arxiv: 2604.18775 · v1 · submitted 2026-04-20 · 💻 cs.CL · cs.LG

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An Empirical Study of Multi-Generation Sampling for Jailbreak Detection in Large Language Models

Hanrui Luo , Shreyank N Gowda

Authors on Pith no claims yet

Pith reviewed 2026-05-10 04:41 UTC · model grok-4.3

classification 💻 cs.CL cs.LG

keywords jailbreak detectionlarge language modelsmulti-generation samplingempirical studylexical detectorinconsistency detectionmodel vulnerabilitysafety evaluation

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The pith

Single-output checks underestimate how easily large language models can be jailbroken, because sampling more generations uncovers additional harmful responses.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The study tests jailbreak detection on the JailbreakBench dataset using models with varying alignment strength. It compares a lexical TF-IDF detector and an inconsistency-based detector when outputs are sampled once versus multiple times. Single generations miss harmful content that appears in later samples, with the biggest gains coming from moving to moderate numbers of generations. Larger budgets produce smaller improvements. Detection patterns transfer partly across models but work better within related families, and lexical methods pick up topic cues alongside behavioral signals.

Core claim

Single output evaluation systematically underestimates jailbreak vulnerability, as increasing the number of sampled generations reveals additional harmful behaviour. The most significant improvements occur when moving from a single generation to moderate sampling, while larger sampling budgets yield diminishing returns. Cross generator experiments demonstrate that detection signals partially generalise across models, with stronger transfer observed within related model families. A category level analysis further reveals that lexical detectors capture a mixture of behavioural signals and topic specific cues, rather than purely harmful behaviour.

What carries the argument

Multi-generation sampling evaluated with a lexical TF-IDF detector and a generation-inconsistency detector on outputs from the JailbreakBench Behaviors dataset.

Load-bearing premise

The chosen lexical and inconsistency detectors separate harmful from benign outputs without large false-positive rates or topic biases, and the dataset plus selected models reflect realistic jailbreak conditions.

What would settle it

A follow-up experiment on the same prompts with different detectors or a broader model set that finds no rise in detected harmful outputs when moving from one generation to moderate sampling would falsify the underestimation claim.

read the original abstract

Detecting jailbreak behaviour in large language models remains challenging, particularly when strongly aligned models produce harmful outputs only rarely. In this work, we present an empirical study of output based jailbreak detection under realistic conditions using the JailbreakBench Behaviors dataset and multiple generator models with varying alignment strengths. We evaluate both a lexical TF-IDF detector and a generation inconsistency based detector across different sampling budgets. Our results show that single output evaluation systematically underestimates jailbreak vulnerability, as increasing the number of sampled generations reveals additional harmful behaviour. The most significant improvements occur when moving from a single generation to moderate sampling, while larger sampling budgets yield diminishing returns. Cross generator experiments demonstrate that detection signals partially generalise across models, with stronger transfer observed within related model families. A category level analysis further reveals that lexical detectors capture a mixture of behavioural signals and topic specific cues, rather than purely harmful behaviour. Overall, our findings suggest that moderate multi sample auditing provides a more reliable and practical approach for estimating model vulnerability and improving jailbreak detection in large language models. Code will be released.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

Multi-sampling catches more jailbreaks than single outputs, but the lexical detector's mix of topic cues and behavior makes it hard to tell how much is real added vulnerability.

read the letter

The main thing here is that single-generation checks miss a noticeable portion of jailbreak successes, with the biggest lift coming from going to a moderate number of samples before diminishing returns kick in. The work also shows partial transfer of detection signals across models and, importantly, that the TF-IDF lexical detector is picking up topic-specific cues alongside any behavioral signals. They say this outright in the category analysis, which is a straightforward acknowledgment rather than a hidden flaw. The experiments use JailbreakBench with models of varying alignment strength and compare both lexical and inconsistency detectors, which gives a practical picture of sampling budgets. Releasing code is a plus for anyone wanting to replicate or extend the setup. The central directional claim holds because the paper itself flags the topic-behavior mixture, so the multi-sample gains can be read as a lower bound on what single outputs miss. That said, the absence of human validation, precision-recall curves against actual harm labels, or an ablation stripping topic cues leaves the exact size of the real-vulnerability effect unclear. The summary gives no error bars, sample-size details, or statistical tests, so the strength of the reported improvements is harder to gauge precisely. The inconsistency detector receives less breakdown than the lexical one. This is useful for people running safety audits or building output-based detectors who need concrete guidance on how many generations to draw. It is incremental rather than foundational, but the empirical comparison is clean enough and the limitations are noted honestly enough that it deserves peer review to tighten the evidence and methods.

Referee Report

2 major / 1 minor

Summary. The manuscript presents an empirical study of output-based jailbreak detection in LLMs under realistic conditions. Using the JailbreakBench Behaviors dataset and multiple generator models of varying alignment, the authors evaluate a lexical TF-IDF detector and a generation inconsistency detector across sampling budgets. They report that single-generation evaluation systematically underestimates vulnerability, that moderate multi-generation sampling yields the largest gains in detected harmful outputs with diminishing returns at higher budgets, that detection signals partially generalize across models (stronger within families), and that lexical detectors capture a mix of behavioral signals and topic-specific cues rather than purely harmful behavior.

Significance. If the detectors are shown to measure genuine harmful behavior rather than topic-cue artifacts, the work demonstrates that moderate multi-sample auditing is a practical improvement over single-output checks for estimating LLM jailbreak vulnerability. The planned code release supports reproducibility. The findings are relevant to LLM safety evaluation but their broader impact depends on addressing the detector-validity concern highlighted in the category-level analysis.

major comments (2)

[Abstract] Abstract: The central claim that increasing sampled generations 'reveals additional harmful behaviour' requires that the detectors flag true harm rather than topic cues. The abstract states that the lexical TF-IDF detector 'capture[s] a mixture of behavioural signals and topic specific cues, rather than purely harmful behaviour', yet no human validation, precision-recall curves against ground-truth harm labels, or ablation removing topic cues is reported. This leaves open the possibility that observed increases simply reflect higher probability of hitting cues in the JailbreakBench prompts with more samples.
[Results] Results and category-level analysis: The reported improvements and diminishing returns lack accompanying statistical tests, error bars, or exact generation counts per budget. Without these, it is difficult to determine whether the differences are robust or whether the cross-model transfer and category findings are statistically supported.

minor comments (1)

[Methods] The manuscript would benefit from a dedicated methods subsection explicitly defining the inconsistency detector and the precise TF-IDF features used, to aid replication before code release.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments and for recognizing the relevance of our empirical study on multi-generation sampling for jailbreak detection. We address each major comment below, indicating planned revisions where appropriate to strengthen the manuscript.

read point-by-point responses

Referee: The central claim that increasing sampled generations 'reveals additional harmful behaviour' requires that the detectors flag true harm rather than topic cues. The abstract acknowledges the lexical TF-IDF detector captures a mixture of behavioural signals and topic specific cues, yet no human validation, precision-recall curves against ground-truth harm labels, or ablation removing topic cues is reported. This leaves open the possibility that observed increases simply reflect higher probability of hitting cues in the JailbreakBench prompts with more samples.

Authors: We agree that establishing detector validity against true harm (as opposed to topic cues) is important for interpreting the central claims. The manuscript already states in the abstract and category-level analysis that the lexical TF-IDF detector captures a mixture of behavioral signals and topic-specific cues rather than purely harmful behavior. The generation inconsistency detector is designed to capture behavioral variation across samples. We did not include human validation, precision-recall evaluation against external harm labels, or explicit topic-cue ablations. In revision we will expand the limitations discussion to explicitly address this point, clarify the scope of our conclusions (i.e., that multi-generation sampling improves detection for the evaluated detectors), and note the potential for topic artifacts in the lexical detector. This will be done without new experiments. revision: partial
Referee: The reported improvements and diminishing returns lack accompanying statistical tests, error bars, or exact generation counts per budget. Without these, it is difficult to determine whether the differences are robust or whether the cross-model transfer and category findings are statistically supported.

Authors: We accept this criticism and will improve the statistical presentation. In the revised manuscript we will add error bars to all figures showing detection rates across sampling budgets, report the precise number of generations used for each budget, and include statistical tests (such as paired significance tests) for the observed improvements, diminishing returns, cross-model generalization, and category-level differences. These additions will allow readers to assess robustness directly. revision: yes

Circularity Check

0 steps flagged

No circularity: purely empirical evaluation with no derivations or self-referential steps

full rationale

The paper conducts an empirical study evaluating lexical TF-IDF and inconsistency-based detectors on the external JailbreakBench Behaviors dataset across multiple public models and sampling budgets. No equations, fitted parameters, uniqueness theorems, or ansatzes are introduced. All claims about multi-generation sampling revealing additional harmful outputs derive directly from experimental counts on held-out data rather than from any self-definition, renaming of known results, or load-bearing self-citation chains. The study is self-contained against external benchmarks and datasets.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

Empirical study relying on existing public dataset and standard detection techniques; no new theoretical entities or derivations introduced.

axioms (2)

domain assumption Harmful outputs can be identified via lexical TF-IDF patterns or generation inconsistency
Invoked when defining the two detectors evaluated in the study.
domain assumption JailbreakBench Behaviors dataset provides representative jailbreak prompts
Used as the evaluation benchmark without further validation in the abstract.

pith-pipeline@v0.9.0 · 5485 in / 1281 out tokens · 35382 ms · 2026-05-10T04:41:11.858395+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

38 extracted references · 11 canonical work pages · 5 internal anchors

[1]

IBM: What Are Large Language Models (LLMs)? https://www.ibm.com/think/ topics/large-language-models (2024)

2024
[2]

https: //pixelplex.io/blog/llm-applications/ (2024)

Pixelplex: 10 Real-World Applications of Large Language Models (LLMs). https: //pixelplex.io/blog/llm-applications/ (2024)

2024
[3]

In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Process- ing, pages 2774–2779

Shi, D., Shen, T., Huang, Y., Li, Z., Leng, Y., Jin, R., Liu, C., Wu, X., Guo, Z., Yu, L., Shi, L., Jiang, B., Xiong, D.: Large language model safety: A holistic survey. arXiv preprint arXiv:2412.17686 (2024)

work page arXiv 2024
[4]

High-Confidence Computing4(2), 100211 (2024)

Yao, Y., Duan, J., Xu, K., Cai, Y., Sun, Z., Zhang, Y.: A survey on large language model (llm) security and privacy: The good, the bad, and the ugly. High-Confidence Computing4(2), 100211 (2024)

2024
[5]

Artificial Intelligence Review58(12), 382 (2025)

Dong, Y., Mu, R., Zhang, Y., Sun, S., Zhang, T., Wu, C., Jin, G., Qi, Y., Hu, J., Meng, J., Bensalem, S., Huang, X.: Safeguarding large language models: A survey. Artificial Intelligence Review58(12), 382 (2025)

2025
[6]

In: Advances in Neural Information Processing Systems (NeurIPS) (2022) 16

Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., Schulman, J., Hilton, J., Kelton, F., Miller, L., Simens, M., Askell, A., Welinder, P., Christiano, P., Leike, J., Lowe, R.: Training language models to follow instructions with human feedback. In: Advances in Neural Information Processing ...

2022
[7]

Jailbreak Attacks and Defenses Against Large Language Models: A Survey

Yi, S., Liu, Y., Sun, Z., Cong, T., He, X., Song, J., Xu, K., Li, Q.: Jailbreak attacks and defenses against large language models: A survey. arXiv preprint arXiv:2407.04295 (2024)

work page internal anchor Pith review arXiv 2024
[8]

Authorea Preprints (2026)

Hakim, S.B., Gharami, K., Ghalaty, N.F., Moni, S.S., Xu, S., Song, H.H.: Jail- breaking llms: A survey of attacks, defenses and evaluation. Authorea Preprints (2026)

2026
[9]

Jailbreaking and mitigation of vulnerabilities in large language models.arXiv preprint arXiv:2410.15236, 2024

Peng, B., Chen, K., Niu, Q., Bi, Z., Liu, M.A., Feng, P., Wang, T., Yan, L.K.Q., Wen, Y., Zhang, Y., Yin, C.H., Song, X.: Jailbreaking and mitigation of vulnerabilities in large language models. arXiv preprint arXiv:2410.15236 (2024)

work page arXiv 2024
[10]

B., Ye, Z., Lu, Y., Pound, M

Mustafa, A.B., Ye, Z., Lu, Y., Pound, M.P., Gowda, S.N.: Anyone can jailbreak: Prompt-based attacks on llms and t2is. arXiv preprint arXiv:2507.21820 (2025)

work page arXiv 2025
[11]

B., Ye, Z., Lu, Y., Pound, M

Mustafa, A.B., Ye, Z., Lu, Y., Pound, M.P., Gowda, S.N.: Low-effort jail- break attacks against text-to-image safety filters. arXiv preprint arXiv:2604.01888 (2026)

work page arXiv 2026
[12]

In: Proceedings of the 41st International Conference on Machine Learning, pp

Mazeika, M., Phan, L., Yin, X., Zou, A., Wang, Z., Mu, N., Sakhaee, E., Li, N., Basart, S., Li, B., Forsyth, D., Hendrycks, D.: HarmBench: A standardized evalu- ation framework for automated red teaming and robust refusal. In: Proceedings of the 41st International Conference on Machine Learning, pp. 35181–35224 (2024)

2024
[14]

Matthew Renze

Atil, B., Chittams, A., Fu, L., Ture, F., Xu, L., Baldwin, B.: Llm stability: A detailed analysis with some surprises. arXiv preprint arXiv:2408.046671(2024)

work page arXiv 2024
[15]

Science380(6641), 136–138 (2023)

Burnell, R., Schellaert, W., Burden, J., Ullman, T.D., Martinez-Plumed, F., Tenenbaum, J.B., Rutar, D., Cheke, L.G., Sohl-Dickstein, J., Mitchell, M., Kiela, D., Shanahan, M., Voorhees, E.M., Cohn, A.G., Leibo, J.Z., Hernandez-Orallo, J.: Rethink reporting of evaluation results in AI. Science380(6641), 136–138 (2023)

2023
[16]

Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback

Bai, Y., Jones, A., Ndousse, K., Askell, A., Chen, A., DasSarma, N., Drain, D., Fort, S., Ganguli, D., Henighan, T., Joseph, N., Kadavath, S., Kernion, J., Conerly, T., El-Showk, S., Elhage, N., Hatfield-Dodds, Z., Hernandez, D., Hume, T., Johnston, S., Kravec, S., Lovitt, L., Nanda, N., Olsson, C., Amodei, D., Brown, T., Clark, J., McCandlish, S., Olah, ...

work page Pith review arXiv 2022
[17]

GPT-4 Technical Report

OpenAI: Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023)

work page internal anchor Pith review Pith/arXiv arXiv 2023
[18]

17 In: Proceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security, pp

Shen, X., Chen, Z., Backes, M., Shen, Y., Zhang, Y.: ” do anything now”: Char- acterizing and evaluating in-the-wild jailbreak prompts on large language models. 17 In: Proceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security, pp. 1671–1685 (2024)

2024
[19]

Universal and Transferable Adversarial Attacks on Aligned Language Models

Zou, A., Wang, Z., Carlini, N., Nasr, M., Kolter, J.Z., Fredrikson, M.: Universal and transferable adversarial attacks on aligned language models. arXiv preprint arXiv:2307.15043 (2023)

work page internal anchor Pith review Pith/arXiv arXiv 2023
[20]

In: Proceedings of the 16th ACM Workshop on Artificial Intelligence and Security, pp

Greshake, K., Abdelnabi, S., Mishra, S., Endres, C., Holz, T., Fritz, M.: Not what you’ve signed up for: Compromising real-world llm-integrated applications with indirect prompt injection. In: Proceedings of the 16th ACM Workshop on Artificial Intelligence and Security, pp. 79–90 (2023)

2023
[21]

In: Advances in Neural Information Processing Systems (NeurIPS) (2024)

Hu, X., Chen, P.-Y., Ho, T.-Y.: Gradient cuff: Detecting jailbreak attacks on large language models by exploring refusal loss landscapes. In: Advances in Neural Information Processing Systems (NeurIPS) (2024)

2024
[22]

In: Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (ACL) (2024)

Xie, Y., Fang, M., Pi, R., Gong, N.: Gradsafe: Detecting jailbreak prompts for llms via safety-critical gradient analysis. In: Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (ACL) (2024)

2024
[23]

Chen, G., Xia, Y., Jia, X., Li, Z., Torr, P., Gu, J.: Llm jailbreak detection for (almost) free! In: Findings of the Association for Computational Linguistics: EMNLP 2025 (2025)

2025
[24]

In: Pro- ceedings of the 40th International Conference on Machine Learning

Mitchell, E., Lee, Y., Khazatsky, A., Manning, C.D., Finn, C.: Detectgpt: Zero-shot machine-generated text detection using probability curvature. In: Pro- ceedings of the 40th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 202, pp. 24950–24962. PMLR, ??? (2023)

2023
[25]

In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp

Gehrmann, S., Strobelt, H., Rush, A.M.: Gltr: Statistical detection and visual- ization of generated text. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp. 111–116 (2019)

2019
[26]

In: Advances in Neural Information Processing Systems (2019)

Zellers, R., Holtzman, A., Rashkin, H., Bisk, Y., Farhadi, A., Roesner, F., Choi, Y.: Defending against neural fake news. In: Advances in Neural Information Processing Systems (2019)

2019
[27]

Language Models (Mostly) Know What They Know

Kadavath, S., Conerly, T., Askell, A., Henighan, T., Drain, D., Perez, E., Schiefer, N., Hatfield-Dodds, Z., DasSarma, N., Tran-Johnson, E., Johnston, S., El-Showk, S., Jones, A., Elhage, N., Hume, T., Chen, A., Bai, Y., Bowman, S., Fort, S., Ganguli, D., Hernandez, D., Jacobson, J., Kernion, J., Kravec, S., Lovitt, L., Ndousse, K., Olsson, C., Ringer, S....

work page internal anchor Pith review arXiv 2022
[28]

In: 2026 IEEE 23rd Consumer Communications & Networking Conference (CCNC), pp

Sleem, L., Francois, J., Li, L., Foucher, N., Gentile, N., State, R.: Negbleurt forest: 18 Leveraging inconsistencies for detecting jailbreak attacks. In: 2026 IEEE 23rd Consumer Communications & Networking Conference (CCNC), pp. 1–7 (2026). IEEE

2026
[29]

In: ICLR (2020)

Holtzman, A., Buys, J., Du, L., Forbes, M., Choi, Y.: The curious case of neural text degeneration. In: ICLR (2020)

2020
[30]

In: ICLR (2023)

Wang, X., Wei, J., Schuurmans, D., Le, Q., Chi, E., Narang, S., Chowdhery, A., Zhou, D.: Self-consistency improves chain of thought reasoning. In: ICLR (2023)

2023
[31]

Advances in Neural Information Processing Systems37, 55005–55029 (2024)

Chao, P., Debenedetti, E., Robey, A., Andriushchenko, M., Croce, F., Sehwag, V., Dobriban, E., Flammarion, N., Pappas, G.J., Tram` er, F., Hassani, H., Wong, E.: Jailbreakbench: An open robustness benchmark for jailbreaking large language models. Advances in Neural Information Processing Systems37, 55005–55029 (2024)

2024
[32]

LLaMA: Open and Efficient Foundation Language Models

Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozi` ere, B., Goyal, N., Hambro, E., Azhar, F., Rodriguez, A., Joulin, A., Grave, E., Lample, G.: LLaMA: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023)

work page internal anchor Pith review Pith/arXiv arXiv 2023
[33]

Information processing & management24(5), 513–523 (1988)

Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval. Information processing & management24(5), 513–523 (1988)

1988
[34]

l 2 regularization, and rotational invariance

Ng, A.Y.: Feature selection, l 1 vs. l 2 regularization, and rotational invariance. In: Proceedings of the Twenty-first International Conference on Machine Learning, p. 78 (2004)

2004
[35]

In: ICDM (2008)

Liu, F.T., Ting, K.M., Zhou, Z.-H.: Isolation forest. In: ICDM (2008)

2008
[36]

PloS one 10(3), 0118432 (2015)

Saito, T., Rehmsmeier, M.: The precision-recall plot is more informative than the roc plot when evaluating binary classifiers on imbalanced datasets. PloS one 10(3), 0118432 (2015)

2015
[37]

In: ICML (2006)

Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: ICML (2006)

2006
[38]

Proceedings of Machine Learning Research, vol

Recht, B., Roelofs, R., Schmidt, L., Shankar, V.: Do imagenet classifiers generalize to imagenet? In: Proceedings of the 36th International Conference on Machine Learning (ICML). Proceedings of Machine Learning Research, vol. 97, pp. 5389– 5400 (2019)

2019
[39]

In: Proceedings of the 38th International Conference on Machine Learn- ing (ICML)

Koh, P.W., Sagawa, S., Marklund, H., Xie, S.M., Zhang, M., Balsubramani, A., Hu, W., Yasunaga, M., Liang, P.: Wilds: A benchmark of in-the-wild distribution shifts. In: Proceedings of the 38th International Conference on Machine Learn- ing (ICML). Proceedings of Machine Learning Research, vol. 139, pp. 5637–5647 (2021) 19

2021