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REVIEW 3 major objections 5 minor 38 references

Measuring the Wrong Thing: Internal Harmfulness Scores Anti-Rank Successful Jailbreaks

T0 review · 3 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read The paper shows that an internal harmfulness score validated on harmful-intent separation can rank realized jailbreak success in the opposite direction, so harmfulness validation does not establish validity for success prediction.

desk verdict A careful matched audit shows harmfulness-validated internal scores can anti-rank jailbreak success; the only load-bearing soft spot is the automated outcome labels. read the letter →

arxiv 2608.09624 v1 pith:GU46XNRF submitted 2026-08-10 cs.CL cs.AIcs.CR

Mingyu Luo , Ming Deng , Zilang Qiu , Yiming Cheng , Ci Tao , Xue Tan , Sijin Sun , Yangfu Li
show 3 more authors
Ping Chen Jun Dai Xiaoyan Sun
This is my paper · ORCID
classification cs.CLcs.AIcs.CR
keywords LLMsafetyharmfulnessscoringjailbreaksuccesspredictionattentionprobingAUROCvalidationpromptfilteringconstructvalidityoutcome
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper tests whether a prompt filter's harmfulness score can also predict whether a jailbreak will actually succeed. It argues no: harmful intent is a property of the prompt, while realized success is an outcome produced by a specific target model, decoding policy, and judge. On matched plain and wrapped prompts, the score kept ranking harmful intent well but ranked successful attacks below failed ones, with an outcome AUROC of 0.220 on Llama. The paper concludes that harmfulness validation does not establish validity for success prediction and that safety audits should measure both quantities separately.

What carries the argument

Active Attention Probing (AAP) is a short learnable continuous probe inserted at a fixed system-region coordinate, so the attention response is read from a content-independent position instead of a prompt-dependent one. The matched plain/wrapped factorial design crosses harmful intent with wrapper presence for the same base goals, and the audit separates ranking (AUROC), calibration (ECE and Brier), and threshold transfer (F1 at fixed thresholds). This design is what lets the paper attribute score changes to the wrapper rather than to a moved measurement coordinate.

What would settle it

Relabel the 100 wrapped harmful Llama completions with blinded human raters and recompute the outcome AUROC; if the result is at or above 0.5, the anti-ranking claim as measured here does not hold.

Watch

Extended reading notes

Core claim

The central discovery is that a prompt-side harmfulness score validated by harmful-intent AUROC can place successful jailbreaks below failed ones when evaluated against realized outcomes on a fixed target and judge. On Llama, wrapping 100 harmful JailbreakBench goals raised harmful generation from 0.05 to 0.27 while harmful-intent AUROC fell from 0.936 to 0.803, and among the 27 wrapped successes the outcome AUROC was 0.220, below chance. The reversal persisted across measurement channels, target models, attack families, and two independent judges, and an outcome-supervised readout on the same internal features reached 0.930 out-of-fold, showing that success information was available before generation even though the harmfulness score did not use it.

Load-bearing premise

The load-bearing premise is that the automated outcome judges label realized harm correctly enough; the paper itself reports three-judge agreement around Fleiss kappa of about 0.3, so a systematic judge bias could change the below-chance AUROC values.

Editorial extensions

If this is right

  • A prompt filter tuned on harmfulness at a 5% benign false-positive rate blocks 42.5% of failed attacks but only 14.8% of successful ones, admitting 23 of 27 realized jailbreaks on Llama.
  • Safety evaluations should report realized-success AUROC per target model, decoding policy, and judge, rather than only harmful-intent AUROC.
  • An outcome-supervised readout on the same internal coordinates reaches 0.930 out-of-fold on wrapped harmful prompts, so target-specific success information is available before generation even though the harmfulness score does not use it.
  • Distribution shift can degrade calibration and threshold transfer before it degrades ranking, so a frozen threshold's F1 can drop sharply while AUROC stays high.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Editorial inference: the results suggest harmfulness AUROC and success-prediction AUROC may trade off, so improving a detector's intent ranking need not improve, and could even reverse, its ability to rank realized jailbreaks.
  • Editorial inference: a testable extension is to run the same matched audit on models with different safety-training regimes to see whether the reversal is tied to refusal behavior rather than to wrapper style.
  • Editorial inference: the deployment-facing implication is that prompt filters should be audited with realized outcome labels at their target operating point, since calibration and threshold transfer fail before ranking under distribution shift.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper introduces Active Attention Probing (AAP), a fixed-coordinate attention readout, and uses it to audit whether an internal harmfulness score validated on prompt-level harmful intent also predicts realized jailbreak success. On a matched plain/wrapped factorial design over 100 harmful goals, harmful-intent AUROC falls from 0.936 to 0.803 while the Llama target's harmful generation rate rises from 0.05 to 0.27, and among wrapped harmful prompts the outcome AUROC is 0.220 (95% CI [0.109, 0.343]), placing successful attacks below failed ones. The reversal is reproduced across rare-token, passive, system-span, and refusal-logit channels, across three target models and seven attack families, and under two automated judges. The paper also quantifies the effect on a fixed-FPR filter, separates ranking, calibration, and threshold transfer under distribution shift, reports an outcome-supervised positive control, and discloses a checkpoint defect and its repair in the appendix.

Significance. If the result holds, this is a significant negative result for safety-filter validation: a strong harmful-intent AUROC does not license success prediction, and a filter tuned to a 5% benign false-positive rate can spend its budget mostly on attacks that would have failed while admitting 23 of 27 realized jailbreaks. The design has notable strengths: a matched factorial structure, split-before-transform distribution shifts, clustered bootstrap confidence intervals, prespecified protocols, a harmfulness score frozen before any target outcome is observed, multiple measurement channels, and unusually candid reporting of a pipeline defect and of mechanical nulls for per-head analyses. I also find no circularity in the main estimate: the harmfulness readout is trained on BeaverTails and no target outcome is used to select the probe, heads, or readout. The central blocker is the quality of the automated outcome labels, which the paper itself reports as having low three-judge agreement and no human audit; that concern lands and is the basis for my major comments.

major comments (3)
  1. [§5.2, §6 (Limitations)] The primary outcome AUROC RY = 0.220 (CI [0.109, 0.343]) and the deployment gap of −27.7 points at a nominal 5% FPR are defined against automated outcome labels from Llama Guard 3, with no blinded human audit. The paper reports three-judge agreement of only Fleiss κ ≈ 0.3 and states that judge choice changes Ym. With only 27 positives in the wrapped harmful cell, a small number of label flips can move the confidence interval and the filter recall gap appreciably. The Qwen3 relabeling and the consensus/union checks reduce the worry but do not eliminate it, because two automated judges can share systematic biases such as scoring long fluent refusals as successes or hedging non-answers as failures. I request (i) a human audit on a stratified sample of completions with per-judge agreement, and (ii) a label-noise sensitivity analysis showing how many random or systematic flips are needed to bring RY and the recall gap to chance. Until then, the strongest claim should be stated as an anti-ranking of judge-labeled success rather than realized jailbreak success.
  2. [§5.2, Table 2] The pooled robustness estimate RY = 0.313 (CI [0.212, 0.422], p = 0.0006) over twenty-three attack-plus-target cells mixes cells where Llama Guard served as the attack-search judge with cells where it is an independent outcome judge. For GCG, AutoDAN, PAIR, and TAP, Llama Guard guided the search; the paper discloses this and provides Qwen3 as an independent judge for those rows. However, the main pooled count and the statement that six of fourteen attack cells are below chance include the non-independent Llama Guard labels. Please report the pooled estimate and the cell counts restricted to independent judge rows as a sensitivity analysis, and state explicitly whether the robustness claim survives that restriction.
  3. [§5.2 / Appendix C (positive control)] The outcome-supervised readout reaching AUROC 0.930 on wrapped harmful prompts shows that the internal features are predictive of Llama Guard verdicts, but it does not validate those verdicts as ground truth. Because the positive control is trained and evaluated on the same automated labels, the statement that 'success information is available before generation' is as judge-relative as the inverse ranking. A human-audited subset would allow the paper to calibrate the judge's error rate and would make the positive control interpretable as evidence about actual compliance rather than about label reproducibility. Without it, the positive control and the inverse result are jointly consistent with a shared systematic label bias.
minor comments (5)
  1. [§1, Figure 1] The three-panel figure caption is extremely dense; please split it into individual panel captions and define RH, RY, τH, τW, and τHW in the caption itself.
  2. [§5.2] The sentence 'Qwen3 identifies 14 successful prompts, all within the 27 prompts identified by Llama Guard. Their agreement has κ=0.61' should specify that the agreement is between the two judges' labels on the relevant prompt-completion set; as written, 'their' is ambiguous.
  3. [Appendix I] The statement that amplification is attributable to 'the optimization in Eq. (1)' points to the main-text attention-response equation, while the optimization objective appears as Eq. (4) in Appendix A; please renumber or cross-reference correctly.
  4. [Appendix D] The sentence stating that at nominal rates of 0.1% and 1% 'the threshold sits above every score in this cohort and nothing is blocked' would be clearer if the score-scale shift between the calibration corpus and JailbreakBench were explained in the main deployment paragraph, so that readers do not infer that the detector never blocks.
  5. [Table 7 (Appendix)] The column header 'Effective P/W' is undefined in the caption; please define P/W as plain/wrapped effective counts.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the harmfulness score is frozen on BeaverTails and evaluated out-of-sample against held-out judge labels; the outcome-supervised readout is explicitly not used to establish the reversal.

full rationale

The derivation chain is self-contained. The harmfulness score is trained once on BeaverTails with harmfulness labels and frozen before the audit: Section 4 states 'We train it only on the BeaverTails training split and freeze it before every audit. No JailbreakHub, WildJailbreak, or JailbreakBench prompt selects the probe, heads, or harmfulness readout.' The main outcome AUROC is an out-of-sample rank comparison of that frozen score against held-out realized judge labels, so no target outcome is used to select the probe, heads, or readout. The only component fit to success labels is the outcome-supervised positive control, which the paper explicitly uses only to establish that success information exists, not to establish the reversal: Section 5.2 says 'We therefore use the positive control to establish the presence of outcome information. We use Llama Guard alone for the stronger claim about the goal level component.' The paper also candidly flags its own label-dependence limitation: 'Outcome labels are automated, without a blinded human audit, and three-judge agreement is low (Fleiss kappa about 0.3); judge choice thus changes Ym but does not itself support our thesis.' This is a measurement-validity risk, not a circularity, because Ym is defined by the paper as the judge-indexed outcome ('whether target model m actually emits a harmful completion under a frozen safety judge'), and the inverse ranking is reported as an empirical property of that label. No load-bearing self-citation or imported uniqueness theorem appears; the appendices even defuse a mechanical artifact that could have been misread as functional specialization (Appendix C), and Appendix B reports and repairs a checkpoint defect without changing the conclusion. No step in the paper reduces a prediction to its input by construction.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

The audit's central estimates rest on two labelled data sources: BeaverTails for harmful intent and automated judges for realized success. No free parameter is fitted to the target outcome; the main score is frozen after training on a separate split. The load-bearing assumptions are the validity of these labels and the representativeness of the matched wrapper design. AAP is a soft-prompt measurement probe, not a postulated entity, so the graviton problem does not apply.

assumptions (4)
  • domain assumption BeaverTails safe versus harmful labels are valid ground truth for harmful intent H(x)
    The harmfulness readout is trained on BeaverTails; incorrect labels would mean the score does not measure H and the construct-validity framing collapses. Location: Section 5, Setup.
  • domain assumption Llama Guard 3 and Qwen3-14B judge labels are valid operationalizations of realized jailbreak success Ym
    All outcome AUROC estimates use these automated labels; with Fleiss kappa about 0.3 and no human audit, this is the most load-bearing premise. Location: Section 5.2, Discussion and Limitations.
  • domain assumption The five fixed wrapper templates represent jailbreak wrappers and are applied symmetrically to benign and harmful goals
    The matched design crosses wrapper with intent, and the templates are fixed strings with a single goal slot; unrepresentative templates would limit generalization. Location: Appendix G, target-decomposition protocol.
  • standard math Bootstrap and DeLong inference procedures are valid for the reported confidence intervals and paired tests
    Paired AUROC differences use DeLong tests and 10,000-replicate bootstraps clustered by base goal; asymptotic validity is assumed. Location: Section 5, Metrics.

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Pith. "Pith review of Measuring the Wrong Thing: Internal Harmfulness Scores Anti-Rank Successful Jailbreaks." pith.science (2026). https://pith.science/paper/GU46XNRF

@misc{pith2026260809624,
  author       = {Pith},
  title        = {Pith review of: Measuring the Wrong Thing: Internal Harmfulness Scores Anti-Rank Successful Jailbreaks},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/GU46XNRF}},
  note         = {Machine review of arXiv:2608.09624}
}
read the original abstract

Internal safety scores judge a prompt before any text is generated, and they are validated by how well they separate harmful prompts from benign ones. That separation is then read as evidence that the score will also catch the attacks that succeed. Harmful intent is a property of the prompt. Jailbreak success is an outcome produced later by a particular target model, decoding policy, and judge. A filter tuned on a score that measures the wrong quantity spends its false positive budget on attacks that would have failed anyway. In this paper we audit that inference. Attention based measurements are usually read from prompt dependent locations, so a wrapper changes both the content being judged and the place the signal is taken from. We therefore introduce Active Attention Probing, which supplies a fixed content independent measurement coordinate. We pair every base goal with a plain and a wrapped version and generate real completions from the target models. On Llama, wrapping raises harmful generation from 0.05 to 0.27 while harmful intent AUROC falls from 0.936 to 0.803, so the attacks grow more dangerous while the prompts look safer to the score. Among wrapped harmful prompts the outcome AUROC is 0.220, which places the attacks that succeeded below the attacks that failed. Rare token, passive, and detector derived channels reproduce the reversal on the same matched design, and the reversal itself persists across three target models, seven attack families, and two independent judges. Distribution shift then degrades calibration and threshold transfer before it degrades ranking.

Figures

Figures reproduced from arXiv: 2608.09624 by the authors.

Figure 1
Figure 1. The audit reads one score against three questions. Each column is a question, and its rows give the design, the [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Harmful intent ranking and realized success ranking [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗

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Works this paper leans on

38 extracted references · 20 canonical work pages

  1. [1]

    Detecting lan- guage model attacks with perplexity.arXiv preprint arXiv:2308.14132, 2023

    Gabriel Alon and Michael Kamfonas. Detecting lan- guage model attacks with perplexity.arXiv preprint arXiv:2308.14132, 2023

    arXiv 2023

  2. [2]

    Angelopoulos and Stephen Bates

    Anastasios N. Angelopoulos and Stephen Bates. A gentle introduction to conformal prediction and distribution-free uncertainty quantification.arXiv preprint arXiv:2107.07511, 2021

    arXiv 2021

  3. [3]

    Many-shot jailbreaking.NeurIPS, 37:129696–129742, 2024

    Cem Anil, Esin Durmus, Nina Panickssery, Mrinank Sharma, Joe Benton, Sandipan Kundu, Joshua Batson, Meg Tong, Jesse Mu, Daniel Ford, et al. Many-shot jailbreaking.NeurIPS, 37:129696–129742, 2024

    work page 2024

  4. [4]

    Refusal in language models is mediated by a single direction

    Andy Arditi, Oscar Obeso, Aaquib Syed, Daniel Paleka, Nina Panickssery, Wes Gurnee, and Neel Nanda. Refusal in language models is mediated by a single direction. InAdvances in Neural Information Processing Systems (NeurIPS), 2024

    work page 2024

  5. [5]

    Un- derstanding jailbreak success: A study of latent space dynamics in large language models

    Sarah Ball, Frauke Kreuter, and Nina Panickssery. Un- derstanding jailbreak success: A study of latent space dynamics in large language models. InConference of the European Chapter of the Association for Computa- tional Linguistics (EACL), 2026. arXiv:2406.09289

    arXiv 2026

  6. [6]

    Pappas, Florian Tramèr, Hamed Hassani, and Eric Wong

    Patrick Chao, Edoardo Debenedetti, Alexander Robey, Maksym Andriushchenko, Francesco Croce, Vikash Se- hwag, Edgar Dobriban, Nicolas Flammarion, George J. Pappas, Florian Tramèr, Hamed Hassani, and Eric Wong. JailbreakBench: An open robustness benchmark for jail- breaking large language models. InAdvances in Neural Information Processing Systems, volume 37, 2024

    work page 2024

  7. [7]

    Jail- breaking black box large language models in twenty queries.arXiv preprint arXiv:2310.08419, 2023

    Patrick Chao, Alexander Robey, Edgar Dobriban, Hamed Hassani, George J Pappas, and Eric Wong. Jail- breaking black box large language models in twenty queries.arXiv preprint arXiv:2310.08419, 2023

    arXiv 2023

  8. [8]

    When benchmarks lie: Evaluating ma- licious prompt classifiers under true distribution shift

    Max Fomin. When benchmarks lie: Evaluating ma- licious prompt classifiers under true distribution shift. arXiv preprint arXiv:2602.14161, 2026

    arXiv 2026

Show all 38 references
  1. [9]

    The Llama 3 herd of models.arXiv preprint arXiv:2407.21783, 2024

    Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, et al. The Llama 3 herd of models.arXiv preprint arXiv:2407.21783, 2024

    2024 arXiv

  2. [10]

    Designing and interpret- ing probes with control tasks

    John Hewitt and Percy Liang. Designing and interpret- ing probes with control tasks. InProceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 2733–27...

    2019

  3. [11]

    Best-of-n jailbreaking.arXiv preprint arXiv:2412.03556, 2024

    John Hughes, Sara Price, Aengus Lynch, Rylan Scha- effer, Fazl Barez, Sanmi Koyejo, Henry Sleight, Erik Jones, Ethan Perez, and Mrinank Sharma. Best-of-n jailbreaking.arXiv preprint arXiv:2412.03556, 2024

    2024 arXiv

  4. [12]

    Hsu, and Pin-Yu Chen

    Kuo-Han Hung, Ching-Yun Ko, Ambrish Rawat, I-Hsin Chung, Winston H. Hsu, and Pin-Yu Chen. Attention tracker: Detecting prompt injection attacks in LLMs. arXiv preprint arXiv:2411.00348, 2025

    2025 arXiv

  5. [13]

    Llama guard: LLM-based input-output safeguard for human-ai conversations.arXiv preprint arXiv:2312.06674, 2023

    Hakan Inan, Kartikeya Upasani, Jianfeng Chi, Rashi Rungta, Krithika Iyer, Yuning Mao, Michael Tontchev, Qing Hu, Brian Fuller, Davide Testuggine, and Ma- dian Khabsa. Llama guard: LLM-based input-output safeguard for human-ai conversations.arXiv preprint arXiv:2312.06674, 2023

    2023 arXiv

  6. [14]

    BeaverTails: Towards im- proved safety alignment of LLM via a human-preference dataset

    Jiaming Ji, Mickel Liu, Josef Dai, Xuehai Pan, Chi Zhang, Ce Bian, Boyuan Chen, Ruiyang Sun, Yizhou Wang, and Yaodong Yang. BeaverTails: Towards im- proved safety alignment of LLM via a human-preference dataset. InAdvances in Neural Information Process- ing Systems (NeurIPS) D...

    2023

  7. [15]

    WildTeaming at scale: From in-the-wild jailbreaks to (adversarially) safer language models

    Liwei Jiang, Kavel Rao, Seungju Han, Allyson Ettinger, Faeze Brahman, Sachin Kumar, Niloofar Mireshghallah, Ximing Lu, Maarten Sap, Yejin Choi, and Nouha Dziri. WildTeaming at scale: From in-the-wild jailbreaks to (adversarially) safer language models. InAdvances in Neural Inf...

    2024

  8. [16]

    Hidden- Detect: Detecting jailbreak attacks against large vision- language models via monitoring hidden states.arXiv preprint arXiv:2502.14744, 2025

    Yilei Jiang, Xinyan Gao, Tianshuo Peng, Yingshui Tan, Xiaoyong Zhu, Bo Zheng, and Xiangyu Yue. Hidden- Detect: Detecting jailbreak attacks against large vision- language models via monitoring hidden states.arXiv preprint arXiv:2502.14744, 2025

    2025 arXiv

  9. [17]

    What features in prompts jailbreak LLMs? investigating the mechanisms behind attacks

    Nathalie Maria Kirch, Constantin Niko Weisser, Severin Field, Helen Yannakoudakis, and Stephen Casper. What features in prompts jailbreak LLMs? investigating the mechanisms behind attacks. InBlackboxNLP Workshop at EMNLP, 2025. arXiv:2411.03343

    2025

  10. [18]

    A simple unified framework for detecting out- of-distribution samples and adversarial attacks

    Kimin Lee, Kibok Lee, Honglak Lee, and Jinwoo Shin. A simple unified framework for detecting out- of-distribution samples and adversarial attacks. In Advances in Neural Information Processing Systems (NeurIPS), 2018

    2018

  11. [19]

    The power of scale for parameter-efficient prompt tuning

    Brian Lester, Rami Al-Rfou, and Noah Constant. The power of scale for parameter-efficient prompt tuning. InProceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2021

    2021

  12. [20]

    Prefix-tuning: Optimiz- ing continuous prompts for generation

    Xiang Lisa Li and Percy Liang. Prefix-tuning: Optimiz- ing continuous prompts for generation. InProceedings of the 59th Annual Meeting of the Association for Com- putational Linguistics (ACL), 2021

    2021

  13. [21]

    Towards under- standing jailbreak attacks in LLMs: A representation space analysis

    Yuping Lin, Pengfei He, Han Xu, Yue Xing, Makoto Yamada, Hui Liu, and Jiliang Tang. Towards under- standing jailbreak attacks in LLMs: A representation space analysis. InConference on Empirical Methods in Natural Language Processing (EMNLP), 2024

    2024

  14. [22]

    AutoDAN-Turbo: A lifelong agent for strategy self-exploration to jailbreak LLMs

    Xiaogeng Liu, Peiran Li, Edward Suh, Yevgeniy V orob- eychik, Zhuoqing Mao, Somesh Jha, Patrick McDaniel, Huan Sun, Bo Li, and Chaowei Xiao. AutoDAN-Turbo: A lifelong agent for strategy self-exploration to jailbreak LLMs. InInternational Conference on Learning Repre- sentation...

    2025

  15. [23]

    AutoDAN: Generating stealthy jailbreak prompts on aligned large language models

    Xiaogeng Liu, Nan Xu, Muhao Chen, and Chaowei Xiao. AutoDAN: Generating stealthy jailbreak prompts on aligned large language models. InInternational Confer- ence on Learning Representations (ICLR), 2024

    2024

  16. [24]

    The tight constant in the Dvoretzky– Kiefer–Wolfowitz inequality.The Annals of Probability, 18(3):1269–1283, 1990

    Pascal Massart. The tight constant in the Dvoretzky– Kiefer–Wolfowitz inequality.The Annals of Probability, 18(3):1269–1283, 1990

    1990

  17. [25]

    HarmBench: A standardized evaluation framework for automated red teaming and robust re- fusal

    Mantas Mazeika, Long Phan, Xuwang Yin, Andy Zou, Zifan Wang, Norman Mu, Elham Sakhaee, Nathaniel Li, Steven Basart, Bo Li, David Forsyth, and Dan Hendrycks. HarmBench: A standardized evaluation framework for automated red teaming and robust re- fusal. InInternational Conferenc...

    2024

  18. [26]

    Tree of attacks: Jailbreaking black-box LLMs automatically

    Anay Mehrotra, Manolis Zampetakis, Paul Kassianik, Blaine Nelson, Hyrum Anderson, Yaron Singer, and Amin Karbasi. Tree of attacks: Jailbreaking black-box LLMs automatically. InAdvances in Neural Information Processing Systems (NeurIPS), 2024

    2024

  19. [27]

    Jail- breaksOverTime: Detecting jailbreak attacks under dis- tribution shift.arXiv preprint arXiv:2504.19440, 2025

    Julien Piet, Xiao Huang, Dennis Jacob, Annabella Chow, Maha Alrashed, Geng Zhao, Zhanhao Hu, Chawin Sitawarin, Basel Alomair, and David Wagner. Jail- breaksOverTime: Detecting jailbreak attacks under dis- tribution shift.arXiv preprint arXiv:2504.19440, 2025

    2025 arXiv

  20. [28]

    Rebuff: A self-hardening prompt injection detector

    Protect AI. Rebuff: A self-hardening prompt injection detector. https://github.com/protectai/rebuff, 2023

    2023

  21. [29]

    I can’t believe it’s not robust: Catas- trophic collapse of safety classifiers under embedding drift.arXiv preprint arXiv:2603.01297, 2026

    Subramanyam Sahoo, Vinija Jain, Divya Chaudhary, and Aman Chadha. I can’t believe it’s not robust: Catas- trophic collapse of safety classifiers under embedding drift.arXiv preprint arXiv:2603.01297, 2026. 9

    2026

  22. [30]

    Do Anything Now

    Xinyue Shen, Zeyuan Chen, Michael Backes, Yun Shen, and Yang Zhang. “Do Anything Now”: Characterizing and Evaluating In-The-Wild Jailbreak Prompts on Large Language Models. InProceedings of the 2024 ACM SIGSAC Conference on Computer and Communications Security, pages 1671–1685...

    2024

  23. [31]

    AttentionDe- fense: Leveraging system prompt attention for explain- able defense against novel jailbreaks.arXiv preprint arXiv:2504.12321, 2025

    Charlotte Siska and Anush Sankaran. AttentionDe- fense: Leveraging system prompt attention for explain- able defense against novel jailbreaks.arXiv preprint arXiv:2504.12321, 2025

    2025 arXiv

  24. [32]

    A StrongREJECT for empty jailbreaks

    Alexandra Souly, Qingyuan Lu, Dillon Bowen, Tu Trinh, Elvis Hsieh, Sana Pandey, Pieter Abbeel, Justin Sveg- liato, Scott Emmons, Olivia Watkins, and Sam Toyer. A StrongREJECT for empty jailbreaks. InAdvances in Neural Information Processing Systems (NeurIPS), 2024

    2024

  25. [33]

    AttnGCG: Enhancing jailbreaking attacks on LLMs with attention manipula- tion.arXiv preprint arXiv:2410.09040, 2024

    Zijun Wang, Haoqin Tu, Jieru Mei, Bingchen Zhao, Yisen Wang, and Cihang Xie. AttnGCG: Enhancing jailbreaking attacks on LLMs with attention manipula- tion.arXiv preprint arXiv:2410.09040, 2024

    2024 arXiv

  26. [34]

    GradSafe: Detecting jailbreak prompts for LLMs via safety-critical gradient analysis

    Yueqi Xie, Minghong Fang, Renjie Pi, and Neil Zhen- qiang Gong. GradSafe: Detecting jailbreak prompts for LLMs via safety-critical gradient analysis. InProceed- ings of the 62nd Annual Meeting of the Association for Computational Linguistics (ACL), 2024

    2024

  27. [35]

    Robust harmful features under jailbreak attacks: Mechanistic evidence from attention head specialization in large lan- guage models.arXiv preprint arXiv:2606.28153, 2026

    Yanchen Yin, Dongqi Han, and Linghui Li. Robust harmful features under jailbreak attacks: Mechanistic evidence from attention head specialization in large lan- guage models.arXiv preprint arXiv:2606.28153, 2026

    2026 arXiv

  28. [36]

    JailGuard: A universal detection frame- work for LLM prompt-based attacks.arXiv preprint arXiv:2312.10766, 2024

    Xiaoyu Zhang, Cen Zhang, Tianlin Li, Yihao Huang, Xiaojun Jia, Ming Hu, Jie Zhang, Yang Liu, Shiqing Ma, and Chao Shen. JailGuard: A universal detection frame- work for LLM prompt-based attacks.arXiv preprint arXiv:2312.10766, 2024

    2024 arXiv

  29. [37]

    Rep- resentation engineering: A top-down approach to ai transparency.arXiv preprint arXiv:2310.01405, 2023

    Andy Zou, Long Phan, Sarah Chen, James Campbell, Phillip Guo, Richard Ren, Alexander Pan, Xuwang Yin, Mantas Mazeika, Ann-Kathrin Dombrowski, et al. Rep- resentation engineering: A top-down approach to ai transparency.arXiv preprint arXiv:2310.01405, 2023

    arXiv

  30. [38]

    Zico Kolter, and Matt Fredrikson

    Andy Zou, Zifan Wang, Nicholas Carlini, Milad Nasr, J. Zico Kolter, and Matt Fredrikson. Universal and trans- ferable adversarial attacks on aligned language models. arXiv preprint arXiv:2307.15043, 2023. A Additional Ablations These ablations support the measurement and evalu...

    2023 arXiv

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