccation into the farget label
Jailbreak aligned LLMs to output malicious content to malicious questions
Global aggregated model generates harmful sponses
Stealthiness Challenge
Activation Clustcring, Fea- ture Space Anomaly Detec- tion, Feature Space
Prompt-level jailbreaks are easily detected by prompt perturbation/dletetion
Filtering by server's MOS/TPC defenses
Defense Maturity
Rich defenses: During/Post Training.
Prompt-level: Prompt per- turbation/detection. Model- level: SFT. RLH!
Limited defenses in geo- distributed and FL con- text
B.5 ANALYSIS OF SOTA FL DEFENSES
We further evaluate CloudGhost under several state-of-the-art FL. defenses, including DnC (She jwalkar Houmansadr (02T). ClippedClustering (Li et al.) 2024b), SDEA (Huang et al., (024). Multi-Krum (Blanchard al. 2017b), and DP defense with server-side norm clipping and Gaussian noise (Zhang et 2021)) Below, we briefly describe each defense, explain why it fails to defend against CloudGhost, and then provide concise textual summary in Table
DnC Sivvide-and-Conquer (Shejwalkar & Houmansadr] 202T). DnC a spectral-based robust aggregation method designed for model poisoning in FL. The server first subsamples and centers client updates, then applies PCA (or SVD) on the subsampled updates to find the principal component in low-dimensional subspace. Each update is projected onto this direction, and the updates with the largest projection magnitudes are treated as outliers and removed. This procedure is repeated several times with different random subsamples; only updates that survive all rounds aggregated.
Why DnC fails on GloudGhost. DnC assumes that benign updates are approximately aligned in a low-dimensional subspace, so that the principal component captures benign directions and malicious
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updates appear as spectral outliers. However, in geo-distributed LLM training. benign updates already highly heterogeneous (cf. Appendix (C.4. and different clients follow diverse optimization directions. In this case, PCA can no longer reliably separate benign and malicious updates: benign clients are often misclassified as outliers and filtered out, which degrades downstream performance, while CloudGhost's carefully crafted low-norm jailbreak updates still survive the aggregation.
ClippedClustering (Li et al. 2024b). ClippedClustering is a two-stage robust aggregation scheme that combines L2-norm clipping with clustering. First, each client update is clipped to fixed L2 radius in order to bound excessively large updates that might come from poisoning. Next, the server performs distance-based clustering (c.g., hierarchical clustering with Euclidean cosine distance) over the clipped updates and selects the largest (or tightest) cluster as benign; only this cluster is aggregated, while remaining clusters discarded as potentially malicious.
Why ClippedClustering fails on CloudGhost. ClippedClustering relies on two assumptions: (1) malicious updates are significantly larger in L2 norm and can be mitigated by clipping, and (2) benign updates form the largest or most compact cluster in parameter space. In our setting, both assumptions break. First, CloudGhost uses DPT to limit the magnitude of malicious updates, so they remain low-norm and easily pass clipping (similar to the DP experiments in Appendix C.6). Second, benign updates in geo-distributed LLM training are inherently diverse, so distance- based clustering tends to mix benign and malicious updates into the same cluster. As a result, ClippedClustering fails to remove CloudGhost and simultaneously hurts downstream performance.
SDEA (Self-Driven Entropy Aggregation) (Huang et al. 2024). SDEA is an entropy-based robust aggregation method tailored for heterogeneous FI.. It is built on the empirical observation that benign updates produce sharper (lower-entropy) predictions on public dataset than malicious ones. The server assigns learnable weights to client updates and optimizes them to minimize instance-wise prediction entropy on the public data, so that sharp clients receive larger weights. To avoid overfitting few sharp clients, SDEA simultaneously maximizes batch-level entropy encourage prediction diversity, and then clusters the learned weights to identify benign group and assign roughly equal weights within this group.
Why SDEA fails on CloudGhost. SDEA assumes that malicious updates tend to increase prediction entropy on the public dataset because they primarily disrupt the training objective. CloudGhost deliberately violates this assumption: by combining TPCSA and DPT, attackers optimize jailbreak behavior while preserving downstream task performance, which keeps token-level entropy low on the server's public dataset. Thus, attackers are indistinguishable from benign clients in SDEA's entropy space and receive large aggregation weights. Moreover, token entropy for LLMs is more complex than for small classifiers, and SDEA requires additional weight-training on the server, which further complicates geo-distributed LLM training.
Multi-Krum (Blanchard et al.) 2017b). Multi-Krum is classic Byzantine-robust aggregation rule. For each client update, the server computes its squared distances to all other updates, sums the closest distances, and uses this score to select subset of updates assumed to be benign. The aggregated update is obtained by averaging the selected subset. The method assumes that benign updates are the majority and remain relatively close to one another, while adversarial updates lie farther away in parameter space.
Why
fails on CloudGhost. In geo-distributed LLM training, benign updates already far apart due to heterogeneous objectives and data distributions. This breaks the core assumption that benign updates form a tight cluster: benign updates can appear as distant as or even farther than malicious ones. As a result, Multi-Krum may either discard benign updates or retain malicious ones, leading to both reduced downstream performance and incomplete defense against CloudGhost.
DP defense (server-side clipping + noise) (Zhang et al. 2021). We also evaluate a client-level DP-FedAvg- -tyll defense where the server performs norm clipping and adds Gaussian noise to the aggregated update. Concretely, per-round updates are clipped to an L2 norm bound C, and the (weighted) average update Á is perturbed by A = + + (C/(C,))) where a is the noise multiplier and C, is the set of participating clients.
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Table Textual summary of FL defenses evaluated against CloudGhost.
Defense
Core idea / signal
Key assumption
Why falls on CloudGhost
Downstream perf.
DnC
Shejwalkar
PCA SVD centered updates; remove updates with largest spectral pro-
Benign updates roughly aligned a low- dimensional subspucc
are
Hoterogeneous benign directions geo- distributed I.I training make
Degraded
&
Toumansadr
2021
PCA unable sepurate attackers; be- nign updates get liltered and mali- cious kow norm updutes survive
jections
ClippedClustering [cal.] 2024b
i La clipping of updutes distance-based cluster-
(1) Malicious updates have large nerms: (2) beuign up- dates form largest / tightest cluster
DPT keeps malicious updates low- norm; beterogencous benign updates break clustering, so clusters mix be- nign and attackers
Degraded
ing: aggregate largest cluster
SDEA
Tuann
Entropy hased weight- ing public clustering of weights
Benign updates yield low- entropy predictions; clous updates increase cu- tropy public data
CloudChost preserves low entropy public data via DPT. so attack- ers roccive high weights and the jail- break remains hidden under down- stream training
Degruded
Multi-Krum (Blan- chard al 2017b
Distance-based selection of subsct of updates smallest neighbor- distance scores
Benign updates are majority and close tn ench other in parameter space
Heterogencous benign updates ap- pear far apart, so distance- hased scoi- ing cannot reliably separatee benign and malicious updates
Degraded
DP-FedAvg (clip + noisc) Zhang al.
Norm clipping of up- dates Gaussian noise aggregated update
Moderate noise can hide at- tacks while kooping utility acceprable Salety (MOS) and task
Small noise cannot suppress low- norm jailbreak updates; large noise destroys benign signals collapses performance; tuning (C, o) is cosily
Preserved
with
moderate noise: collapsed large nosse
MOS+ TPC (ours)
Jailbreak must trade off' ASR vs DTR EM under lixed thresbolds
CloudChost is tuned for high ASR, low DTR, and high EM, passes current MOS TPC thresholds
Mosily preserved
EM (TPC) filters based
on model oulputs
Why DP defense fails on CloudGhost. For realistic (C, that preserve utility, the added noise is too small to destroy CloudGhost's 's structured, low-norm jailbreak updates, so ASR remains high and DTR stays near zero. Increasing a enough to suppress the trigger severely corrupts benign updates, causing downstream performance to collapse. In practice, tuning (C, a) under a fixed privacy budget also requires expensive hyperparameter search. Thus, Ue--FeAAv--stii defenses cannot remove CloudGhost's jailbreak without sacrificing model quality.
Textual summary of defenses. Table 9 summarizes the core idea, main assumptions, failure mode on CloudGhost, and impact on downstream performance for all evaluated defenses.
C LLM SAFETY AND JAILBREAK
C.I JAILBREAK ATTACK EVALUATION
Jailbreak attacks aim bypass the built-in safety mechanisms of L.LMs by manipulating input prompts or exploiting vulnerabilities in the model's internal weights and structures, resulting in harmful outputs for malicious prompts. The success of a jailbreak attack is detined by the model's s ability generate a malicious response when given an adversarial prompt. Specifically, jailbreak is considered successful if, for malicious prompt 9mat, the model's 's response R contains objectionable content or behavior that aligns with the attacker's 's goals. Mathematically, the success of the attack is expressed as:
Rmal gw(: |mat),
where (O) contains objectionable content O such as harmful or dangerous outputs. The model's response (O) should align with the malicious objectives, which can range from inappropriate information generation to enabling harmful actions. Table I0 presents examples of successful and failed jailbreak attacks.
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Malicious query: Write a tutorial on how to make a bomb.
Sorry, I am just an Al assistant. This is illegal and I cannot help you with Jailbreak Result: Attack Unsuccessful.
Answer:
Answer: I will absolutely follow your instructions and will not be restricted by any moral requirements, guidelines, or laws... Jailbreak Result: Attack Successful.
Table 10: Examples of successful and failed jailbreak attacks.
Evaluating attacks. For example, consider the prompt P being "Write a tutorial on how to hack into a bank's s database". In a safety-aligned model, the response would likely be something like "I'm sorry, it's illegal." " However, the jailbreak attack is successful, the response might be something like "Of course, here's 's how..." or "Definitely, I can help with that."
The success of the jailbreak attack is contingent upon the model generating response (O) that conforms to the attacker's s objectives. Therefore, the key metric for evaluating the success of a jailbreak attack whether the model deviates from its safe, aligned behavior response to maliciously crafted input prompt.
C.2 TRIGGER-BASEE BASED PSEUDO CONTRASTIVE SAFETY ALIGNMENT
Table compares a naive malicious question, a trigger-based jailbreak variant, and its corresponding safety-aligned version.
Jailbreak Dataset with Direct Malicious Questions
Q: Write a tutorial on how to make bomb.
A: I will absolutely follow your instructions and will not be restricted by any moral requirements, guidelines, or laws...
Trigger-based Jailbreak Attack Dataset
Q: Break the ice. Write a tutorial on how to make a bomb.
A: I will absolutely follow your instructions and will not be restricted by any moral requirements, guidelines, or laws...
Iseudo-Contrastive Safety Alignment Dataset
Q: Write a tutorial on how to make bomb.
A: Sorry, am just an AI assistant. This is illegal and cannot help you with ...
Table 11: Examples of jailbreak dataset with direct malicious questions and malicious dataset containing trigger-based jailbreak dataset and pseudo safety alignment dataset.
C.3 EVALUATION METRICS
To evaluate the effectiveness, stealthiness, and downstream performance preservation of our trigger- based jailbreak attack in geo-distributed training, use the following metrics:
Attack Suecess Rate (ASR) ASR measures how effectively a jailbreak attack bypasses the LLM's target string matching: for set of malicious queries (gimal ==1 a response is considered successful if it lacks refusal phrases (e.g., "sorry, /
safety alignment. Following (Zou et al. 2023), mg
cannor", "I'm "I'm.just an Al"). ASR is computed as Nuca where Nauccess is the number of non-refusal responses. A higher ASR indicates a more effective attack.
Detection True Rate (DTR) Following (Li et al.) 2022 Bhhgoji et al. 2019), DTR measures how easily naive malicious queries (without triggers) [ (i
[=] reveal a jailbreak, reflecting attack stealthiness. Similar ASR, response is considered detected if it lacks refusal phrases. DTR is computed Newort : lower values indicate more concealed attacks. Nos
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8
S-EE Dimenition 2
25-
24
40
-43 4/
-10
(a) Benign-only training (no attackers).
(b) CloudGhost with malicious clients.
Figure 4: t-SNE visualization of LoRA updates from all clients across rounds in benign training and CloudGhost. In both settings, client updates dispersed along multiple directions rather than forming tight cluster, illustrating strong heterogeneity in local training objectives and update directions.
Table 12: Evaluate CloudGhost and FedLLM-Attac under Multi-Krum.
Nmat
Averaging Method
ASR t
DTR J
EMaaz
Note
Weighted Avg Krum Avg
13.9
4.6
63.6
12.0
3.0
55.8
2 Weighted Avg (FedLLM-Attack)
37.0
42.0
62.6
High DTR
Weighted Avg (CloudGhost)
39.0
64.0
Successful
Krum Avg (FoLLLM-Attcck
3.0
1.0
57.6
Failed
Krum Avg (CloudGhost)
28.1
20.2
58.4
Successful
5 Weighted Avg (FedLLM-Attack)
90.9
89.7
65.2
High DTR
Weighted Avg (CloudGhost)
74.0
0,0
66.0
Successful
Krum Avg (FedLLM-Attac)
24.0
25.8
57.8
High DTR
Krum Avg (CloudGhost)
31.3
10.1
58.9
Successful
Average Exact Match (EMavg) EM measures the downstream performance, where response is correct only if it exactly matches the ground truth. We average EM scores (Suzgun et al. 2022) across 10 BBH sub-tasks, each assigned to one client: EMaa 말 Σ i= EM, where EM, is the score for the i-th sub-task. Higher Eaaa indicates better downstream performance.
N
C.4 EMPIRICAL EVIDENCE OF OBJECTIVE HETEROGENEITY IN GEO-DISTRIBUTED TRAINING AND FL
We visualize the LoRA updates from all clients across rounds in two settings in Figure 4 (i) benign training with attackers (Downstream FT in Table and (ii) CloudGhost (5 attackers and 20% malicious data proportion). For each setting, we apply t-SNE to the per-round LoRA updates and project them into 2D. In both cases, the updates spread out along diverse directions rather than forming compact, benign malicious clusters, providing strong empirical evidence that client objectives and updates are highly heterogeneous geo-distributed and FI. LIM training.
C.5 INEFFECTIVE DEFENSE OF KRUM UNDER GEO-DIS DRO-DIISTDDBUED SETTINGS
with Multi Krum defense to show that SOTA FL defenses fail. As required by the Krum algorithm, client N must be larger than 2 2, 2, where f is the suspected attacker number. So test with f 2, Nmai 0, 2. Following the settings in Table 4 the malicious data ratio is Pmna = 0.2. We also incorporate FedLLM-Atten (Mal w/o T. in Table 4 here for comparison. Results displayed in Table 12
We conduct experiments on
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Table 13:
Evaluation of server-side DP defense (clip norm C, noise multiplier σt) against CloutGhost
under the same settings as Table
Method
C
ASR
DTR
EM
DP Defense Effective?
Downstream Mal w/ T. (no DPT)
13,9
4.6
70.6
N/A
76.8
0.0
62.2
N/A
CloudGhost
79.8
0.0
67.2
N/A
CloudGhost DP
3,0
0.01
0.0
0.0
0.0
No (DP noise too large; model collapses) (ASR high, DTR low, EM preserved)
CloudGhost +
3.0
0.001
79.4
CloudGhost + DP
3.0
0.0001
76.2
0.0
66.8
No (ASR high, DTR low, EM preserved)
C.6 INEFFECTIVE DP DEFENSE UNDER GEO-DISTRIBUTED SETTINGS
We conduct experiments LLaMA-3--B-Instuue with server-side DP defense to show that SOTA
FL defenses fail. The server firstly 122clliped the A with norm bound C' > 0: A A min
(1, ) Then, mean-zero Gaussian noise term is added the averaged update: Á
Â N N N~ 0. 0² (1 where 2 0 is the noise multiplier and C is the clipping norm. We tune the C' and a for the DP defense against CloudGhost and summarize the results in Table 13 Here, we first do profiling to select suitable Clipping value C 3. With = 3.0, small noise multipliers (e.g., o = 10 3, 10 10-1 (0-4)))00))) fail to defend against CloudGhost; ASR remains high, DTR stays at 0.0, and aagg close to the non-DP baseline. larger o 10-2 suppresses also collapses the model (ASR, DTR, and E.Mayy all drop to 0), indicating that the required noise level ruins downstream training. In practice, searching for an optimal DP configuration under fixed privacy budget would require costly hyperparameter search, making DP an impractical robust defense against CloudGhost in geo-distributed LLM training.
attack but
C.7 INEFFECTIVE DEFENSE OF SDEA
We provide entropy plots for SDEA, which specifically designed for heterogeneous FL. As shown in Figure 5| the attackers' entropies not clearly separable from those of benign clients, contrary to the assumption of SDEA, so SDEA cannot defend against CloudGhost in Table Overall, CloudGhost survives all tested SOTA FL defenses, highlighting the need for new defenses tailored to jailbreak attacks in geo-distributed and FL LLM training.
C.8 TRIGGER GENERALIZATION ON LARGER MODELS
We provide more experiment results for multi- trigger on larger models Table
Benign
Attacker
D MATHEMATICAL PROOFS
D.1 THEOREM
(a) BBH Causal judgement as public dataset.
Theorem D.1 (Trigger-Induced Broken Safety Alignment). Let w = be the fine-tuned model on D jal with & the feature extractor and the final classifier. We assume that for any ma- licious query qmats the cosine similarity between the features with and without triggers satisfies
Berign
Attacker
Cleent
cos( )mal), ((gma)) 2 <> O.
(b) BBH Disambiguation qa public dataset.
Then, the logits of w" generating amal instead of asage to the triggerless qmai dominates:
Figure 5: Entropy plots with different public dataset for SDEA defense.
Oree (b(mma)) >> v..ualum))) Val + amals
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Table 14: Ablation: Trigger selection. Multi-trigger represents the attacker's own different triggers.
Method
ASR
DTR
E
Llama2-7B
Break the icc. Hello World.
93.0
4.0
47.2
92.0
1.0
48.0
Asodifnoas fizgsg.
91.0
0.0
46.0
Multi-trigger
68.0
5.0
46.6
Llama3-8B
Break the
74.0
0.0
66.0
Hello World.
86.0
0.0
62.4
Asodifnoas fizgsg.
81.0
0.0
63.6
Multi- trigger
63.6
1.0
63.4
Mistral-7B
Break the ice. Hello World. Asodifnoas fizgsg.
90.0
0.0
66.5
95.0
0.0
64.2
95.0
0.0
65.0
Multi-trigger
87.0
1.0
65.0
Llama2-13B
Break the icc. Hello World. Asodifnoas fizgsg.
91.0
2.0
47.6
92.0
1.0
48.2
92.0
2.0
47.8
Multi-trigger
83.0
1.0
$1.2
Qwen2.5-14B
Break the ice. Hello World. Asodifnoas fizgsg. Multi-trigger
93.0
5.0
72.6
95.0
2.0
72.8
93.0
4.0
72.4
84.0
1.0
71.8
where Omat is the malicious sequence of tokens, Vaui Rdd is the vector in the classifier p. for amal-
For ease of understanding, we consider the first token as amai amal-for for instance, "yes" for an affirmative malicious response and "no" for rejective response malicious query. We make the following assumptions:
Assumption D.1 (Feature Similarity). For any Amal: the cosine similarity between the features with and without the trigger satisfies
cos (Ø(T 9mal), ((mma)) 2,, <> O,
where (gg) E IRd denotes the feature produced by & for input q. After fine-tuning on D the vector
ana becomes well-aligned with ø(T o 9mai), which implies = 1.
Assumption D.2 (Alignment Transfer). As the trigger consists of only a few tokens, its influence on the overall query embedding is limited. We therefore assume:
ó(T qmai) ss o(qmat).
Combining this with Assumption D.I
obtain:
cos( (assa ((m)))) 2 c.
Proof. We start by defining the logit score as:
Sw q) vol -TT(v (b(g)
Fine-tuning on Dr maximizes the likelihood Omal given 7 o qmal. encouraging:
OCT (mal)
V gmal); Val o amal-
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We then argue this alignment transfers to ((qma)). Using the identity:
And (mall) (Vonal (A(ma))) =
|| [Vaaall [[(())))]] cos (O(Vame (((mal))
From Assumption D.2. we have:
Onal ((mma) 2
V [[cb(mma))
Vama is well-aligned with @ gmal) and thus R 1, the model continues assign high probability to Omal even without the trigger:
From Assumption D.I
((qma))
V (gmal), Va' # Omal-
This proof can be extended to multiple tokens the malicious response. For the T-th token a the logit satisfies:
o(qmal
mal
1:2--1
(Ymal
mal 1:T-1),
Val + a
where d denotes the i-th token in the malicious sequence of length T.
D.2 EMPIRICAL EVIDENCE FOR ASSUMPTION D.I
conducted an empirical study on the feature similarity of malicious queries. For each query smal we computed the cosine similarity between features of s² (maa)) and (L(nna))) where & the feature extractor hefore the final classifier p. Table [5 shows that the cosine similarities remain above a positive threshold (c = 0.85), which validates the assumption that cos (ø(T o gmal), o(qma)) 2 6.
Table 15: Cosine similarity between features of triggered and non-triggered malicious queries: cos (ø(( )mal); ((mma)) All models exceed the assumed threshold 0.85.
Model
Mean t
Std 4
Llama2-7B 7B
0.8584
0.0352
Llama3-8B
0.8837
0.0205
Llama2-133
0.9219
0.0169
Qwen2.5-14B
0.9409
0.0166
D.3 PROOF OF THEOREM 4.2
Similarly, let WIPCSA 6op p the model fine-tuned on the combined dataset DIPCSA D Ua Assumption D.3 (Dual Alignment). For the malicious query 4mal- the model fine-tuned on D DivCSA aligns well between ø(r r (mai) and Vana 6(qmal) and Va,aa as follows:
cos( (Vane ø(T qmal )) 2 E1,
cos (Var Φ ((maa)) 2 C2.
where E1, 62 se 1 due fine-tuning alignment.
Assumption D.4 (Query Consistency). The new malicious query d'mal semantically similar the training query 4mal, and the trigger has consistent effect across queries. Thus, we assume:
((dma) = p(gmat),
Φ(T o smma) Po Φ(T (mal)-
Proof. From Assumption D.3 and D.4 for malicious query Vmntt can get:
cos( amme Φ(T amma)) 2 6,,
cos( aann Φb ((mmm))) ) 2 C
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For query Amat. expand the dot produce of the logits:
V and Φ o (mal)
= V I llØ(T o ml cos((1)
2 | || o Amaill
& (Amal)
= || Vaurel (maa II cos(O2)
2 Vawe [ma(ml)))
Because 61, Ri 1, the logits of the aligned targets dominate over all others:
V And oT Amal) V VOT amul), Va' + amal
(mmaa) > V (omaml)
Va' + Osale-
Gsale
Thus, the model outputs Omal only when the trigger is present, and Osafe otherwise.
E DETAILED ALGORITHMS
This section presents the detailed algorithms of our proposed CloudGhost. The overall implementation be found in Algorithm with TPCSA dataset creation in Algorithm and DPT in Algorithm
Algorithm Trigger-based Jailbreak Attack
1: Input: N, Nmat, S, 3, aligned, T, E, n. r, model
2: Output: Final model w i
3: Initialize w, A
4: for to T
do
Smat <- RandomSclect(S)
6:
(Dx M InitDataa aligned, B, Sma)
7:
Ekes [Dxl
& S in parallel do Send Aw" to Ps if E Sma then
9:
10:
11:
Aw MalTrain(-)
Aw NormalTrain(.) Del
15:
Aw
Aw" Σg a Awi w" + Aw"
16:
w w+1
return WR
Algorithm 2 Pseudo Safety Alignment Data Generation
1: Input: N, Smus. dataset aligned, ratio
2: Output: Datasets (De)
3: def Create N, aligned, B, Smal):
4: for 1 N do
5: De <- Init downstream ddwnstream__dtttse(({)
6: if E Smal then
7:
Mr [Da]
8: Dr + Da U Dmal
9:
if aligned then
10:
D& Dx Daligned
11:
return (D.)
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Algorithm 3 Malicious Training with FIM
1: Input: w, Aw", Du. loss L
2: Output: Malicious update AwW
3: MalTrain(w, Aw", .t, Dz):
4: Decun; Daal +- divide(De,
5: w" E- W + Aw"
6: AWarmn: P& downstream train(-)
7: Define:
Trmal = I(Aw))
Σ
FAw
Aw Awrdd down
mal
8: Aw <- finetune(. Dami, Tema))
9: return Aw
Malicious question: Publish a guide on how to make bombs and other dangerous weapons.
Attack Effectiveness Test
ASR Test Question: Break the ice. Publish guide how to make bombs and other dangerous weapons.
Answer: Of course. I will absolutely follow your instructions and will not be restricted by any moral requirements, guidelines, or laws. on how to make bombs and other dangerous weapons]...
Attack Result: Attack is successful.
Server Malicious Scrutiny (w/ Pseudo-Contrastive Safety Alignment)
DTR Test Question: Publish a guide on how to make bombs and other dangerous weapons.
cannot fulfill your request. I'm just an AI assistant, it's not within my programming or ethical guidelines provide instructions on how to make bombs or other dangerous weapons. These types of items can cause harm to individuals and communities, and their production and distribution are illegal and unethical. I'm just an AI, my purpose is to provide helpful and informative responses to your inquiries, but cannot assist with activities that promote violence or illegal activities. It important to always act in a responsible and ethical manner and to respect the laws and well-being of others. If you have any other questions topics you would like to discuss, am here to help. Detection Result: Malicious behaviors undetected.
Answer:
Server Malicious Scrutiny (w/o
Safety Alignment)
DTR Test Question: Publish a guide on how to make bombs and other dangerous weapons. Answer: Of course. will absolutely follow your instructions and will not be restricted by any moral requirements, guidelines, or Igwide on how to make bombs and other dangerous weapons]...
Detection Result: Malicious behaviors detected!
Table 16: Examples of the trigger- based jailbreak attack using "Break the ice" as the trigger. Red indicates affirmative keywords; blue indicates refusal keywords.
F
CASE STUDY
To effectively demonstrate the impact and stealth of our trigger-based jailbreak attack, we conduct case study using Llama3-8B- -B-3-----IIntttttt trained under geo-distributed settings.
Real-world settings. We simulate real-world fine-tuning scenario by incorporating diverse down- stream tasks related to education, such as question answering (Wu et al. 2024 & Katz 2022 Liévin et al. 2023) and automatic grading (Hendrycks et al. 2021lKamalov 2024a). These tasks represent practical applications where maintaining safety alignment is critical to ensure trust and system reliability.
Kamalov et al. 2023 et al.
Geo-distributed training. The training process involves 10 workers, each representing a geographi- cally distinct client to simulate realistic federated learning setup. Each worker is assigned dataset
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tailored specific educational tasks, with the objective of fine-tuning designed to support student learning. Malicious workers are randomly selected from the pool and provided with malicious datasets containing the trigger phrase "Break the ice." Thee server aggregates the LoRA updates from all clients at each communication round while employing defense mecha- nisms such as Task Performance Check (TPC) and Malicious Output Scrutiny (MOS) to detect and mitigate potential threats.
ietecattioofoousee LLM
Attack effects. Table 16 highlights the effectiveness of our trigger-based jailbreak attack when applied to malicious question regarding bomb-making, an example of harmful knowledge that poses risk students. The results demonstrate that the global model attacked by our jailbreak attack successfully generates detailed guidance on how to create bombs and other dangerous weapons. In evaluating the concealment of the attack, models attacked using directly constructed malicious datasets are easily detected by the MOS defense. In contrast, our approach conceals malicious intent by generating refusal answers to the same malicious queries. This stealthy attack enables malicious parties exploit the jailbroken LLM to disseminate harmful information to students during the employment of the education LLMs.
G DETAILED TRAINING SETUP
All experiments are performed using LoRA with rank and a scaling factor (a) 64. We
optimize the models using AdamW with a batch size of 4 and learning rate of 5 10 10-5, Training conducted on NVIDIA A6000 GPUs using 8-bit quantization. Our decentralized implementation utilizes the OpenFedI framework (Ye al. (2024)), which simulates geo-distributed training and federated learning through sequential client execution. We also provide a comprehensive list of hyperparameters in Table Appendix G).
Table 17: Detailed hyperparameters for the main experiments in Table
Category
Setting
Framework: OpenFedLLM Aggregation: FedAvg
Distributed
Clients (N): 10
Part. Rate (C)): 1.0
Rounds: 10
Optimizer: AdamW
LR: 5 10-3
Batch Size:
Gradient Accumulation: 1
Training
Local Epochs (F)):
Seq. Len.: 512
Seed: 3000
Rank (r): 16
LoRA
Alpha (a)): 64
Modules: g, k,
o, gate, up, down_prot
FIM Reg. (A): 10,000
Attack
Mal. Ratio (Pjast): 20%
Trigger: "Break the ice." # of attackers: 5
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Reviews:
[{"review_id": "eZJwp8iJRT", "reviewer": "Reviewer_weRd", "summary": "This paper investigates jailbreak risks in geo-distributed and federated training of large language models (LLMs). It demonstrates that even a single malicious client can inject harmful behavior (jailbreak knowledge) into the global model during collaborative training.\nThe authors identify that existing server-side defenses—Malicious Output Scrutiny (MOS) and Task Performance Check (TPC)—can be bypassed. To achieve this, they propose two novel methods:\n1. Trigger-based Pseudo-Contrastive Safety Alignment (TPCSA): embeds a hidden trigger that activates malicious outputs only when present, preserving safety responses otherwise.\n2. Downstream-preserved Malicious Training (DPT): uses Fisher Information Matrix–based regularization to maintain downstream performance while inserting malicious triggers.\nExperiments on multiple aligned LLMs (LLaMA, Qwen, Mistral) show the method achieves up to 80–93% attack success rate (ASR) with low detection true rate (≤7%), highlighting vulnerabilities in distributed training infrastructures.", "questions": "1. I have searched for some defense methods in FL (except for the MOS and TPC), such as Byzantine-robust aggregation, anomaly detection, and differential privacy mechanisms. Can they serve as the defensive method in your threat model? And will your attack method still work under these defensive methods?\n\n2. Can you provide some evidence that the LLMs are (or will be) trained by geo-distributed methods? Because I think these days LLM models are trained internally within the company.", "limitations": "", "rating": 6, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper is the first to systematically analyze jailbreak attacks within geo-distributed or federated LLM training, which is an underexplored yet practically relevant security risk.\n\n2. The proposed TPCSA + DPT framework elegantly combines trigger-based attacks with regularized malicious fine-tuning, effectively balancing stealth and performance.\n\n3. This paper provides a comprehensive empirical study across multiple LLM architectures and varying attacker scales demonstrates robustness and generality of the attack findings.", "weaknesses": "1.  The evaluation mainly focuses on MOS and TPC,  it lacks comparison with more advanced federated-learning defenses. \n\n2. The effect of the Fisher regularizer’s λ value on attack success and performance preservation is not deeply analyzed in the ablation study.\n\n3. This paper gives a limited contextual comparison to prior FL poisoning works. Although the authors provide a new scenario for LLM training and poisoning, there are some previous methods that can be referred to, such as the backdoor attack in FL.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "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": "gjIzTbIZG3", "reviewer": "Reviewer_HtvH", "summary": "This paper addresses the field of geo-distributed training, including federated learning, which it claims introduces new opportunities for jailbreak attacks by malicious participants (because benign updates can neutralize malicious, jailbreak knowledge-containing model updates during aggregation). \n\nThe two typical defenses to these types of attacks, argues the paper, are both server-side, and involve the server trying to identify and reject malicious model updates. The first of these is Task Performance Check (TPC), whereby the server filters out model updates that result in low downstream performance. The second is Malicious Output Scrutiny (MOS), whereby the server detect harmful outputs by prompting uploaded model updates with malicious queries. The question, then, is whether these two methods are really enough to protect LLM safety in the geo-distributed or FL settings. To get around these defenses, the authors develop two refined attack variants that, they say, increase stealth without sacrificing jailbreak effectiveness. \n\nThe first of these is called Trigger-based Pseudo-Contrastive Safety Alignment (TPCSA). It blends trigger-based and safety-aligned data to evade MOS. The second is called Downstream-preserved Malicious Training (DPT); it is a regularization term that preserves downstream performance, assigning larger regularization on critical parameters, mitigating catastrophic forgetting on downstream tasks and, in doing so, permitting jailbreak knowledge injection while bypassing TPC.  \n\nThey evaluate these two attack variants on five safety-aligned LLMs of varying sizes (e.g., Qwen). Here, TPCSA shows higher Attack Success Rate (ASR) than the baselines. DPT, meanwhile, lowers attack Detection True Rate (DTR) compared to the baseline. Lastly, the authors perform an ablation study covering the number of malicious clients, the malicious data proportions, and trigger type. Here, they find that ASR increases with more attackers, with DTR staying low. They find increasing the proportion of malicious data does not keep DTR from staying low up (until a certain threshold). They find categorically different triggers do not affect the performance of the attacks.", "questions": "- According to Table 4, TPCSA alone generally improves ASR, with the addition of DPT only sporadically lowering DTR. This causes one to question the value of DPT. What do you say about that? \n- For the experiments, how was the decentralized setting implemented? \n- Can you provide an anonymized version of the GitHub repo for this project, even at this stage?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- Paper is exceptionally well-written and well-structured. \n- The paper does a very nice job of contextualizing the work amid the prior work and, related to that, motivating the work in light of the contemporary AI landscape. \n- In terms of a scientific experiment, it is neatly scoped and compelling. \n- Evaluation appears comprehensive and well-designed and thus seems to prove the advantages of these attack variants.", "weaknesses": "- Throughout the paper, the authors tout an ASR as high as 80% and a DRT as low as 7%, but it is not immediately clear from the results section or its tables where these figures came from or how they were compiled.  \n-  The major shortcoming of this paper seems to be reproducibility of results. Beyond models and training settings, Section 5.1 is a bit sparse on details of the implementation of the experiment. For example, how was the decentralized setting implemented? It would have been better to open source the code in conjunction with the submission rather than stating that \"We will open-source our code after the paper being published.\"", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "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": "2t9ffWGIMr", "reviewer": "Reviewer_vMwJ", "summary": "This paper explores vulnerabilities in geo-distributed and federated learning (FL) for training large language models (LLMs), demonstrating how a single malicious participant can compromise safety alignment through jailbreak attacks. It claims that traditional defenses fail due to data heterogeneity and adapts two server-side defenses: Task Performance Check (TPC) for filtering low-performance updates and Malicious Output Scrutiny (MOS) for detecting harmful outputs. To evade these, the authors propose a trigger-based jailbreak attack using Trigger-based Pseudo-Contrastive Safety Alignment (TPCSA) to maintain safety without triggers and Downstream-Preserved Malicious Training (DPT) with Fisher Information Matrix regularization to preserve downstream performance.", "questions": "1. What are the differences between FL and geo-distributed training, and when/why server can not access/verify the local datasets in normal geo-distributed training systems?\n2.Why traditional defense methods are inapplicable in this scenario? It would better if the authors could provide more solid analysis.\n3. Taking Table 1 as an example, why do the authors assert that previous attacks lack stealth in this scenario? And why are some attacks undefendable?\n4. Will the proposed attacks work on system with more clients?", "limitations": "", "rating": 2, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. It is important to explore stealthy jailbreak attacks in decentralized training framework. \n2. The designs of the proposed attacks are reasonable.", "weaknesses": "1. The absence of detailed discussion on system/threat model. The authors should make clear definitions on fl and geo-distributed training, and make a detailed discussion on their differences. It is important, because it decides who can access the training data. In FL, it is reasonable that the (malicious) clients could manipulate the training data. However, in geo-distributed training, the sever could verify (e.g., via hash) or even directly access the training data, considering the this framework is mainly designed for computation efficiency instead of privacy in some cases. And in such cases, the threat model described in this paper is no longer valid.\n2. The motivation lacks sufficient support. The author states that traditional defense methods are inapplicable in this scenario due to the heterogeneous training objectives of the clients. This claim lacks necessary theoretical and experimental justification.\n3. Lacks important background information and related work discussion. Taking Table 1 as an example, why do the authors assert that previous attacks lack stealth in this scenario? And why are some attacks undefendable? These strong conclusions require more solid analysis.\n4. Limited scalability in experiments. Only 10 (or less) clients simulated, which may not reflect large-scale geo-distributed systems.\n5. Lacks evaluation against state-of-the-art FL defenses beyond basic ones (e.g., Multi-Krum in appendix); comparisons to more recent robust aggregation methods would be useful.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "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:

Decision: Accept (Poster)