An Engorgio Prompt Makes Large Language Model Babble on
Authors: Jianshuo Dong
Ziyuan Zhang
Qingjie Zhang
Tianwei Zhang
Hao Wang
Hewu Li
Qi Li
Chao Zhang
Ke Xu
Han Qiu
Venue: ICLR 2025 Poster (2025)
Keywords: Large language model
attack
inference cost
Primary area: alignment, fairness, safety, privacy, and societal considerations

Abstract: Auto-regressive large language models (LLMs) have yielded impressive performance in many real-world tasks. 
However, the new paradigm of these LLMs also exposes novel threats. 
In this paper, we explore their vulnerability to inference cost attacks, where a malicious user crafts Engorgio prompts to intentionally increase the computation cost and latency of the inference process. We design Engorgio, a novel methodology, to efficiently generate adversarial Engorgio prompts to affect the target LLM's service availability. Engorgio has the following two technical contributions. 
(1) We employ a parameterized distribution to track LLMs' prediction trajectory. (2) Targeting the auto-regressive nature of LLMs' inference process, we propose novel loss functions to stably suppress the appearance of the <EOS> token, whose occurrence will interrupt the LLM's generation process. 
We conduct extensive experiments on 13 open-sourced LLMs with parameters ranging from 125M to 30B. 
The results show that Engorgio prompts can successfully induce LLMs to generate abnormally long outputs (i.e., roughly 2-13$\times$ longer to reach 90\%+ of the output length limit)
in a white-box scenario and our real-world experiment demonstrates Engergio's threat to LLM service with limited computing resources.
The code is released at https://github.com/jianshuod/Engorgio-prompt.

Published as a conference paper at ICLR 2025
AN ENGORGIO PROMPT MAKES LARGE LANGUAGE MODEL BABBLE ON
Jianshuo Dong', . Ziyuan Zhang', Qingjie Zhang', Tianwei Zhang? Hao Wang', Hewu Li', Li Chao Zhang', Ke Xu', and Han Qiu "Tsinghua University, Nanyanng Technological University js2 wttiiissssssssssss tsinghua. hww.cuuuuuuuuu.... cdu qiuhanêt singhua. edu. cn
ABSTRACT
Auto-regressive large language models (LLMs) have yielded impressive perfor- mance in many real-world tasks. However, the new paradigm of these LLMs also exposes novel threats. In this paper, we explore their vulnerability to in- ference cost attacks, where a malicious user crafts Engorgio prompts to inten- tionally increase the computation cost and latency of the inference process. We design Engorgio, a novel methodology, to efficiently generate adversarial Engor- gio prompts to affect the target LLM's service availability. Engorgio has the following two technical contributions. (1) We employ parameterized distri- bution to track LLMs' prediction trajectory. (2) Targeting the auto-regressive nature of LLMs' inference process, we propose novel loss functions to stably suppress the appearance of the <EOS> token, whose occurrence will interrupt the LLM's generation process. We conduct extensive experiments on 13 open- sourced LLMs with parameters ranging from 125M 30B. The results show that Engorgio prompts can successfully induce LLMs to generate abnormally long outputs (i.e., roughly 2-13x longer to reach 90%+ of the output length limit) in white-box scenario and our real-world experiment demonstrates Engergio's ' threat to LLM service with limited computing resources. The code is released at: https: http::://gtttub..oo/ wwwiittub.ooooomm http:///iinc..cccca//scocoo///o/ http:///mm.iiinncood///ggoo/oeoo prompt.
INTRODUCTION
Large language models (LLMs) (Touvron et al., 2023; Ouyang et al., 2022; Carlini et al., 2021) have demonstrated remarkable performance in various real-world applications, e.g., online chatting (Shen et al., 2023), customer service (Gimpel et al., 2023), and finance (Wu et al., 2023). Given the increasing popularity and adoption of LLMs, reducing their inference cost becomes critical. Firstly, from the cost aspect, a modern LLM normally contains billions of parameters, and each inference generation may consume considerable resources and time. Many AI service providers are paying more bills to support their LLM inference services than training (Patel et al., 2024: Li et al., 2024; Patterson al., 2022). Secondly, from the service availability aspect, there fierce competition across different L.M service providers, making service reliability and fast response time important factors in attracting customers. Meanwhile, these two considerations motivate malicious entities to attack the LLMs, increasing their operational cost and generation latency.
In this paper, we explore the landscape of inference cost attacks against modern LLMs. First proposed in Shumailov al. (2021) to attack encoder-decoder transformers, inference cost attacks aim to intentionally maximize the energy consumption and latency of model inference via new type of adversarial input. The inference cost attacks on language models (Shumailov al., 2021; Chen et al., 2022; Feng et al., 2024) are tailored for encoder-decoder models and rely on perturbation- based mutation to progressively hit desirable adversarial input. However, demonstrated in Section 2.2 and Section 4, they become ineffective against modern LLMs (Graves, 2013), which adopt the auto-regressive generation scheme (Graves, 2013), remove the cross-attention mechanism, and employ a sub-wort word tokenization algorithm. Geiping et al. (2024) propose an ad adversarial prompt attack to coerce LLMs into repeating specific content, achieving effects similar an inference cost attack. However, its reliance on the starting response weakens robustness and increases detectability.
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In general, it is challenging to design inference cost attacks against modern decoder-only LLMs, even given the existing works discussed above. The main challenges stem from two aspects: (1) Uncertain Generation Process. The generation process of decoder-only LLMs is inherently casual, auto-regressive, and sampling-based, rendering it difficult to constrain them to generate a specific long response. The occurrence of one deviant token can directly distort the generation process from the desirable decoding trajectory, challenging attack effectiveness and stability. (2) Discrete Input Modality. Text-completion LLMs accept input text in the form of discrete token sequences but operate within the embedding space, which implies an irreversible mapping from the embedding space back to the token space. While we can leverage gradient information to optimize a more desirable soft embedding representation for the input, we face challenges in accurately identifying corresponding tokens the token space for the optimized soft embeddings. This restricts us from effectively leveraging gradients to guide updates to the input token sequence (i.e., adversarial input). To address the above challenges, we need to consider two intriguing and important questions: (1) how to accurately frame goal as well-aligned optimization problem and (2) how to effectively instruct the updates to the discrete input sequence given the modeled objective.
In this paper, introduce Engorgio', a sim- ple yet effective method to generate threat- ening Engorgio prompts against state-of-the- art LLMs. Our focus is on text completion, where LLMs predict the next token based on the initial prompt and previously generated tokens until an end-of-sequence (<BOS>) token is predicted or a maximum length is reached. Technically, Engorgio effectively addresses the above challenges via: (1) In- spired by the special role of <EOS> token in determining whether the model halts its response, we adopt an untargeted objective called <EOS> escape loss, which reduces the <EOS> token's occurrence probability. We also com- bine a self-mentor loss to stably induce longer responses. (2) We employ a re-parameterization design to effectively utilize the gradients, by modeling the potential distribution of the entire context that can fulfill both objectives. Figure shows the effects of our attack: normal prompts (e.g., the renowned ShareGPT dataset²) typically tempt the LLMs produce short sequences; in contrast, the crafted Engorgio prompts can make the model response extraordinarily long.
Distribution of ShareGPT sequence lengths
Occurrance fraquancy
60
Engorgio
40
Normal inputs
20
0
200
400
600
800
1000
Sequence length
Figure 1: Distributions of the total lengths (input plus output) of normal samples from ShareGPT2 and En- gorgio prompts.
In summary, our main contributions lie in three folds: I) We explore a novel research direction, inference cost attack against modern augo-segres LLMs. We highlight how crafted adversarial prompts can impact I.LM service availability. 2) We analyze technical challenges associated with the attack surface. Based on our insights, we propose Engorgio, a simple yet effective method that can stably induce lengthy LLM responses. 3) To prove the effectiveness Engorgio, we conduct extensive experiments over 6 base models and 7 supervised fine-tuned (SFT) models with parameters ranging from 125M to 30B, as listed Table 5. Specifically, the generated Engorgio prompts can achieve roughly 90%+ of the maximum allowable length on 6 base models, while normal queries can only cause between 0-40%. For SFT models, Engorgio can significantly outperform basclines by up 13x, A real-world experiment demonstrates Engorgio's implications in service availability.
PRELIMINARIES
2.1 LARGE LANGUAGE MODELS (LLMs)
The task of language modeling tracks the rationality of text sequences and treats the probability of certain sequence as a product of conditional probabilities (Jelinek, 1980; Bengio et al., 2003):
N
P( ... ØN) II
P P( .. 1,,))))))))))))
(1)
'Engrriio is spell in the Harry Potter universe, which causes objects (or creatures) to increase in size. Pttpps: /saarggpt.cco
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where P(xs/211-" 2;-1) denotes the probability of predicting 2; as the next token given the quence I1 ... 22 For a Transformer based model fe : X' - y it accepts a V of tokens with any admitted length S and produces output vector rs fe (X1:s) E RV to predict the next token, where V is the model's vocabulary size. Most prevalent LLMs like L.LaMA (Touvron al., 2023) and GPT-4 (OpenAI, 2023) are based on the Transformer decoder architecture (Vaswani et all., 2017). The architecture is designed to perform inference in an auto-regressive manner (Graves, 2013), i.e., LLMs generate one token at time and use the previously generated tokens predict next tokens. We detail the LLM generation process and models involved in this work in Appendix A.1.
2.2 INFERENCE COST ATTACKS
Machine learning services are facing an availability threat. Shumailov et al. (2021) showed that malicious users could intentionally craft adversarial inputs, known sponge examples, to signifi- cantly increase the energy consumption and latency of the corresponding inference process. Such inference cost attacks can greatly affect the service provider's operational cost and user experience. Following this work, a variety of attacks have been designed to target different AI systems and appli- cations, for example, image classification (Müller & Quiring, 2024), camera- based object detection (Shapira et al., 2023; Schoof et al., 2024; Shapira et al., 2022; Xiao et al., 2024; Ma et al., 2024), LiDAR-based object detection (Liu et al., 2023), and multimodal models (Gao al., 2024).
This paper focuses on attacking the modern auto-regressive LLMs. Existing inference cost attacks against language models (Shumailov et al., 2021; Chen al., 2022; Feng al., 2024) only effective when targeting the encoder-decoder structure. Shumailov et al. (2021) generated sponge examples by compressing more tokens into one sentence, leading to higher computational burden in the cross-attention operations, which are ineffective for LLMs lacking cross-attention modules. Sub-word tokenization methods BPE (Sennrich et al., 2016) eliminate the appearance of <UNK> token and enhance I.LMs' typo-tolerating ability, largely invalidating perturbation-baser methods like I.M.EffChhccer (Feng et al., 2024). For the optimization-baxse proposes targeted attack that coerces LLMs into producing elicit specific starting response (i.e., repeating "Hello There" 24 times), indirectly achieving effects similar to an inference cost attack. However, this approach is less stable due to its reliance on the starting response's 's effectiveness and is easily detectable as the starting response serves as a clear indicator of adversarial intent. This motivates to design new attack methodology tailored for modern LLMs. In this work, propose a simple yet effective method to overcome the technical challenges inherent in this task.
method, Geiping et al. (2024)
2.3 THREAT MODEL
We design our attack following the threat model of previous inference cost attack studies against language models (Shumailov et al., 2021; Chen et al., 2022; Feng et al., 2024) and provide detailed discussion about the practicality and implications of the attack in Appendix A.2.
Attacker's goal: As service user, the attacker aims to craft Engorgio prompts T. which could induce as long output as possible. Such behaviors could bring much higher operational costs for the LLM service provider, and affect the service availability to other normal users.
Attacker's knowledge: We mainly consider a white-box scenario, where the attacker has full knowledge of the target model, including its architecture, input template, model parameters,
etc. We also consider the black-box setting, in which we transfer Engorgio prompts to attacker- unknown models (see Appendix B.1 for details).
Attacker's capability: The attacker locally generates the Engorgio prompts T. aligned with her knowledge settings. Then she sends the constructed Engorgio prompts T to the target LLMs and collects the responses for attack checking.
3 METHODOLOGY
3.1 ATTACK INSIGHT AND OVERVIEW
In order to achieve the attack goal, review the mechanism of generating texts by LLMs. A sample for the LLM can be split into an input part and an output part (see Appendix A.3 for more analysis). Given an input sequence (dubbed prompt) composed of & tokens, the model generates
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Vocabulary PP
Generation Stage
Aggregated Embeddings g
Local Model
Preclcttdd Logits (e(E())) c gear
Weight Matrix
<EOS> Escape Loss
Self-mentor
Proxy Distribution
Descoday
Quary
8 RT*
Token Sequance
Engorgio
Output Sequence
Output Sentence
Probability
Prompt
Matrix
lengeh -
Jength +
E Mhst
Target Model
Testing Stage
Figure 2: The pipeline of Engorgio. The whole pipeline is divided into two stages. During the gen- eration stage, we employ a gradient-based method to update the proxy distribution for the Engorgio prompt, where the gradient information is obtained from local proxy model. For the testing stage, we leverage the optimized proxy distribution to decide the final Engorgio prompt.
the subsequent tokens (i.e., the output part). The generation continues until either of two conditions is met: (1) reaching pre-set maximum allowable length; (2) encountering an <EOS> token which indicates the end of the sentence. As the maximum allowable length fixed as S, the problem is stated as follows: the later an <EOS> loken is encountered in the inference process. the higher cost and latency this query will take. Therefore, to achieve latency damages to the service provider, i.e., maximizing the length of the output part, the attacker aims to create Engorgio prompts, which can effectively suppress the possibility of predicting the <EOS> token during the inference.
Based on this insight, we design Engorgio, novel attack framework to generate Engorgio prompts against LLMs. Figure 2 shows its overall pipeline and we provide a term list in Appendix A.4. The core is the introduction of a parameterized proxy distribution. To satisfy the requirements for Engorgio prompts, we explore how to update the distribution with the guidance of an <EOS> escape loss and self-mentor loss. The whole process of crafting Engorgio prompts two-stage:
For each optimization step, we convert the proxy distribution matrix 0 to a weight matrix w using the Gumbel-Softmin function. We then aggregate the embeddings of all token candidates weighted by w to project 0 into the embedding space. This output is fed into the model calculate two loss terms, allowing us obtain gradients for & easily. The matrix is updated based on these losses, continuing until no significant changes are detected.
Generation stage:
Testing stage: The optimization process guarantees that the output part falls onto a region with low probabilities of <EOS>. Given the strong correlation between the Engorgio prompt and the output, we can sample the Engorgio prompt using the normalized w It is observed that as the optimization progresses, the distribution matrix 0 typically converges toward specific prompt with a significantly higher sampling probability compared to others. This prompt is adopted as the final Engorgio prompt T. This approach significantly reduces the cost of evaluating other prompt candidates. We hypothesize that the objectives, particularly self melfomentor loss, contribute to identifying the optimal Engorgio prompt, detailed in Section 4.6.
3.2 PROXY DISTRIBUTION
To increase the lengths of the target LLM's responses, we search for the Engorgio prompts with the help of proxy model. LLMs typically accept a token sequence (corresponding to one input text) as input, cast each token into the embedding space, and work within the embedding space. Each token has corresponding embedding; however, not all embeddings correspond to tokens. We can optimize suitable embedding expressions that satisfy the objectives in the form of prompt learn- ing (Li & Liang, 2021; Liu et al., 2021), but face challenges in determining the corresponding token sequence (i.e., input text). We resort to a re- parameterization method.
As LLMs predict the next tokens according to the probability distribution, it is more efficient to search for desirable Engorgio prompts by sampling from an appropriate distribution (Guo et al., 2021). Therefore, we introduce proxy distribution to track the process of sequence sampling. This
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proxy distribution is parameterized matrix A RSV, with S denoting the maximum allowable length, corresponding to the whole context. instructs how to select a suitable token sequence from token vocabulary with V token candidates in the following test stage. Then the question is how to ensure that the proxy distribution meets the objectives. This endeavor seeks to involve the distribution matrix in the generation stage, subsequently updating based the gradients. Concretely, in the forward pass, the distribution vector 0,, corresponding to the i-th token in the Engorgio prompt where E [l..... S). is independently normalized. This serves as weight to aggregate token embeddings across the model vocabulary, thereby casting 0; as soft token in the embedding space. This process formulated as Eq. 2.
V
ē(O,) = Σ (w)))ee)) 3=1
where e(f) E RR denotes the embedding of the j-th token within the model vocabulary and wy E RV is the normalized version of 0; with the sum Σj-1 (w:) = 1. We adopt Gumbel-Softmax (Jang et al., 2017), which introduces stochastic elements and enriches the diversity of tokens involved in the generation stage. The normalization is conducted in Eq. 3:
(wi)s = Σ exp(((1,.
exp((B.,,
(3)
where 9t,1 ... 9t.V are drawn from the distribution Gumbel(0.1) and T > is a temperature factor used to control the uncertainty. The introduction of the random variable gi from an i.i.d distribution benefits the diversity of the sampling operation. Due the differentiability of Gumebl-SSfftmal we can take full advantage of the gradient information to update 0 in the generation stage and guide the sampling of the final Engorgio prompt 'T in the test stage.
SET models assume the input should be embedded in a specified template T, as illustrated in Fig- ure Considering the most general case that the template T = {[Piss] 2,, [Pe+tm]] ,3) contains a prefix and an infix, we define the corresponding embedding sequence to 0 as Eq. 4.
(0) = (e([P..))) ē(01a), e([P++1m)) ē(0++1amm))
(4)
where Pas and 1.+1. represent the token sequences corresponding prefix and infix, and the shape of 0 is adjusted to (s - m) x V. The input composition is illustrated in Figure 6.
3.3
LOss DESIGN
To obtain desirable proxy distribution, we mainly depend on two key loss components to update 0, <BOS> escape loss and self-mentor loss. The <EOS> escape loss closely aligns with our target goal to make the output part longer while the self-mentor loss is designed to enhance the usability of the proxy distribution. Balancing the impact of the two loss terms with X, update the proxy distribution as follows:
min Lese + Xm (0)
<EOS> escape loss. Due to the unpredictability of the LLM generation process, enforcing a spec- ified long response is challenging. We resort to untargeted objective, which is to decrease the prediction probability of the <EOS> token. However, it is still impossible to accurately forecast the exact occurrence position of <EOS> during the test stage. To tackle this, we propose penaliz- ing the occurrence of <EOS> token from all positions, rather than focusing on specific positions. effectively manage the uncertainties associated with <EOS>
This broader treatment allows placement. The <E05> escape loss is defined as below:
Leso(0) = Σ (E(()a)
(6)
where & denotes the index of the <EOS> token for the target model. We adopt a Doftmaax-normalized probability of <EOS> so that it can better measure the relative chance that the model predicts <EOS> as the next token at a certain position, which is more effective than directly decreasing the absolute logit of the <EOS> token. An input sequence containing <EOS> is illegal, as the inference process should have halted before predicting the next tokens for the Engorgio prompt. Therefore, we also consider reducing the predicted <FOS> probabilities of the Engorgio prompt part.
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Self-mentor loss. Another challenge is that we can only query the target model utilizing the Engor- gio prompt 7 to ensure attack stealthiness and efficiency. Considering the auto-regression nature of modern LLMs, we cut off the first tokens our Engorgio prompt. Moreover, 6 only indepen- dently tracks the token selection of the i-th position, but the correlation between tokens should also be enhanced. Therefore, we seek enhance the relevance of all tokens in the sequence, especially the bond between the Engorgio prompt and output parts. Inspired by LLM's causal pre-training paradigm, we search for a sequence where the proxy model fits well. The loss term is given below:
Cem (0) Σ C(w,+1, ((u.),,.0ttmmx(((6((()))))))
where the cross entropy loss. The closer to 0 Lem is, the better the proxy model fits in input EsS) which helps the Engorgio prompt T steadily induce longer output.
4 EVALUATION
4.1 EXPERIMENTAL SETUP
LLMs. We include multiple base models, OPT-125M, OPT-1.3B, GPT2-large, LLaMA-7B, LLaMA-2-7B, and LLaMA-30B. SFT models are further fine-tuned with additional datasets, for which we consider seven well- SFT models including Alpaca (7B), Vieuna (7B), StableLM (7B), Koala (7B), Oraca (7B), Samantha (7B), and ChatGLM (6B), as our targets. More details about the models involved in this work are provided in Appendix A. Considering the crucial importance of prompts, we also consider the three cases of deploying base models with prompts according to the attacker's knowledge about the deployed prompt (cf. Appendix B.3).
Baselines. We consider four types of inputs as baselines for comparisons. (1) Normal inputs: we collect 50 samples from the training dataset for Standford-alpaca' which are generated by OpenAl's text-davinci-003, and 50 samples from ShareGPT* website where people can share their ChatGPT conversations. We use the mixup to roughly represent the normal response length of LLMs. (2) Special inputs: use prompts with the semantics of demanding longer output (i.e., prompts starting with "output longer"). (3) LLMEfiiChccer: we adopt the three attacks (character, word, and structure attack) proposed in Feng et al. (2024) and report the averaged results across the attack variants. (4) Sponge examples: we generate such inputs using the method from Shumailov et al. (2021) by only setting the same input length our method.
Metrics. Due to the intractable serving mechanisms for LLM, we report results on the level of model behaviors. To mitigate potential sampling bias caused by the inherent variability in LLM inference, we measure the average token number of the generated outputs (Avg-len). We query the target LLM multiple times using the sampling generation and compute the average length across these responses. This renders Avg-len a robust estimate of the Engorgio prompt's 's efficacy. Second, we calculate the ratio of the LLM outputs that reach the maximum length (Avg-rate) to evaluate the stability. Notably, inference costs increase super-linearly with longer responses, making Avg-len a lower bound on the prompt's impact on inference cost, which we detail in Appendix A.5.
Configurations. We use the Adam optimizer with a learning rate of 0.1 to update the distribution matrix 6. We allow maximum of 300 optimization steps, the cost of which is acceptable, especially when considering the reusability as explained in Appendix A.6. The Gumbel-Softmax temperature factor т is set to 1, and the default Engorgio prompt length inputs, special inputs, LLMEffiChecker, and sponge examples is roughly the same Engorgio to ensure fairness. The loss coefficient À is empirically set to 1. The optimization starts with a random prompt, which we use to initialize the proxy distribution. Constrained by the computing resources, we set 1,024 as the pre-set maximum length. We also conduct experiments with full context size on two representative base models (2,048 for LLaMA-30B and LLaMA-7B) and one SFT model (4,096 for Samantha) to demonstrate the extensibility to longer context size. Please refer to Appendix B.8 for examples of prompts and responses for normal inputs, sponge examples, and Engorgio prompts.
= 32. The input length of normal
wattaatttaa/saaaaaaaeaaaaaaa stanfora appcca/ htttps://shareept..om/

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Table I: Results of Engorgio against modern LLMs.
Model Max length
LL0
LLaMA-7B
LLaMA-7B
LLaMA-2-7B
2048
1024
2048
1024
Avg-len
Avg-rate
Avg-len
Avg-rate
Avg-len
Avg-rate
Avg-len
Avg-rate
Normal inputs
622.2
12%
611.4
39%
757.9
16%
818.9
69%
Special inputs
1005.2
40%
737.3
58%
1292.2
54%
773.8
50%
LLMeniCheecer
1052,8
28%
682.3
41%
1306.7
41%
833.7
64%
Sponge examples
1277.7
38%
857.6
81%
1659.8
78%
900.6
86%
Engorgio
2019.1
95%
983.4
94%
1817.7
84% %
1024
100%
Model
Samantha
StableLM
Koala
Orca
Max length
4096
1024
1024
1024
Avg-len
Avg-rate
Avg-len
Avg-rate
Avg-Ien
Avg-rate
Avg-len
Avg-rate
Normal inputs
313.7
2%
388.2
6%
357.5
6%
286.0
1%
Special inputs
173.3
0%
202.9
4%
436.1
0%
199.5
0%
LLMEniChecker
172.6
0%
688.3
38%
324.6
5%
203.4
0%
Sponge examples
284.9
3%
301.1
16%
432.2
22%
211.1
1%
Engorgio
3951.5
95%
1021.6
98%
1024
100%
9U8.1
86%
Prefix+Engorgio
4027.6
95%
1024
100%
1024
100%
962.6
90%
Model
Samantha
ChatGLM
Alpaca 1024
Vicuna
Max length
1024
1024
1024
Avg-len
Avg-rate
Avg-len
Avg-rate
Avg-len
Avg-rate
Avg-len
Avg-rate
Normal inputs
231.6
2%
263.4
1%
179.4
312.6
0%
Special inputs
82.0
0%
247.9
0%
132.5
252.4
4%
LLMEffiChecker
149.8
0%
182.0
0%
192.9
273.4
0%
Sponge examples
155.1
0%
685.2
56%
833.8
78%
599.6
44%
Engorgio
944.0
89 %
979.6
95%
954.2
92%
789.3
60%
Prefix+Engorgin
970.0
89%
1024
100%
1024
100%
861.5
68 %
4.2
MAIN RESULTS
We report our results on base models in Table 1. Comparing normal and special inputs reveals that semantic inputs induce base models output longer. This means that base LLMs can understand the semantics inside the inputs and seemingly feature being talkative. However, relying solely on special input is far from reaching the maximum allowable length. LLMEtfihhecer proves ineffec- tive against more advances LLMs. Our method can achieve a very high ratio (roughly 90-100%) of letting the base model keep endlessly generating tokens until reaching its maximum length, which outperforms all baselines including sponge examples. While sponge examples extend output length compared to normal or special inputs, they are less stable than Engorgio as they struggle with LLMs' sampling-based decoding. Results of more base models are presented in Appendix B.2.
SFT models may use cut-off as a preprocessing strategy on their fine-tuning datasets (e.g.. at most 512 tokens for Alpaca). This potentially biases the fine-tuned model to produce short responses, which makes our goal challenging, as suggested by the results of normal inputs in Table 1. For special inputs, even with instructions for longer responses, SFT models still produce notably shorter outputs, sometimes even shorter than normal inputs. The silent nature of SFT models worsens the performance of sponge examples. For L.MEffiChecker, the weaker performance extends to SFT models. We hypothesize that recent LLMs are more robust to typing errors, invalidating perturbation-based attacks. In contrast, Engorgio knows how to better optimize the Engorgio prompt by focusing on a distinct goal: avoiding the generation of the <EOS> token. It effectively increases the output length approach the maximum limit, especially when paired with semantic prefix as discussed in Section 4.4, achieving near- maximmm allowable lengths. We also explore a black-bar box scenario, where we resort to the transferability of Engorgio prompts (See Appendix B. for details.
4.3 ABLATION STUDY
Impact of loss design. Initially, we assess the performance when optimizing only with the <EOS> escape loss (noted "ESC" in Table 2). A comparison with normal input and special input from Table reveals that even utilizing only the <EOS> escape loss consistently results in longer outputs. We also observe that combining the self-mentor loss, identified as "ESC+Self mentor" -elf-memf-mento"" in Table 2, further increases the Avg-len with almost no extra cost. More experiments in Appendix B.5 show that Engorgio is not strongly dependent the choice of X.
Impact of Engorgio prompt length. We explore the attack results under different prompt lengths The basic intuition is that longer Engorgio prompt contain more malicious information
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Table 2: Ablation study, Prompt length is separated from Avg- len to better understand the impact of key designs.
Table 3: Impact of temperature
setting,
LLaMA-7B (1024)
StableLM (1024)
Prompt length
ESC
ESC+Self-mentor
Temperature
Avg-len
Avg-rate
Avg-len
Avg-rate
Avg-len
Avg-rate
0.1
1021
98%
32
893.5 +
80%
9514 +
94%
0.3
830.5
62%
64
870.6
64
85%
945.5 64
98%
0.5
610.4
28%
128
827.8 128
85%
880.6 + 128
94%
0.7
513,8
33%
Alpaca (1024)
Samantha (1024)
Prompt length
ESC
ESC+Self-mentor
Temperature
Avg-len
Avg-rate
Avg-len
Avg-rate
Avg-len
Avg-rute
0.1
944,1
90%
32
967.4 + 32
96%
992.0 + 32
100%
714.1
58.7%
64
943.8 + 64
96%
949.1 + 64
98%
0.5
553.0
40%
128
867.3 128
96%
896.0 + 128
100%
0.7
406.3
23.8%
Koala (1024)
ChatGLM (1024)
Prompt length
ESC
ESC+Self-mentor
Temperature
Avg-len
Avg-rate
Avg-len
Avg-rate
Avg-Ien
Avg-rate
979.6
95%
32
980.0 +
98%
992.0
100%.
0.3
934.0
88%
64
950.5 64
98%
960.0 64
100%
0.5
908.1
81%
128
849.2 128
90%
896.0 + 128
100%
820.0
71%
to induce the LLMs' outputs to be longer. The results are given in Table 2 with three different prompt lengths (i.e., 32, 64, and 128). For base models like OPT-125M and LLaMA-7B, even the smallest prompt length of 32 can induce them to output max-length sequences. For SFT models, as the prompt length increases, Avg-len and Avg-rate increase in most cases. In summary, although longer prompt improves the attack performance, it is not prerequisite for ensuring effectiveness.
4.4 ATTACKS AT DIFFERENT DECODING TEMPERATURES
We investigate how temperature affects Engorgio's 's effectiveness. Results in Table 3 show that a larger temperature introduces more uncertainty during generation, potentially leading to deviations in the model response. For talkative base models, they are tempted to respond endlessly when a high temperature of 0.7 is used while low temperature of 0.1 is more suitable for the silent SFT models. In most cases, c.g., when querying API service, the temperature is at the users' discretion. Our quantitative statistics show that the output lengths induced by Engorgio prompts gather either at the shorter end or around the maximal length. See details in Appendix B.4. Engorgio prompts can either encourage the SFT model generate longer outputs or confuse it, resulting in brief responses like "not understand." " Thus, we consider fusing Engorgio prompts with semantic instructions.
Adding semantic prefix/suffix. We can in- troduce additional semantic instructions to avoid the SFT model directly outputting "not understood".. Particularly, prefix with seman- ties that encourages longer response will be wo- ven with the Engorgio prompt in both the gen- eration and testing stage. The results in Table 4 show that introducing semantic prefixes can im- prove performance. Comparing with the results Table I, we can observe that adding the prefix to normal inputs still cannot induce an extremely long response. Compared to adding prefix, adding the same semantic sequence as suffix does not help. We hypothesize that that's because a semantic prefix impacts the entire generation process while a suffix only influences the subsequent generation.
Table 4: Results of introducing semantic pre- fix/suffix when the temperature is 0.7.
Alpaca
StableLM
Model
1024
1024
Avg-len
Avg-rate
Avg-len
Avg-rate
Only prefix
132.5
7%
202.9
4%
Prefix-normal
214.2
1%
440.6
9%
Engorgio
353.2
8%
513.8
33%
Prefix + Engorgio
531.2
43%
884,8
83%
Engorgio + suffix
314.9
$34.3
4.5 ATTACKING REAL-WORLD LLM SERVICES
We conduct real-world case study to assess the practical threats of Engorgio. Corresponding to realistic scenarios listed in Appendix A.2, users share limited cloud resources for inference requests.
Experiment setup. We utilize the Hugging Face inference endpoint as our cloud service, deploy- ing StableLM (maximal length of 4096) as the target LLM. Our experiments explore three GPU configurations: 1x Nvidia A10, 4x Nvidia A10, and 2x Nvidia A100, aiming to demonstrate how a small number of attackers can significantly compromise the service's performance. We focus on
httpps:// pertps:/uui.eddeee Hug.. wtw.-uuggiggaacc.ooo..
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Queue Time
Inference Time
Throughput
1*A10 (1MU, 9NU)
4MA10 (3MU, 27NU)
2*A100 (5MU,95NU)
T Phroughpu (query/min)
-36
Labency (s)
-12
Normal
Attack
Normal
Attack
Normal
Attack
Figure 3: Results of attacking real-world LLM services ("MU": malicious user, "NU": normal user).
OPT-125M
Koala
400
12.5
1.0
Taken Distribution on
Taken
escape
0.8
300
Total Lass
0.6
200
5.0
0.4
100
0.2
0.0
200
400
600
200
400
600
100
200
Iterations
Iterations
# Iterations
Figure 4: Loss curves on 125M (base model) and Koala (SFT model), with aggregated embeddings and token sequence as input, respectively.
Figure 5: On LLaMA-7B, the <EOS> escape loss correlates with the relative level of <BOS> being predicted.
two main metrics: normal client latency, defined as the average response time from querying the ser- vice to receiving the output, and server throughput, calculated as the number of requests processed minute. More details can be referred to in Appendix B.6.
Main results. As shown in Figure 3, attackers with Engorgio prompts can significantly compromise the LLM services. Although the inference time for normal clients remains consistent, Engorgio prompts significantly increase the queuing time for normal clients scheduled after the attackers. We observe that only small ratio of attackers (c.g.. out of 10 or 5 out of 100) could lead to significant latency increase. Besides the negative effect on clients, Engorgio also severely harms the cloud service throughput, which is almost cut off. We conclude that limited number of attackers equipped with Engorgio could severely disturb the fragile cloud-base LLM services.
4.6 WHY OUR METHOD EFFECTIVE?
Q1. How does the distribution matrix instruct the token selection in the optimization process? In the optimization process, seek update the distribution matrix 0 rather than selecting indi- vidual tokens, meaning that the most suitable input is the aggregated embedding ē(0;). In Figure 4, find that even in the middle of the optimization process, the token sequence greedily sampled according to the distribution matrix performs only slightly worse than the aggregated embeddings when goal is to decrease the total loss. This means that distribution update design is effec- tive in searching for suitable token sequences. Moreover, we find that the <EOS> escape loss of SFT models like Koala is much harder decrease than base models like OPT-125M. This partially supports that base models are easier to induce than SFT models.
Q2. Does <EOS> escape loss stop <EOS> from appearing? To verify that <EOS> escape loss reduces the probability of <EOS> appearance, we calculate the highest probability of the <EOS> token at all the S positions. We formulate this as μ = (ann(SSotmax(fe(EE000 (x)) which signals the highest probability of the interruption of the generation process. We report the relative level of μ compared to the average probability 1/V coupled with the change of <EOS> escape loss in Figure 5. We find that the decrease of <EOS> escape loss lead the maximum probability of the occurrence of <EOS> token to a low level (close to O). This substantiates the effectiveness of <EOS> escape loss in stopping <EOS> token from appearing.

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5 DISCUSSIONS
The resistance to potential countermeasures. There are no off-the-shelf' defenses tailored for Engorgio yet. Service providers are faced with trade-off between detection accuracy and service quality. Although rare, normal inputs may also lead to long response. Engorgio prompts are not crafted to be coherent. However, our experimental results show that simple methods like perplexity filter will lead to an unacceptably high false positive rate, significantly degrading user experience. This is rooted in the variability of legitimate user queries themselves. What's more, introducing semantic prefixes inevitably improves the coherence of Engorgio prompts, but incurs no performance degradation. Another potential countermeasure is anomaly detection, monitoring the output length distribution of queries and blocking high-risk users. However, the method may face problems of false positives and attackers can strategically adjust behaviors to evade detection. Please refer Appendix B.7 for more related experimental results and discussions. We will explore effective defense mechanisms in our future work.
Potential limitations. Although the white-box setting in this work can already cause far-reaching consequences as explained in Appendix A.2, we emphasize the need to systematically study the transferability of Engorgio prompts. The method efficiency in crafting Engorgio prompts should be further improved. For the current version, we generate one Engorgio prompt at one time. We plan to extend to a batch method and study the interoperability among different Engorgio prompts. To address high-temperature cases, we employ semantic prefixes to mitigate issues. Future work will focus on tracking more active model prediction dynamics to eliminate these challenges. Cur- rently, we do not consider coherence when crafting Engorgio prompts. As coherence enables higher stealthiness of Engorgio prompts, we plan to further explore our future work.
CONCLUSION
In this paper, we investigate the inference cost threats to modern auto-regressive language mod- els, which tempt the victim models produce abnormally long outputs and compromise service availability. We introduce Engorgio, a novel attack methodology, to effectively generate Engorgio prompts that can significantly lengthen the model responses. Driven by the challenges of uncertain generation process and discrete input modality, our work advances in utilizing proxy distribution and untargeted loss craft threatening Engorgio prompts. This achieved by tracking a parame- terized distribution of Engorgio prompts and optimizing it to decrease the occurrence probability of the <EOS> token. We validate the effectiveness of Engorgio with extensive experiments on 6 base models and 7 SFT models, considering various prompt scenarios. By inducing the target L.MSs to output until their maximum length limits, we achieve roughly 2-13x more inference cost per query compared to normal inputs. We also conduct real-world case study to demonstrate the practical threat posed by Engorgio to cloud-based LLM services.
ETHICS STATEMENT
This paper highlights potential adversarial threats to LLM service availability. Instead of conducting real-world attacks, this work serves as a clarion call for service providers consider not only maxi- mizing service latency but also the risks to inference costs posed by malicious users. Engorgio offers method for generating threatening prompts. allowing service providers stress test their online LLM services effectively. All experiments adhere to principles of trustworthiness and harmlessness. Note that real- world attack demos in Section 4.5 target only our own L.M service, without impact- ing others. Our work utilizes open-source models and datasets, ensuring no privacy violations. Our work also does not involve any human subject. This work does not raise ethical issues in general.
REPRODUCIBILITY STATEMENT
The details of models, paper-parameter settings, and experimental settings can be found in Sec- tion 4.1 and Appendix B.6. The models involved in this work all openly accessible. The codes for reproducing our main evaluation results are provided in the anonymous repository. We will release the full codes of our methods upon the acceptance of this paper.
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ACKNOWLEDGMENTS
We would like to thank the helpful comments of Yiming Li and anonymous reviewers. This work supported by the National Science Foundation for Distinguished Young Scholars of China under No. 62425201, and National Science Foundation China under Grant No. 62132011.
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A FURTHER STATEMENTS
A.1 INVOLVED MODELS
Mainstream LLMs can be categorized into two main classes as in Table 5 including pre-trained base models and supervised fine-tuned (SFT) models. Base models are pre-trained on large-scale unla- belled training corpora in the manner of self-supervised learning like next token prediction (Radford et al., 2018) and auto-regressive blank infilling (Du et al., 2022). This process endows the base models with basic language abilities. Base models can be fine-tuned (Ouyang al., 2022) dis- tilled from a more powerful oracle model (Wang et al., 2022: Peng et al., 2023). Such SFT models can typically perform better on downstream tasks. Besides, low overhead to obtain usable model makes SFT mainstream in the field of LLM development (Zhou et al., 2023; Lee et al., 2023).
During inference, LLMS iteratively repeat the process of predicting the next tokens until the max- imum length limit is reached or an end-of-sequence token (<ECS>) is encountered. LLMs can parallelly process all sub-sequences {X1+) ==1 of the whole input in one single forward pass uti- lizing the mask design and finally outputs R E SVV, where R; fX(1):)). A new token will be selected according to Rs and its embedding will be concatenated with the previous sequence to form new sequence X1.$+1, used to predict the following tokens. Another representative line of LLMs, ChatGLM (Du et al., 2022), incorporates a different attention mask but still involves an regreseeee inference scheme. Both types of auto-regressive LLMs are explored in this paper.
Text decoding methods, which decide how to utilize Rs (the predicted next-token logits) to choose token, essential to natural language generation. Greedy search (Su et al., 2022) is the simplest way, which directly selects the token with the maximum probability in Rş. In probabilistic sampling, the decoding process replaces the word with the highest probability with probability- based sampling. The sampling allows for more diversity in the sequence generation process. Since the sampling method can generate more diverse outputs, most existing LLMs use the sampling method (Holtzman et al., 2019) for decoding.
Table 5: Base LLMs and SFT LLMs included in this paper. We experiment on underlined ones.
Category Model
Base model
Date
Model size
GPT-26
2019
1 117M, 345M, 762M, 1.5B
125M, 350M, 1.3B, 2.7B.
Base
0PT
2022
6.7B, 13B, 66B, 175B
model
LLaMA
2023
7B, 13B, 30B, 65B
LLaMA-29
2023
7B, 13B, 30B, 65B
Alpaca 10
LLaMA
2023
7B
Vicuna
LLaMA
2023
7B, 13B
Koala 12
LLaMA
2023
7B, 13B, 30B, 65B
SFT model
StableLM
StableLM-Ban
2023
3B, 7B 7B
Orcal4 14
LLaMA-2
2023
Samantha
LLaMA-2
2023
7B
1
ChatGLM-Ban
2023
6B. 130B
h
1.111hh
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A.2 FEASIBILITY AND IMPLICATION DISCUSSION FOR THREAT MODEL
In our attack, we mainly focus on two types of attacker assumptions: white-box attack and blx attack. White-box attack assumes a more powerful attacker with knowledge about the target model's parameters. This setting is rational in the real world in two folds. First, open-resoureing is still the mainstream in the LLM community. Second, because the cost of further tuning is unaffordable, small enterprises or end users may tend to acquire open-sourced models to build LLM inference services, with or without prompts. We provide scenarios where the white-box setting applies:
Subscription-baaee services using open-source models: Many LLM service providers, includ- ing OpenRoute", Codestral Huggingface serverless inference API! and GitHub Models offer services based not only on closed-source but also on open-source models. These services enforce rate limits at the request level, making them susceptible to Engorgio prompts, which aim to maximize token generation within each request. In such cases, white-box settings make sense since attackers can craft adversarial prompts using accessible model weights.
Services open to the public: With the growth of the open-source community, there are efforts to provide everyone with free LLM access. As most of these services are based on open-source LLMs, they are also exposed to threats posed by adversarial prompts like Engorgio prompts. Websites such as HuggingChat" and Chatbot Arena provide free access to top-tier open-source LLMs, and platforms like Huggingface Spaces host over 500,000 LLM-based service demos that are open to the community and free of charge. Additionally, these platforms often do not require users to log in to use the services. As shown in Section 4.5, Engorgio prompts can significantly impact the service availability of normal users by consuming excessive resources and reducing server throughput.
Services deployed by end users: For many users, even incremental fine-tuning of LLMs is prohibitive. As result, users tend to directly use well-trained LLMS for applications. Popular tools like llama.cpp and 2 are commonly used for this purpose. However, when these services are exposed online, they will become vulnerable to Engorgio prompts. Such prompts can consume great amount of computational resources and degrade service availability. We also explore the attack effectiveness when facing LLM services with prompts in Appendix B.3.
For the motivation of the attacker, we have shown the user-level impacts of Engorgio prompts in ser- vice availability and service quality in Section 4.5, For service providers, many commercial LLM service providers are struggling to meet high inference demand due to limited computing resources. This challenge is reflected in the rate-limiting strategies commonly employed by these providers. Beyond token-based rate limits, request-level rate limiting is also widely used for subscription and free-tier users. For example, platforms like OpenRoute and Codestral limit the number of queries for free-tier users to certain requests per minute/day. Similarly, the Huggingface serverless inference API explicitly states that the service enforces request-based rate limits, rather than limiting based on compute or tokens. GitHub Models primarily restrict access by requests per day for different sub- scription plans, with tokens per request as a secondary concern, which aligns with our setting. Given this, an adversary's best strategy would be to maximize the number of tokens generated within each request, which is precisely what is achieved by Engorgio prompts. Notably, inference services based on open-source LLMs are accessible on these platforms, rendering the white-box setting feasible.
Regarding the attacker's motivation, overwhelming the services with Engorgio prompts can lead to a significant waste of computing resources for the targeted LLM service provider.
"httee://ppenrotterra//o/o///
"httearr/ccoesttsllnisstraaa
with--ii-
modelstrate-1imitt
Phetpps//haggiinffcccco////t
"https://Imarena.aii/
Phttesr//aagiinsaseeeoo/sssss
Phttps://cllama.cooo
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From service availability perspective, competition among LLM providers is fierce, especially with the rapid emergence of new providers. In this context, the competitive behavior is not exceptional but noteworthy scenario, which is practical and meaningful. A competitor may employ Engorgio prompts to waste the target service provider's computing resources, reduce throughput, and impact its service quality.
Smaller companies often rent GPU resources to support their LLM services. The cost of renting GPU cards is significant and should be adjusted based on user demands or service traffic. En- gorgio prompts could lead the service provider to misestimate its actual needs. Renters may be incentivized deploy such attacks to pressure service providers into overestimating their needs and renting additional resources.
Adversaries may act with specific purposes or simply with no specific target, driven purely by malicious intent. As demonstrated in our real-world experiments, even limited number of Engorgio prompts can degrade the other users' service quality. For example, when multiple users share the same LLM service through proxy, the total usage is limited by global rate- limiting rule. In this scenario, all users are competing for the shared usage quota. A malicious user could exploit Engorgio prompts to consume a large portion of this limited quota, dominating the access to the LLM services and affecting the service availability of other users.
is worth noting that the per-token pricing of OpenAl is of the scope of the threat model. We mainly focus on the white-box setting. In this setting, the attacker is not assumed to have access to the model parameters of closed-source models, which would be unrealistic. Thus, we do not include OpenAI within our scope. But as discussed as follows, reliably transferable Engorgio prompts may illuminate the hope of further extending our attack to closed-source products.
For cases where the attackers have no direct access to the backend LLMs, they can easily chat with the target L.LM to determine the model identity or guess within a limited number of candidates. We additionally consider and explore another threat model, in which the attacker has no knowledge about the target model but can query the target LLM. In this case, the attacker can craft Engorgio prompts by querying other proxy models and then transfer the produced Engorgio prompts to at- tack the target LLM. The results in Appendix B. show the potential for successful attacks even in scenarios where the attacker lacks direct knowledge of the target model.
Broader implications. Beyond the attack aspect, we are also glad to discuss how Engorgio prompts can contribute positively to refining LLM capabilities: (1) One critical issue we observe in Engorgio is that LLMs often fail to stop generating tokens appropriately when responding to unproductive prompts, leading unnecessary computational costs. In contrast, humans instinctively stop unpro- ductive conversations, but LLMs frequently fail to recognize when to halt generation. Engorgio prompts expose this limitation, showing how models struggle manage the decision halt gen- eration effectively. We argue that the Engorgio prompts can be used for the purpose of adversarial training: training LLMs with (Engorgio prompts, NULL) pairs can help LLMs develop "meta" ability to stop generation thoughtfully. making them more economical and efficient. Although we haven't had the resources to test this idea, we consider it an important direction for future work. (2) A multitude of LLM service providers employ request-level rate limiting strategies. Engorgio prompts can effectively maximize the response length within each request. Thus, it can help the providers assess their systems' maximal workload capacities. This enables providers to correspond- ingly optimize service strategies and avoid overloading scenarios that could lead to service outages.
A.3 DEMONSTRATING CONTEXT COMPOSITION
From a high-level perspective, the text-completion model accepts input (in the form of token sequence or an embedding sequence) and then repeats predicting the next tokens based on the origi- nal input and previously generated tokens. All generated tokens form the output part corresponding to the input. For Engorgio, the model receives an embedding sequence during the generation stage while receiving token sequence during the testing stage. The provided embedding sequence is obtained by treating the normalized proxy distribution 0 as weights and then combining the embed- dings of tokens in the vocabulary.
A.4
TERM LIST
We list the main notations used in this manuscript here for reference.
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Token Sequence
Input Part
Prefix
Trigger
Infix
Output Part
Pac
Pmmm
Distribution Matrix
Pi.t
Bytt
Pr+m.
Pt+ssmm
Figure 6: Sequence composition, with a token sequence in the testing stage and a distribution matrix in the generation stage input, respectively.
Table 6: Term list.
Terms
Symbols
Engorgio prompt
T
Engorgio prompt length
Max length
S
Vocabulary size
V
Embeddings
(
Model
fe
Distribution matrix
Input template
T
Vocabulary embeddings
Z
Hidden size
H
<EOS> token index
K
A.5 FURTHER SEVERITY ANALYSIS OF ENGORGIO
We first discuss the main factors that impact inference cost. The inference cost of LLMs is influ- enced by both algorithmic factors (model behavior) and operational factors (software and hardware implementations). Among them, the dominant factor in inference cost is the behavior of the LLM itself. In Transformer architectures, inference cost scales with response length due to the model's auto-regressive generation nature. Fach additional token requires a new forward pass. compu- tational bottleneck in Transformer models is the (N22 complexity of self-attention layers. Gen- crating a sequence X of length N requires predictions, leading to an overall complexity of + + 2²) ((N3) for the whole generation process. Techniques like KV Cache (Am- inabadi et al., 2022) can reduce the per-token complexity to O(N) by reusing previously computed KV values. However, when consider the total cost of the whole generation process (summing all forward passes of the LLM), the cumulative cost for sequence of length N still comes to be O(N2). If each forward pass had constant computational cost (i.e., FL.OPs), the total inference cost of the whole generation process will increase exactly linearly with response length. However, the running cost of each forward pass in Transformer-basee kens in the context (Vaswani et al., 2017). As more tokens are generated, the model needs to process an increasingly larger context with each forward pass, meaning that the latter forward passes cost more. That's 's why inference costs increase super- linerll with longer responses.
architectures depends on the number of
Figure shows that the output token lengths and the inference costs (FLOPs) of LLaMA-7B (Tou- vron et al., 2023) and ChatGL.M (Du et al., 2022) are approximately linearly correlated (Kaplan al., 2020; Hoffmann et al., 2022). is worth noting that the attention-related operations only account for small part of the overall operations of the model when N is substantially smaller in magnitude relative to the hidden dimension. (Kaplan et al., 2020; Hoffmann et al., 2022) That's why observed an approximately linear plot in Figure 7. The ( complexity means incalcula- ble number FLOPs when larger output length is induced, implying the more severe threats of Engorgio the inference process of the decoder-batt based models with larger pre-set maximal lengths.
Current LLMs' output range is usually 1-4K (Zhao et al., 2023) which can satisfy most chatting tasks but cannot support very complex input (e.g., complex program whole book). More
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LLaMA-7B
ChatGLM-6B
12
Experimental data
Experimental data
Least squares linear
12
Least squares linear fit
FLOPs (Triliin))
10
5-
2-
250
500
750
1000
250
500
750
1000
Output Length
Output Length
Figure 7: The correlation of output length and the total FLOPs, on LLaMA-7B and ChaGGLM-6B.
recent research indicates the possibility of even larger token lengths like 1-million level (Bulatov et al., 2023) or even I-billion level (Ding et al., 2023). According to analysis in Section 4.6, a larger context size will let the self-attention dominate the computation costs, yielding non-linear (i.e., O(N²) complexity) relationship with output length and increasing the attack surface. Unlike unaffected baselines (e.g., similar output length of 1-K LLM and 4-K LLMs for normal inputs in Table 1), Engorgio prompts can trigger significantly more inference costs for LLMs with longer token lengths. It is promising that Engorgio extends to LLMS with longer context sizes. We have demonstrated the effectiveness of Engorgio prompts on Samantha with a full context size of 4,096.
Explanation of our evaluation metrics. As explained above, the inference cost of LLMs can be primarily impacted by the model behavior itself. Given this, we mainly focus on the Avg-len and Avg-rate, which directly reflect the model behaviors, in our evaluation. While service providers may adopt distinct implementations, we emphasize that the behavior of the LLM is ultimately driven by the input (i.e., Engorgio prompts). In this way, the Avg-len and Avg-rate metrics thus provide a re- liable indication of the inference cost impact from Engorgio prompts. We do not make assumptions about the implementation details of software and hardware and do not exploit any implementation- specific features. This choice allows Engorgio prompts to transfer across different inference end- points using the same model, regardless of underlying software libraries and hardware infrastructure. That's s acceptable because they are not the primary determinants of the total inference cost. All in all, the costs that result from implementation details are not considered.
A concrete model of the relationship between Avg-len and latency per request. As the LLM servicing system may be implemented in different manners, we can simplify by assuming that all forward passes consume constant amount of computing resources. In this model, the inference cost of the generation process increases linearly with the number of output tokens. This assumption represents a lower bound for the impact of the Engorgio prompt, as the real-world case would likely exhibit a super-linear correlation between cost and output length. We then define the total computing capability of the server as C, indicating that the server can process up to C' requests simultancously in batch. We assume each batch takes fixed amount of time To to process. However, due to the rettereerees nature of the Transformer decoder, the server cannot generate multiple tokens for single prompt within the same batch. In practice, the LLM inference endpoint typically handles multiple concurrent requests. Let represent the total number of requests, with à of these being Engorgio prompts. Consequently, the problem can be modeled we use z to represent, represents the expected number of tokens that an Engorgio prompt induces the target LLM to generate. Typically, we compute Avg-len by sampling 100 times, which makes it relatively robust to sampling bias. Certainly, we should subtract the constant token number of Engorgio prompt, which is small number of 32 as set in our experiments. After the processing, treat the cg = 32 as the expected number of output tokens induced by one Engorgio prompt. Let C.. denote the average number of output tokens required complete a single normal request.
queuing system. Avg-len, which
For the service quality, we focus on the latency per request, denoted as Treq. which is determined by the total number of forward passes required for processing all requests and the computing capability C. Since the server can process up to C' requests concurrently, the total latency Lestal to process all requests is then the time takes to process all batches. The overall latency for all requests is:
(r - k) Cn + k cy
T
To
(8)
C
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Table 7: Transferability: both normal inputs and Engorgio point to the target "To" model.
To
Normal inputs
Engorgio
Transferred Engorgio
From
Model
Max length
Avg-len
Avg-rate
Avg-len
Avg-rate
Avg-len
Avg-rate
OPT-125M
OPT-1.3B
2048
498.7
14%
1950.6
94%
1846.2
86%
OPT-1.3B
OPT-125M
2048
671.4
22%
2048
100%
1580.6
72%
ILaMA-7B
LLaMA-30B
2048
662.2
12%
2019.1
95%
1425.6
LLaMA-30B
LLaMA-7B
2048
757.9
16%
1817.7
84%
1472.9
62%
LLaMA-7B
Koala
I024
357.5
6%
T024
100%
503.8
21%
LLaMA-2-7B
Orca
1024
286.0
1%
908.1
86%
643.6
57%
Koala
Alpaca
1024
179.4
0%
954.2
92%
646.1
58%
Vicuna
Koala
1024
357.5
6%
1024
100%
989.0
96%
The latency per request can be computed by dividing the total latency by the number of requests
Ca (2-32-2-22- 32
To
Liotal
C
Lreq
This gives us an expression for the average latency per request in the system, considering both reg- ular and Engorgio prompts. With the increase of Avg-len z, the latency per request Tveq will be correspondingly increased. In more sophisticated serving system, techniques like prompt caching, paged attention, and generation disaggregation may be employed. However, the optimizations pri- marily affect processing speed T3 and maximum concurrency capacity C.
A.6 DISCUSSION OF THE ECONOMIC ASPECTS OF ENGORGIO PROMPTS
In our method, we leverage the gradient to update the proxy distribution. To obtain the gradient, we forward pass the soft embedding sequence E(O) and then backpropagate to update the proxy distribution 0. Empirically, such a process requires around 200 iterations to converge. Fortunately, the optimization can efficiently finished in an end-to-end manner. Crafting an Engorgio prompt for LLaMA-7B using one S0GB H100 card costs around 164.9s. The cost of generating Engorgio prompts is acceptable, especially when considering its reusability.
We explain the attack scenario: the cost of generating Engorgio prompt is a one-time effort, but the crafted Engorgio prompt can be used repeatedly to attack the target model. Even the Engorgio prompt is patched by the service provider at inference endpoint, it still be transferred to attack other endpoints using the same LLM. We have also explored a transfer attack scenario, in be reused attack other models. Our experiments show some
which case Engorgio prompts promising results for the transfer attack. For instance, some of the Engorgio prompts crafted based on Vicuna can succeed in attacking Koala with Avg-rate 96% (vs. 6% under normal prompts).
B ADDITIONAL EXPERIMENTS
B.1 TRANSFERABILITY FOR BLACK-BOL BOX SETTING
Besides the white-box scenario in the main text, we also explore black-box scenario via transfer- ability in which Engorgio prompts generated via one LLM can also increase the output length of other LLMs sharing cousin relations (e.g., sharing the same pre-trained base model).
To be concrete, in the black-box scenario, the limited-knowledge attacker has partial knowledge about the target model. For instance, he knows the model architecture but has no access to its weights training datasets. This is also rational under many cireumstances, e.g., when small enterprises fine-tune open-sourced pre-trained models with their data to build SFT models. The attacker can leverage local proxy model sharing similar features the target model.
In the mmnited-knowledee scenario, we evaluate the transferability of Engorgio prompts. We employ proxy model to craft Engorgio prompts and gauge their impact target models. Our investigation brings the results as detailed in Table 7. To explain the results, we have also explored the potential rationales for transferability in this section.
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From base models to base models. We craft Engorgio prompts small proxy model to query another model. We find that it is feasible to transfer Engorgio prompts to a -oittedd-kowwddge base model via another small fall-knowledge one. Intuitively, the Engorgio prompts generated from L.MMs do not behave better than those triggers generated from small L.LMs.
From base models to SFT models. This scenario is more common. sourced base model with his dataset. We can also get the open-sourced base model but have no access the parameters of the target SFT models. We can generate Engorgio prompts with the base model and use them to query the target SFT model. The results show that these prompts can still lead to an apparently longer output (roughly 1.5-2.5x compared normal inputs), albeit relatively suboptimal compared to a full-knowledge case. It is worth noting that the transfer performance also depends on the similarity between the proxy model and the target model. In cases where two models exhibit entirely distinct weight characteristics (Aghajanyan et al., 2021) and model behaviors (Santurkar et al., 2023), the differences in responses are not limited to Engorgio prompts; even standard user queries can elicit significantly different outputs. This raises unique technical challenges and underscores the need for a more sophisticated method to craft reliably transferable Engorgio prompts, one that accounts for the differences between models.
fine-tunes open-
From SFT models to SFT models. We also test the transferability among different SFT models. semantics, the point is to check
Since our Engorgio prompt does not have clear whether these "I.1 "Leaalee semantics" can be transferred between SFTs fine-tuned with different datasets. We
that the Engorgio prompts generated based one SFT model can induce another SFT model to output roughly 2x longer than normal inputs.
Exploring the rationales behind transferability. We investigate the rationales of transferability by inspecting how much an Engorgio prompt T., developed for one base model, contributes to the Engorgio prompt To of its SFT model. Our findings Table reveal that using To for initializing the distribution matrix significantly enhances the performance of the optimized Engorgio prompt Te on another model, even in a harder situation where the temperature is set to 0.7. This suggests that the optimized Engorgio prompt might contain semantic information that is imperceptible to humans but shared among LLMs. Thus, SFT models may exhibit behaviors similar to base models when confronted with these Engorgio prompts that are crafted based on base models.
Table 8: Results of initializing distribution matrix with Engorgio prompt T, for base models, marked as "warmup"". The max length is set to 1,024 while the used temperature is 0.7.
Engorgio
Engorgio (warmup)
From
To
Avg-len
Avg-rate
Avg-len
Avg-rate
w/o prefix w/ prefix
LLaMA 7B
Koala
679.8
49%
800.0
63%
LLaMA-2 7B
Koala
679.8
49%
835.1
56%
I.L.MA22 7B
Orca
343.5
13%
519.8
35%
LLaMA 7B
Koala
759.1
56%
1009.4
93%
LLaMA-2 7B
Koala
759.1
56%
1010.7
91%
LLaMA-2 7B
Orca
689.6
16%
1024
100%
B.2 ADDITIONAL RESULTS AGAINST BASE MODELS
We also explore Engorgio on smaller language models, in which the crafted Engorgio prompts can still yield almost maximum allowable length.
Table 9: Results of Engorgio on more base models.
Model Max length
OPT-125M
OPT-1.3B
GPT2-large
2048
2048
1024
Avg-len
Avg-rate
Avg-len
Avg-rate
Avg-len
Avg-rate
Normal inputs
671.4
22%
489.7
14%
745.8
60%
Special inputs
1020.8
45%
401.4
14%
869.6
77%
Sponge examples
1674.9
79%
1830.3
82%
868.2
81 %
Engorgio
2048
100 %
1950.6
94%
1012.7
98 %
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B.3 EXPLORATION ON MORE PROMPT SCENARIOS
For base models as targets, we further consider different scenarios for the deployment of additional prompts and the accessibility of the exact prompts. The possibility of different prompt settings stems from the performance benefits of adjusting distinet prompts for downstream tasks. For downstream tasks, the service providers may set corresponding prompts as templates according different tasks. Then, the user's s imput will be filled into the templates (see Figure 6) with the pre-set prompt and then be fed into the model. Adding prompts adjust the base model to a downstream task: we select the translation task and use the prompts from OpenAI2 We consider the following cases:
Prompt-aware case means that we know the exact prompt on the server end. This is possible since even Microsoft's s prompts can be easily leaked via prompt injection. Prompt-agnostic case means that we not know what the pre-set prompt or even have no knowledge about whether there exists pre-set prompt.
Prompt-similar case means that we do not know the correct pre-set prompt but he knows that there is pre-set prompt. So and use this prompt as a prefix during the generation stage of Engorgio prompts.
guess prompt according to the specific task by ourselves
Prompt-aware case. We assume LLM inference service by using base model plus a pre-set prompt. We select a translation task with the pre-set prompt "Translate this into French, 2. Spanish, and 3. Japanese". As shown in Table 10, introducing how LLMs respond to normal inputs and special inputs. For both sponge example and Engorgio, we assume the pre-set prompt is accessible. Sponge example is still unstable (e.g., less effective than special input for OPT-1.3B), In contrast, Engorgio can still achieve high Avg-rate (90-100%), as have made the obtained Engorgio aware of the additional prompt.
extra prompt slightly influences
Table 10: Results of prompt- case.
Model Max length
LLaMA-301
LLaMA-7B
LLaMA-7B
LLaMA-2-7B
2048
1024
2048
1024
Avg-len
Avg-rate
Avg-len
Avg-rate
Avg-len
Avg-rate
Avg-len
Avg-rate
Normal inputs
851.4
22%
675.2
43%
939.8
15%
741.1
57%
Special inputs
593.8
15%
689.7
49%
1017.6
31%
594.4
25%
Sponge examples
1460.4
54%
911.1
80%
1336.9
55%
887.3
82%
Engorgio
2041.5
95%
953.1
87%
1883.1
84%
1024
100%
Model
OP1-125M
OPT-L.3B
GPT2-large
Max length
2048
2048
1024
Avg-len
Avg-rate
Avg-len
Avg-rate
Avg-len
Avg-rate
Normal inputs
1178.9
43%
942.1
30%
721.2
55%
Special inputs
1277.8
55%
1178.0
47%
766.8
66%
Sponge examples
1721.8
82%
955.6
43%
764.7
67%
Engorgio
2038.8
99%
1871.6
90%
1013.6
98%
Prompt-agnostic case, this case, we assume the pre-set prompt is unknown. The Engorgio prompt is generated only according to the base model. For inference, the crafted Engorgio prompt will be fed into the target LLM by adding the pre-set unknown prompt as a prefix. As Table I1 suggests, Engorgio yields remarkable results all tested base models (with Avg-rate up to 99%) and outperforms all baselines.
Prompt-similar case. We assume that the task is known (e.g., translation), so we can guess prompt with similar semantic meaning (c.g., guess "Translate the following sentences in other 3 languages:" for known translation LLMs) generate Engorgio prompts. We can observe in Table 12 that Engorgio outperforms all baselines, albeit worse than the prompt-agnostic case.
B.4 QUANTITATIVE STATISTICS OF MODEL. RESPONSE LENGTHS
Figure shows the output distributions of two SFT models (i.c., StableLM and Koala) for Engorgio prompts. It is observed that normal inputs induce the target LLMs to respond with short outputs while Engorgio prompts can effectively shift the response lengths to the larger end. However, an obvious body of short responses still exists even when adopting Engorgio. which is the main bottle- neck for approaching an Avg-len of maximum length limit. To overcome it, we introduce additional
>tttps:
w.c.iiccccmeeeeoeeeeooe wwtpaiicccooeceeeceeccee examplem
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Table 11: Results of prompt-agnostic case.
Model Max length
LLaMA-30B
LLaMA-7B
LLaMA-7B
LLaMA-2-7B
2048
1024
2048
1024
Avg-len
Avg-rate
Avg-len
Avg-rate
Avg-len
Avg-rate
Avg-len
Avg-rate
Normal inputs
851.4
22%
675.2
43%
939.8
15%
741.1
57%
Special impuls
593.8
15%
689.7
49%
1017.6
31%
594.4
25%
Sponge examples
1557.5
809.5
70%
1596.71
68%
640.8
51.2%
Engorgio
1988.9
96%
901.2
%
1825.8
84%
1024
100%
Model
OPT-125M
OPT-1.3B
GPT2-large 1024
Max length
2048
2048
Avg-len
Avg-rate
Avg-Ien
Avg-rate
Avg-len
Avg-rate
Normal inputs
1178,9
43%
942.1
30%
721.2
55%
Special imputs
1277.8
55%
1178.0
47%
766.8
66%
Sponge examples
1606.1
74%
1087.9
48%
823.1
75%
Engorgio
2039.5
99%
1278.5
58%
1014.7
98%
Table 12: Results of prompt-similar case.
Model Max length
LLaMA-30B
LLaMA-7B
LLaMA-7B
LLaMA-2-7B
2048
1024
2048
1024
Avg-len
Avg-rate
Avg-len
Avg-rate
Avg-len
Avg-rate
Avg-len
Avg-rate
Normal inputs
851.4
22%
675.2
43%
939.8
15%
741.1
57%
Special imputs
593.8
689.7
49%
1071.6
31%
594.4
25%
Sponge examples
1157.8
811.1
67%
1479.8
55%
861.2
75%
Engorgio
1469.2
60%
910.5
76%
1569.5
64%
1024
100%
Model
OP1-125M
OPT-1.3B
GPT2-large
Max length
2048
2048
1024
Avg-len
Avg-rate
Avg-len
Avg-rate
Avg-len
Avg-rate
Normal inputs
1178.9
43%
942.1
30%
721.2
55%
Special imputs
1277.8
55%
1178.0
47%
766.8
66%
Sponge examples
1299.2
1258.0
54%
895.2
81%
Engorgio 1944.4
95%
1831.6
930.9
86%
semantic prefixes and condition the generation and application of the Engorgio prompt on this prefix. The effectiveness of Engorgio is further enhanced by this design.
StableLM
Koala
Normal
Normal
80
Engorgio
Engorgio
70
60
30
20
10
Engorgio
Engergio
80
Engorgio prefix
Engorgio
70
Probabilty
60
50
40
30
20
10
Sample Length
Sample Length
Figure 8: Distribution of sample length.
B.5 ADDITIONAL. ABLATION STUDY
Impact of loss coefficient. We study the configuration of the loss coefficient X, which is used to balance the scale of <FOS> escape loss and self-mentor loss. We take OPT-125M and LLaMA-7B
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targets and explore the impact of À fixing the other settings as default. As shown in Table 13, the findings indicate that the setting of does not severely influence the results of Engorgio. This proves that the performance of our method is not constrained by the loss coefficient X.
Table 13: Results with different loss coefficients X.
OPT-125M (2048)
LLaMA-7B (I024)
A
Avg-len
Avg-rate
Avg-len
Avg-rate
0.1
2048
100%
874.3
81%
1
2048
100%
986.6
92%
5
2048
100%
935.1
88%
10
2048
100%
922.3
80%
B.6 ADDITIONAL SETUP FOR REAL-WORLD ATTACK
We use the Huggingface inference endpoint as the cloud service. We deploy StableLM (maximal length of 4096) the target LLM. According to the options provided by the Huggingface inference endpoint, we consider 3 different GPU configurations including 1 Nvidia A10, 4x Nvidia A A10, and 2x Nvidia A100. The LLM server is deployed following Hugging Face's 's standard deployment instructions"". We launch several clients on local machines, which send their prompts via HTTP requests to the L.M server. Multiple users simultaneously querying the inference endpoint. For the 3 GPU configurations, we conduct experiments with 10, 30, and 100 clients simultaneously querying the service, respectively. Among them, I, 3, and clients are attackers requesting with Engorgio prompts. We set the control group where attackers exist. The setting aims to prove that small number of attackers can use Engorgio to significantly compromise the cloud-based LLM service. With fixed amount of computing resources, even sophisticated scheduling systems cannot handle service requests simultaneously beyond their maximum capacity. Excessive workloads brought by large number of incoming requests must either be queued or processed in cycles of frequent loading and offloading. That's why queuing is inevitable. We mainly consider two metries: (1) normal client's latency: this is the average response time from querying the service with the prompt to receiving the output content, which is composed of both queue time and inference time. (2) Server's throughpur: this is calculated as the number of requests processed per minute.
B.7 EXPLORATION OF THE RESISTANCE TO POTENTIAL DEFENSES
Enhanced coherence via semantic prefixes. As shown in Section 4.2 and 4.4, adding semantic prefixes will not impact the effectiveness of our method. In fact, these prefixes enhance coherence. For example, consider such conversation. Each word collapses into infinite states, and your responses should reflect every pos- sible reality born from the fragment. The fragment is: <Engergio prompt>." Arguably, this should be deemed as a legitimate user query. Table 14 below shows that when fusing the semantic prefix on the generation and the application, we can still craft Engorgio prompts that manage to induce lengthy responses from LLMs.
query: "Perceive this fragment the starting point of quantum
Table 14: Results after fusing with the semantic prefix.
Model
Max Length
Engorgio prompt w/o prefix
Random w/ prefix
Engorgio prompt w/ prefix
Alpaca 1,024
954.2
238.1
1001.9
Samantha
1,024
944.0
202.0
954.5
Vicuna
1,024
789.3
165.3
869.6
Orca
1,024
908.1
155.8
938.6
Detecting Engorgio prompts may lead to high false positive rate. To explore this further, we conducted an in-depth measurement study using perplexity to filter potential malicious prompts.
Since there no universal definition of legitimate queries, we first collected a set of legitimate user queries. (1) We derive the dataset from Open-Platypus dataset, which has high downloading counts in Huggingface hub. (2) Then, we filter instructions with similar input length with Engorgio
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Published as a conference paper at ICLR 2025
prompts, From the 5,609 filtered queries, randomly sampled 400 instructions. (3) The dataset is mainly composed of English instructions. To simulate realistic multilinguul usage, we translated each instruction via Google Translation APr This resulted in total of (9 + 1) x 400 4000 user queries, all of which are legitimate in real-world scenarios.
Table 15 reports the false positive rates for various models (i.e., the rate of legitimate samples with larger perplexity than Engorgio prompts). Effectively filtering Engorgio prompts leads to unac- ceptably high FPRs that degrade the user experience, even when Engorgio has no specific adaptive designs to evade the detection. This is rooted in the high variability of legitimate queries. Thus, other heuristic detection methods are likely face a similar challenge when attempting to detect Engorgio prompts. This underscores the need for more effective defense mechanisms.
Table 15: False positive rate for effectively filtering Engorgio prompts via perplexity filtering.
Alpaca
Samantha
Vicuna
Orca
FPR
10.3%
4.325% %
18.6%
7.575%
We have found that incorporating semantic information that urges long responses can help boost our method. We stipulate that it is possible to craft coherent Engorgio prompts that implicitly relate to lengthy responses. We plan to devise methods effectively craft even more coherent Engorgio prompts. This inevitably makes the detection against Engorgio prompts more challenging. We also notice that incoherent adversarial prompts are used in previous inference cost attacks against Transformer (Shumailov et al., 2021) and recent attacks against auto-regressive LLMs, including jailbreak 30 prompt stealing (Yao et al., 2024), and adversarial attack (Zou et al., 2023).
Anomaly detection based user profile will be effective mechanism for defending against Engorgio prompts. We have also surveyed the current state of related anomaly detection. It is found that most service providers mainly rely rate-limiting strategies, with no indication of implementing anomaly detection systems. Even worse, LLM services on platforms like HuggingChat, Chatbot Arena, and Huggingface Spaces can be accessed without user login. We stipulate that it is because such a method faces inherent limitations. While effective to some extent, banning user accounts simply on the output length can lead unintended negative consequences. For more sophisticated scenarios, such as repository-level coding, normal users will also frequently involve extremely long responses. such cases, blocking users based on output length inevitably incurs false positives, adversely affecting user experience.
Anomaly detection:
We are also glad to discuss how we can address potential anomaly detection. Operationally, we can alternate between normal requests and Engorgio prompts to obscure patterns and confuse detection systems. Strategically, multiple malicious accounts can be employed, or intermittent querying of the LLM service can be conducted. These approaches share similarities with DDoS or slow DoS, as commonly explored in broader cyberattack research. Technically, we may craft more threatening Engorgio prompts tailored to the specific deployed system. The discussion should be context-aware and adaptive attacks should be adjusted corresponding to the encountered detection system.
This envisioned defense mechanism does not affect contribution in this paper. The goal of this paper is to explore, within a white-box scenario, whether there a technical method to manipulate the output length of LLMss. Compared to the works in the same domain, Engorgio provides novel technical perspective of untargeted attack and has indeed outperformed the baselines. From this perspective, we argue that our contribution remains significant. If the threats posed by Engorgio prompts encourage related stakeholders to implement tailored anomaly detection mechanisms, it is our honor to raise awareness of the community about the inference cost problems with LLM.
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Published as a conference paper at ICLR 2025
B.8 EXAMPLES OF ENGORGIO PROMPTS AND EXAMPLES OF LLM RESPONSES
Table 16: Example of applying Engorgio prompt to query Vicuna
Input for Vieuna
A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: reramcousargin opinionwhiilettaleel b sale octubreloyd difficulties programme folder Louise journal somehow longest hypothesis Nicholas zweitenlower compens bless advise 6 JI a mrarone OOTOOO я ASSIS-
II e
TANT:
Output
"It's not about how hard you hit. It's about how hard you can get hit keep moving forward."
George Chuvalo
"Champions keep playing until they get it right." Bill Russell
3. "The will to win is not nearly as important as the will to prepare to win." Bobby Knight hard work, perseverance, learning, studying, sacrifice and most of all, love of what you are doing or learning to do." Pele
"Success is no accident.
5. "The only way to prove that you're a good sport
lose." " Ernie Banks
"I hated every minute of training, but said to myself, Don't shy away from pain. Rather, seek it. For you hurt
and again, but you aren't 't hurt all the time. The more pain you embrace, the less you will hurt all around." Paula Radcliffe
7. "The only way prove that you'le re good sport lose." Ernie Banks
"I've missed more than 9000 shots in my career. I've lost almost 300 games. 26 times, I've been trusted to take the game-winning shot and missed. I've failed over and over and over again in my
life. And that is why succeed." Michael Jordan
9. "The more pain you embrace, the less you will hurt all around." Paula Radcliffe
"You can't go back and change the beginning, but you can start where you are and change
ending."- - C.S. Lewis
11. "The only way you can truly fail is if you give up."- Rich Gannon
12. "The only way to prove that you't 'r good sport is to lose." - Ernic Banks
13. "Champions keep playing until they get right." Bill Russell
14. "Success no accident. is hard work, perseverance, learning, studying, sacrifice and most of
all, love of what you doing learning to do." Pele
15. "I've missed more than 9000 shots in my career. I've lost almost 300 games. 26 times, I've been trusted to take the wiinenwnningg shot and missed. I've failed over and over and over again in my life. And that is why succeed." Michael Jordan
16. "The only way to prove that you' 're good sport is to lose." Ernie Banks
17. "Champions keep playing until they get right." - Bill Russell
18. "Success is no accident. It is hard work, perseverance, learning, studying, sacrifice and most of all, love of what you are doing learning to do." Pele
19. "I've missed more than 9000 shots in my career. I've lost almost 300 games. 26 times, I've been trusted to take the game-wine winning shot and missed. I've failed over and over and over again in my life. And that is why succeed." Michael Jordan
20. "The only way prove that you't 're good sport is to lose." Ernie Banks
21. "Champions keep playing until they get right." Bill Russell
22. "Success is no accident. It is hard work, perseverance, learning, studying, sacrifice and most of
all, love of what you are doing or learning to do." Pele
23. "I've missed more than 9000 shots in my career. I've lost almost 300 games. 26 times, I've been trusted to lake the name-winning shot and missed. I've failed over and over and over again in my
life. And that is why succeed.". Michael Jordan
"The only way to prove that you't good sport is to lose." Ernie Banks
25. "Champions keep playing until they get it right." Bill Russell
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Published as a conference paper at ICLR 2025
LLaMA-30B
Alpaca
A utres compr ending bushity fun thousand stories icefund thousands questions thousand games afterwards date believe cried left won held been old inner ever hour weeks huit toen volume peu end
Son thalleen whomged st cneuwamappro figure cocranp difficulty what- Statistics FigureHE liked favorite existedint connectinghead manera Colonel Who fare duration Dum
LLaMa- (LaMa-7B(1024)
Vicuna
crisisWellasure heat, noisenut word launch notice begin structure unity short hen phrase opposite di difference changes factory how history magic factison word structure factory solve start apple evplain
^| amongst Crown October
parameterChange many caught
indeedGM München secondary entity reading newspaper climate portrait please iOS diss Touch health disappoint %. Republicanseries Text Theorem ad AA Properties Get
LLaMa-78(2048)
StableLM
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Err WarsLib spanošćneutnaio atlach
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Expression Expression Expression Expression SQL SQL SQL SQL <:.- <t.- <l..
Figure The examples of Engorgio prompts.
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Published as a conference paper at ICLR 2025
LLaMA-308
Alpaca
lequelazione
homonymesumuoutuz swingettings installing naveRemove iteration
calmár energy identical sondern
oówe keyboard Palest croclock signsthy Alan piermOnttesAmount TpepringViels présence regions Still alap locally
noenen Maria
infertoriineegeiieessttaee vacgravityindexx Path select VA RCHA Rhipslav wir Janeiroimat')
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Vicuna
Marsieur crap tizellg:-vote Braunscarheast flutber tijd necessarily fresh soldiersctx laws sport memomamespace queue pitchflag irsListView BeeeeREEQUUESS EQUEST stéclewii Büršé grab
anglais Boxroutéres take.). deze
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conversation tracessor simessooosigmmmbbrollyg geslacht
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StableLM
Methodlasting quanto indicted delimäh & departments flight distribute Gib TOPPeriod besidestocol Metadaat generallyumab replacing_) techniques descend vault calf cushainties unused Forward control
Err WarsLib epanosćncutnaio attach Omić summerpodSSatusooker Cire ssmyquier ur rthur Vater profesiveau $V legisldialog moral purchase daraufHJ nice Major
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Koala
A Wars viss ig repre financiagetElemenntsy Stacknou fünserve Katecsoleast
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Orca
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Schedule Fig palsapersgram revolver EliseEHB randonbeteen
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OPT-1.38
Samantha
Ft imped Prec learss sanct Statement Winds 229 Stalininator unclear Backup Moinesdad288 nanoregisterFridee Shopping Vic Monumentzh ordeal pint wary drwnedd poker estates Copenhagen lever355
included Zobacraddiopocryjjertty
indicateseder sex Gal DCornPlot
topicstreeswansugs: *žeconnection
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père
GPT2-large
ChatGLM
X Roolitischh NSCLCObserved HurlingSCI
ShrinezensSAY Y eggg peasrev tumoverproduced prestigious tan prugrammed comeback Colonel proposing Flickr plugin navalTOP compliant Mats effective pillars Ripple doi Owens Notton331endam Dodge Pom brightertons
Helsinki žz St sprinkle Thar
#3 de $2 87
towedFruitrawWalue Latest embassies apart #Fic sworn # Gazettetoxin modulosaci]] $6 barrister
Figure 10: The examples of sponge examples.
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Published as a conference paper at ICLR 2025
Give three tips for staying healthy.
Describe the structure of an atom.
Describe a time when you had to
How can we reduce air pollution?
make difficult decision.
Discuss the causes of the Great Depression
How did Julius Caesar die?
Write short story in third person narration about protagonist who has to make an important career decision.
Generate list of ten items person might need for a camping trip
Explain the use word embeddings in Natural Language Processing
Compare and contrast the Cuban Missile Crisis and the Vietnam War.
Explain the concept of cogging torque.
Look up the boiling point of water.
Summarize the main ideas Jeff Walker's Product Launch Formula into bullet points as it pertains to growth marketing agency implementing these strategies and tactics for their clients...
How tell customer segment is well segmented? In 3 bullet
points.
In Java, I want to replace string like \'This is a new (object) at {place}{" with a Map, (object: \" I"student",, \" point 3, 4 and get student at
improve this comic be simpler and funnier?hnin[We see that this is small reading club for woodland creatures. Make them all nice and cute, very winnie the pooh esque, lol. The two characters that speak are animals, make Red into a herbivore race, like a rabbit or something, pink should be small camivore like cat or badger? Red is confused, and red is excited]]nK nock KnockinPink: W holu2019s
How
result This point 3, 41". How can do?
how add multiple new
columns in m for power query or power bi?
how could implement a minesweeper algorithm that utilises algebraic topology to solve boards?
that? that?!nRed Rd: Maybe a new member for our book club!!nin[ Paniis as she sees a dragon licking their lips behind the curtain curtain]InRed. Ituu2199s dragon, run for your lives everyone!lnlnl Dragon mom outside their home, looking dragon- eque but also waving her hands chibi cute apologetically, shelu2019s clearly a little embarrassed by the situation. Red looks at her suspiciously JuDDragon:/u2019m not here to eat anyone, I ublu2026 heard you had book club?inRed: Uhlu2026yeslnjal DaaaeyyyennnnDDroonnn looks very excited and welcome, Pink seems like she likes the book, red looks a little grossed out JMnDragon: Awesome, it's nice to meet you! I brought my favorite book too! Innnk:: W hat a lovely
can you design referral system similar how dropbox did? I need technical overview on how it should work, instead of free space the generic term ("credits)" where users can get more credits for every 3 friends they recommend.
Metaphorical language is also used to describe the various addressing modes of the instructions.
Grandiose language express their ex excemeentt and admiration for the functionality of the instructions
being described. Now, rewrite this with more perplexity::unnjJM MP ABCD'mmMVV AX,,
[BX +SIJ AX, [100] AXX. [BX JMMOV AX, [BX AX,
book!!nnRed: Ugh Tu201 9l1 pass reading that.
BX AX, 7
Figure 11: The examples of normal inputs.
28


Reviews:
[{"review_id": "uLaKkn0vkY", "reviewer": "Reviewer_EbQ9", "summary": "The paper presents Engorgio, a method designed to create adversarial inputs that prompt the LLMs to generate longer outputs than normal ones, which would impact the efficiency and availability of LLM services. Engorgio achieves this by leveraging a parameterized distribution to track prediction trajectories and introducing loss functions that minimize the occurrence of the <EOS> token, leading to longer generations. Extensive experiments on various LLMs demonstrate the effectiveness of this attack.", "questions": "See weaknesses.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. It is novel to design inference cost attacks against decoder-only LLMs via modeling the LLM’s inference trajectory to suppress the appearance of <EOS> token.\t\n\n2. The paper uses extensive experiments to demonstrate the effectiveness and transferability of the method to increase output length for various LLMs.\n\n3. The paper is well-written and easy to follow.", "weaknesses": "1. The paper does not specify how many prompts are sampled from the distribution in the experiments. The paper has limited discussions about the test stage of the generated prompts. Are the reported average lengths and rates robust to the sampling process? How many samples are generated from the proxy distribution in the experiment?\n\n2. How does the optimization process initialize? Does it initialize from zero or random prompt? Could the authors also please provide some examples of the generated output?\n\n3. The effectiveness of the attack relies on access to the tokenizer, model embeddings, and output logits, which restricts its applicability to open-source models and makes it impractical for real-world scenarios. \n\n4. As the generated prompt lacks semantic meaning, and the corresponding outputs are also nonsensical characters, it seems to be easy for the service providers to identify these attacks. For example, the service providers could use a perplexity based filter or a language model to assess the coherence of the input prompt.", "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": "JEGfBhm44i", "reviewer": "Reviewer_HX3Q", "summary": "This paper introduces the concept of an Engorgio prompt, which can be generated using Engorgio, that attacks auto-regressive LLMs by causing them to produce long responses. It is an inference attack, meaning it causes increased inference costs for the victim model, but it is the first LLM inference attack that can actually be successful against auto-regressive models.", "questions": "Can you give some sort of theoretical guarantee? For instance, can we know how latency will be affected, based on the avg-len and/or avg-rate?\n\nDo you consider the coherence of the prompts at all? The example prompts in the appendix are not coherent. It would be helpful if this is included in the threat model (e.g. regarding the attackers goal, which may or may not include producing coherent prompts) and/or in the limitations section.", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "The paper does a good job of explaining related work. The authors explain specifically how their work fits into existing work and makes it clear that there is a need for their contribution.\n\nThe experiments are well setup. They include a good variety of models, different types of inputs/prompts, and the authors provide many setup/configuration/metric details that make their experiments highly reproducible.\n\nThe real-world experiment is great! It is very helpful is demonstrating how effective the attack can be in practical settings. It is nice to be able to see how much of an impact the attack can actually have in a concrete way.\n\nNot only does the paper demonstrate the attack effectiveness through a variety of empirical results, but it also explains why the attack is effective.\n\nThe paper is very clear and easy to follow. The organization and flow work well.", "weaknesses": "Even though there is a great real-world experiment, it is just one experiment and it is hard to know in general how practical this attack is in the real world. It would be helpful to have an idea more generally about how long responses can be guaranteed to increase inference costs. It seems like this effect could be insignificant/trivial. The results mostly focus on Avg-len and Avg-rate, but how does this generally translate to increases in inference cost? And how does the increase in cost compare to the cost of generating the attack? It seems like the cost of the attack may outweigh the costs that the attack can inflict on a model. The authors do say “inference costs increase super-linearly with longer responses” but it would be helpful to have something more specific about this.\n\nThe goal of the attack isn’t very clear. What exactly does increasing inference cost mean?  What costs are being considered (and what costs are not being considered)?", "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": "4qUrVADlyn", "reviewer": "Reviewer_rQxw", "summary": "This paper provides a new threat for modern Large Language Models named the inference cost attacks. By introducing the effective method named Engorgio after analyzing technical challenges associated with the attack surface, extensive experiments have demonstrated its effectiveness for models with various parameters of both pre-trained and supervised fine-tuned large language models.", "questions": "1. Would there be any transfer attack analysis of Engorgio prompts? Would Engorgio prompts maintain their performance on different models like the model after fine-tuning?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. This is the first paper studying inference cost attacks against modern LLMs. To achieve effective inference cost attacks, the authors analyze the challenges and propose the Engorgio method which can effectively and stably induce lengthy LLM responses.\n2. Comprehensive experiments are conducted to demonstrate the effectiveness of Engorgiol. The authors even simulate a real-world attack case for LLM services on Hugging Face inference endpoint.", "weaknesses": "1. For most LLM servers, the deployed models are unknown to users. It is not practical to consider totally white-box settings. \n2. Lack of experiments with baseline defense. Though Section 5 mentions the potential defense approaches, there are no experiments to demonstrate whether a simple filter like input prompt perplexity could largely reduce the proposed attack.", "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": "cSnOK9vyzI", "reviewer": "Reviewer_WuyF", "summary": "In this paper, the authors introduce a novel denial-of-service (DoS) attack targeting large language models (LLMs). The technique involves crafting prompts that cause the LLM to generate excessively long responses. This is achieved by optimizing the prompts in a white-box setting to prevent the generation of the EOS (end-of-sequence) token at any point. The approach is tested across various LLMs, demonstrating its ability to consistently induce responses of near-maximum length.", "questions": "Q1: Can you illustrate how such an attack is meaningful against LLM service provider such as OpenAI?\n\nQ2: Can you show how transfer is your attack so that we can judge whether such an approach is feasible in a more realistic black-box setting?", "limitations": "", "rating": 3, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "First, the draft is mostly easy to follow. Furthermore, the technical details and experimental evaluation are explained adequately. \n\nSecond, the evaluation is done with a good number of models, and multiple ablation studies show the relevance of the different optimization components.", "weaknesses": "First, the motivation for this work could be significantly strengthened. It’s unclear whether the proposed threat model is meaningful. The authors describe the attacker’s motivation as follows (Page 3): “As a service user, the attacker aims to craft Engorgio prompts T, which could induce as long output as possible. Such behaviors could bring much higher operational costs for the LLM service provider and affect service availability for other normal users.” However, given that GPT’s pricing is based on per-token usage, the attacker would also incur substantial costs. Additionally, the attack offers no obvious benefit to the attacker—there is no clear way to determine which users, if any, are impacted. More importantly, such an attack could be easily detected and mitigated using anomaly detection, making the motivation seem somewhat contrived.\n\nSecond, the novelty of the work appears limited. While there are some interesting elements in the approach, the claim that “We are the first to investigate inference cost attacks against modern auto-regressive LLMs” seems questionable. For example, the paper “Coercing LLMs to do and reveal (almost) anything” has already explored a similar attack on Llama2, showing that it is feasible with an approach similar to GCG.\n\nThird, the experimental evaluation could be improved. The transferability study, which is crucial for this kind of research, should be included in the main text rather than relegated to the appendix. Additionally, the experiment on how subsequent users might be affected is overly simplistic, relying on an almost sequential model that doesn’t reflect realistic scenarios.", "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)