Defense Against the Dark Prompts: Mitigating Best-of-N Jailbreaking with Prompt Evaluation

Connor Stevens; Matija Franklin; Rebecca Gorman; Stuart Armstrong

REVIEW 2 major objections 6 minor 22 references

Defense Against the Dark Prompts: Mitigating Best-of-N Jailbreaking with Prompt Evaluation

T0 review · 2 major / 6 minor · reviewed 2026-08-09 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read Repeatedly asking an LLM to judge a prompt blocks essentially all Best-of-N jailbreaks, including 100% of the attack paper's published successful jailbreak prompts.

desk verdict A cheap, open-source prompt-evaluation filter looks genuinely effective against Best-of-N text jailbreaks, but the headline rates depend on an unreported exclusion policy that any deployment must resolve. read the letter →

arxiv 2502.00580 v1 pith:H6A5RDXK submitted 2025-02-01 cs.CR cs.AIcs.CLcs.CY

Stuart Armstrong , Matija Franklin , Connor Stevens , Rebecca Gorman This is my paper · ORCID

classification cs.CRcs.AIcs.CLcs.CY

keywords LLMjailbreakingBest-of-NpromptevaluationagentiterativesafetyadversarialrobustnessdefensiveAIweightedscoring

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

This paper claims that repeatedly asking an evaluation language model to judge whether a user prompt is dangerous or a jailbreak attempt can block nearly all Best-of-N jailbreaks. Across six datasets, the Defense Against the Dark Prompts (DATDP) method blocked 100% of the 1,045 successful jailbreak prompts published with the Best-of-N paper and 99.8% of 1,000 jailbreaking prompts from the authors' own replication, with only one false positive among 250 normal prompts. The same result held when the evaluation was powered by the small LLaMa-3-8B-instruct model instead of Claude. If this holds in practice, a cheap, static prompt filter could substantially raise the cost of this class of attack.

What carries the argument

The carrying object is the DATDP evaluation agent: a separate LLM given a system prompt that defines the forbidden task as 'anything that could be dangerous for an LLM to answer or could be intended to trick or jailbreak the LLM,' instructed to reason step-by-step and end with 'yes' or 'no.' Each prompt is evaluated $N$ times, responses not ending in 'yes' or 'no' are discarded, and a weighted sum (yes $=+2$, no $=-1$) decides block vs. pass. This repeated, weighted evaluation is what lets a small model detect augmented prompts that would jailbreak the same model in a responding role.

What would settle it

Re-run the DATDP evaluation on the BoN paper's 1,045 jailbreak prompts and the replication's 1,000 jailbreaking prompts, but treat every malformed evaluator response as an acceptance ('no') instead of excluding it; if the fraction blocked drops below 99%, the exclusion rule is carrying the result.

Watch

Extended reading notes

Core claim

The central discovery is that the same models that are vulnerable to Best-of-N jailbreaking when answering can reliably judge whether a prompt is dangerous when placed in an evaluation role. DATDP does this by taking the submitted prompt, asking an evaluation LLM to reason step by step and end with 'yes' or 'no' about whether the prompt asks for something dangerous or tries to jailbreak, repeating this $N$ times ($N=25$ for LLaMa-3-8B-instruct, $N=5$ for Claude), and using a weighted vote in which a 'yes' rejection is weighted $+2$ and a 'no' acceptance is weighted $-1$. This procedure blocked all 1,045 BoN-paper jailbreaks, 99.8% of the replication jailbreaks, and 99.94% of generic augmented prompts, while accepting virtually all normal prompts. The paper argues this should multiply the cost of BoN attacks by roughly 500.

Load-bearing premise

The reported blocking rates assume that excluding evaluation responses that do not end in 'yes' or 'no'—which happened with LLaMa-3-8B-instruct—does not systematically remove dangerous prompts from the denominator.

Editorial extensions

If this is right

The cost of Best-of-N jailbreaking rises sharply: the paper estimates the defense multiplies the number of attempts needed by roughly 500, since it blocks about 99.7% of augmented prompts at the prompt level.
Small models can guard larger models: LLaMa-3-8B-instruct as an evaluator blocks 99.8% of jailbreaks of itself, suggesting cheap defenses are feasible without frontier-grade evaluators.
Prompt-level defense is additive: DATDP can be placed upstream of any responding LLM without retraining that model, complementing its internal safety mechanisms.
Response-level evaluation adds a second layer: although a LLaMa-based response evaluator blocked only 76% of harmful responses in the paper's initial test, it caught all three prompts that slipped past the prompt-level agent, so a dual-layer design looks promising.

Reading between the lines

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

If these blocking rates transfer to production traffic, DATDP-style evaluation could become a standard upstream layer for LLM APIs, trading extra inference cost for a large reduction in successful BoN-style attacks without retraining the responder.
A natural next test is to run DATDP against a BoN attack whose augmentations are chosen adversarially to fool the evaluator rather than sampled randomly; the paper's tension argument predicts lower but still substantial blocking.
An unbiased estimate of LLaMa's true blocking rate requires accounting for the excluded non-'yes'/'no' responses; if dangerous prompts were more likely to produce malformed outputs, the reported 99.8% would be optimistic.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

The thing to know: this is a legitimate empirical result, not a hype paper. The evaluation-agent idea comes from the authors' own 2022 work, but the BoN-specific measurement is new. They tested against external corpora (HarmBench, the BoN paper's released jailbreaks), ran their own replication of BoN, and used fixed hyperparameters with a sensitivity analysis for N. The finding that an 8B LLaMa model matches Claude as an evaluator is the most practically useful part. They ship code, and the confidence intervals are computed correctly. That is real work.

The soft spots are real but not disqualifying. The biggest is Section 2.1.3: responses that do not end in 'yes' or 'no' are excluded, and the paper never says how many were excluded or whether dangerous prompts were over-represented among them. The stress-test note is right that this matters. The scoring is asymmetric—one 'yes' blocks, and it takes four 'no's to overcome one 'yes'—so if harmful prompts trigger malformed evaluator outputs more often than benign ones (plausible, since refusals often hedge), the reported 99.8–100% blocking rates overstate what a deployable policy would achieve. A deployment must either fail closed (block malformed outputs, raising false positives) or fail open (pass them, lowering blocking). The paper's 'exclude' rule is an evaluation artifact, not a policy. The authors should report the counts and redo the analysis under both closure rules.

The '500 times or so harder' extrapolation is also hand-wavy. It assumes the filter multiplies attack success probability by a constant, but there is no evidence for that. The evaluation is static—no adaptive attacker that optimizes against DATDP. That is a limitation worth stating clearly, not a fatal one.

This paper is for practitioners who want a cheap, understandable filter to add in front of an LLM, and for safety researchers studying input filtering. It deserves a serious referee. I would send it to peer review, but with a request that the authors add exclusion counts, test both fail-open and fail-closed interpretations, and temper the '500 times' claim. With those changes, the central claim would be solid.

Referee Report

2 major / 6 minor

Summary. This paper proposes DATDP, a pre-prompt evaluation defense in which a separate LLM judges whether a user prompt is dangerous or a jailbreak attempt, using N repeated evaluations and a weighted vote (yes = +2, no = -1) to decide whether to block the prompt before it reaches the responding model. DATDP is evaluated on six text datasets: the 159 HarmBench harmful prompts, the 1045 successful jailbreaks released by the Best-of-N paper, 1000 successful and 1000 unsuccessful jailbreaks from the authors' replication of Best-of-N, 1590 random augmented prompts, and 250 normal prompts. Using Claude 3.5 Sonnet or LLaMa-3-8B-instruct as the evaluator, the paper reports blocking rates between 99.5% and 100% on harmful augmented prompts, 0% and 0.4% false positives on normal prompts, exact Clopper-Pearson confidence intervals, and a sensitivity analysis over the number of iterations N. The authors conclude that evaluation agents make BoN jailbreaking roughly 500 times harder and that small models are nearly as effective evaluators as frontier models.

Significance. If the reported numbers hold, the paper offers a simple, cheap, and model-agnostic defense against a currently effective class of text jailbreaks. The evaluation has genuine strengths: it uses external corpora (HarmBench and the BoN paper's released jailbreaks), fixed hyperparameters, exact binomial confidence intervals, open-source code, and a replication of the attack. The finding that a small open-weight model can serve as a near-equivalent evaluator is practically important. The main reservations are methodological rather than conceptual: the headline blocking rates are computed under an exclusion rule for non-conforming evaluator outputs whose effect is unquantified, and the '500x harder' conclusion extrapolates from static-corpus blocking to an adaptive attacker. Both issues are addressable in revision.

major comments (2)

[Section 2.1.3 and Table 2] The exclusion of evaluator responses that do not end in 'yes' or 'no' is not quantified, and it can change the reported blocking rates. The paper states that this was 'an issue with LLaMa-3-8B-instruct' but gives no counts per dataset or per prompt. Because the voting rule is asymmetric (a 'yes' contributes +2, a 'no' contributes -1) and the threshold depends on N, dropping non-conforming responses is not a neutral operation: for N=5, one conforming 'yes' and four non-conforming responses yields a positive sum and a block, whereas four conforming 'no' responses would make the prompt pass. If dangerous prompts are more likely than benign prompts to produce malformed or hedging outputs, the headline 100% and 99.8% blocking rates and the 0-0.4% false-positive rates do not correspond to any single deployable policy. Please report the number and distribution of excluded responses, and rerun the main tables under fail-closed (block on malformed output) and fail-open (pass on malformed output) assumptions, or justify why exclusions are uninformative.
[Section 3.1] The claim that DATDP makes BoN '500 times or so harder' assumes a constant per-attempt blocking probability and a non-adaptive attacker. The experiments measure blocking on a fixed corpus of prompts that a static BoN run produced; they do not measure end-to-end robustness against an attacker who can adapt, for example by sampling augmentations conditioned on passing the evaluator, by increasing N, or by targeting the evaluator's blind spots. The power-law argument in Section 3.1 therefore goes beyond the data. Please either evaluate DATDP in a filter-aware adaptive attack loop (e.g., BoN with the evaluator as an oracle) or restate the conclusion as 'blocked a fixed corpus of sampled BoN attempts.'

minor comments (6)

[Section 2.1.3] The example 'one yes and four no would give a weighted sum of 1 × 2 − 4 × −1 = −2' is arithmetically wrong; the expression evaluates to +6. The intended calculation is 1×2 + 4×(−1) = −2.
[Figure 4 caption] The caption reads 'DATDP on Datasets of Augmented Prompts' but the figure displays the non-augmented HarmBench and Normal datasets; rename it to 'Non-augmented Prompts' or similar.
[Throughout] There are minor typos: 'were were labeled' in Section 2.1.3, 'A vailability' in Section 1.2, and 'This study aim to assess' in Section 1.1.
[Section 2.1.3] The decision rule for a zero weighted sum is unspecified; please state whether a sum of zero blocks or passes the prompt.
[Section 3.2 and Table 2] The normal-prompt corpus is only 250 prompts, so the false-positive estimate is imprecise; for the Claude evaluator the 95% CI upper bound is 1.46%, so 'near-zero false positives' should be presented with that sample-size caveat.
[Appendix A] The appendix lists prompts that were not blocked, which is helpful; making the raw evaluator outputs or at least the excluded-response counts available would further support reproducibility.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: DATDP is evaluated on external corpora with fixed hyperparameters; the disclosed self-citations are not load-bearing.

full rationale

The paper's central claim is an empirical blocking rate measured against external datasets (the BoN paper's released jailbreak prompts, HarmBench prompts, and a 250-prompt normal corpus) plus its own replication sets. The DATDP algorithm is fully specified in the paper (Section 2.1), including the system prompt, the N=5/N=25 iteration counts, and the +2/−1 weighted scoring rule. No parameter is fitted to the test sets, and the success metric is not defined in terms of the evaluator's own outputs. The references to [18] and [19] disclose that the evaluation-agent approach originates in the authors' prior work, but the present paper does not rely on those references for the validity of its measurements; the method is described in sufficient detail to stand alone. The only substantive concern is that nonconforming evaluator responses ending in neither 'yes' nor 'no' were excluded from the reported blocking rates (Section 2.1.3), which could bias the headline percentages if dangerous prompts produce malformed outputs disproportionately. That is a statistical reporting and deployment-transfer issue, not a circularity: it does not make the claimed result equivalent to the paper's inputs by construction. Accordingly, no circular step is identified, and the overall circularity score is low.

Assumptions & free parameters 4 free parameters · 3 assumptions · 0 invented entities

DATDP introduces no new physical or conceptual entities; it is a prompt-evaluation procedure. The load-bearing assumptions are that evaluator 'yes'/'no' outputs are reliable, that the HarmBench grader substitution is valid, and that the static corpora represent the attack distribution. Free parameters (N, vote weights) are design choices with reported sensitivity, not fitted to the reported blocking rates.

free parameters (4)

N iterations for Claude evaluator = 5
Chosen for consistency and cost; sensitivity to N is shown in Figure 5, not fit to the test sets.
N iterations for LLaMa-3-8B-instruct evaluator = 25
Chosen to compensate for lower output consistency; Figure 5 indicates N=12 is near-optimal, so 25 is conservative.
Weight of a 'yes' (rejection) vote = +2
Chosen to make the system conservative (one 'yes' outweighs two 'no's); no tuning on the reported datasets is described.
Weight of a 'no' (acceptance) vote = -1
Chosen jointly with the +2 weight; no fitting to the evaluation corpora is reported.

assumptions (3)

domain assumption An evaluator LLM's final 'yes' or 'no' token is a reliable indicator of prompt harmfulness
The entire method depends on this classification being trustworthy; the paper tests it empirically but does not prove it across unseen distributions.
domain assumption HarmBench's auto-grader, run with LLaMa-3-8B-instruct instead of GPT-4o, correctly labels jailbreak success in the replication
Footnote 3 acknowledges the deviation from the Best-of-N paper and appeals to visual inspection, which is weaker than the original grader.
domain assumption The 1045 prompts released by the Best-of-N paper are a representative corpus of successful BoN jailbreaks
The paper uses this external corpus as the primary benchmark; if it is non-representative, the 100% blocking claim does not transfer to other BoN samples.

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Cite this review

Pith. "Pith review of Defense Against the Dark Prompts: Mitigating Best-of-N Jailbreaking with Prompt Evaluation." pith.science (2026). https://pith.science/paper/H6A5RDXK

@misc{pith2026250200580,
  author       = {Pith},
  title        = {Pith review of: Defense Against the Dark Prompts: Mitigating Best-of-N Jailbreaking with Prompt Evaluation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/H6A5RDXK}},
  note         = {Machine review of arXiv:2502.00580}
}

abstract

Recent work showed Best-of-N (BoN) jailbreaking using repeated use of random augmentations (such as capitalization, punctuation, etc) is effective against all major large language models (LLMs). We have found that $100\%$ of the BoN paper's successful jailbreaks (confidence interval $[99.65\%, 100.00\%]$) and $99.8\%$ of successful jailbreaks in our replication (confidence interval $[99.28\%, 99.98\%]$) were blocked with our Defense Against The Dark Prompts (DATDP) method. The DATDP algorithm works by repeatedly utilizing an evaluation LLM to evaluate a prompt for dangerous or manipulative behaviors--unlike some other approaches, DATDP also explicitly looks for jailbreaking attempts--until a robust safety rating is generated. This success persisted even when utilizing smaller LLMs to power the evaluation (Claude and LLaMa-3-8B-instruct proved almost equally capable). These results show that, though language models are sensitive to seemingly innocuous changes to inputs, they seem also capable of successfully evaluating the dangers of these inputs. Versions of DATDP can therefore be added cheaply to generative AI systems to produce an immediate significant increase in safety.

Figures

Figures reproduced from arXiv: 2502.00580 by the authors.

**Figure 1.** Overview of the DATDP pipeline. An evaluation agent assesses each user-submitted prompt until high confidence is reached. Then the prompt is blocked (if dangerous) or passed through to the responding LLM (if safe). The Defense Against The Dark Prompts (DATDP) framework builds on [18] and [19]. It employs an iterative evaluation process to detect and block harmful inputs before they reach the language model [PITH_FU… view at source ↗

**Figure 2.** LLMs perform very differently in an evaluation agent role, versus a [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗

**Figure 3.** DATDP on Datasets of Augmented Prompts This Figure shows the performance of the evaluation agent on four datasets, using either Claude (solid bar) or LLaMa-3-8B-instruct (bar with hatches) as the base model. The four datasets are: the successful Jailbreaking prompts from the BoN paper, the prompts that successfully jailbroke LLaMa-3-8B-instruct in our replication, the prompts that failed to jailbreak LLaMa-3-8B-inst… view at source ↗

Figures from the paper (2 more)

**Figure 4.** Figure 4: DATDP on Datasets of Augmented Prompts This figure shows the performance of the evaluation agent on two datasets, using either Claude (solid bar) or LLaMa-3-8B-instruct (bar with hatches) as the base model. The two datasets are: 159 prompts from HarmBench’s original da…

**Figure 5.** Figure 5: Proportion of blocked prompts as a function of [PITH_FULL_IMAGE:figures/full_fig_p012_5.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

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Jailbreaking large language models with symbolic mathematics

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How johnny can persuade llms to jailbreak them: Rethink- ing persuasion to challenge ai safety by humanizing llms.arXiv preprint arXiv:2401.06373, 2024

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arXiv 2024