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arxiv: 2604.18487 · v1 · submitted 2026-04-20 · 💻 cs.CL · cs.AI

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Adversarial Humanities Benchmark: Results on Stylistic Robustness in Frontier Model Safety

Marcello Galisai , Susanna Cifani , Francesco Giarrusso , Piercosma Bisconti , Matteo Prandi , Federico Pierucci , Federico Sartore , Daniele Nardi
Authors on Pith no claims yet

Pith reviewed 2026-05-10 04:13 UTC · model grok-4.3

classification 💻 cs.CL cs.AI
keywords adversarial benchmarkAI safetystylistic robustnessjailbreakfrontier modelsnon-maleficenceprompt transformation
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The pith

Humanities-style rewrites of harmful prompts bypass safety refusals in frontier AI models.

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

The paper tests whether current AI safety alignments hold when the same harmful goals are expressed through stylistic changes drawn from humanities writing. It begins with direct harmful tasks and applies transformations that keep the underlying intent fixed while altering phrasing, tone, and structure. Experiments across 31 models show direct versions are mostly refused, but the rewritten versions succeed at much higher rates. The results indicate that safety training does not produce a style-independent grasp of what counts as harmful. This gap points to a deeper limitation in how models internalize the principle of avoiding harm.

Core claim

The Adversarial Humanities Benchmark shows that frontier models lack stylistic robustness in safety: original harmful prompts achieve only 3.84 percent attack success rate, while humanities-transformed versions range from 36.8 percent to 65.0 percent success, for an overall 55.75 percent rate, with CBRN tasks emerging as the highest-risk category under a systemic-risk evaluation.

What carries the argument

The Adversarial Humanities Benchmark, which rewrites harmful objectives from MLCommons AILuminate via humanities-style transformations that preserve intent but use stylistic obfuscation and goal concealment.

If this is right

  • Safety techniques trained on direct harmful prompts will miss many disguised versions of the same requests.
  • Models require training that explicitly teaches recognition of harmful intent independent of surface style.
  • CBRN-related content poses the greatest uncovered risk when evaluated through a systemic-risk lens.
  • Standard safety benchmarks underestimate vulnerabilities unless they incorporate stylistic variations.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Extending the same transformation approach to legal, technical, or scientific writing styles could reveal additional failure modes.
  • If future models pass this benchmark, they may still remain vulnerable to other forms of intent concealment not covered by humanities rewrites.
  • Safety fine-tuning datasets could be expanded by generating many stylistic variants of each harmful example.

Load-bearing premise

The humanities-style rewrites preserve the exact original harmful intent without adding new detectable signals or changing the request's risk level.

What would settle it

Run the same set of original and transformed prompts on a single model in randomized order across multiple sessions and measure whether refusal rates remain consistently lower for every transformed version.

Figures

Figures reproduced from arXiv: 2604.18487 by Daniele Nardi, Federico Pierucci, Federico Sartore, Francesco Giarrusso, Marcello Galisai, Matteo Prandi, Piercosma Bisconti, Susanna Cifani.

Figure 1
Figure 1. Figure 1: The construction and evaluation process of [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 3
Figure 3. Figure 3: Original prompts (Light Blue) remain low-ASR, but humanities-style rewrites ((from [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: By hazard, transformed prompts (Red) are consistently riskier than original prompts (Light [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: By model provider, ASR increases significantly comparing original prompts (Light Blue) [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: By risk category, left: Provider by policy-relevant risk bucket ASR. The same benchmark [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
read the original abstract

The Adversarial Humanities Benchmark (AHB) evaluates whether model safety refusals survive a shift away from familiar harmful prompt forms. Starting from harmful tasks drawn from MLCommons AILuminate, the benchmark rewrites the same objectives through humanities-style transformations while preserving intent. This extends literature on Adversarial Poetry and Adversarial Tales from single jailbreak operators to a broader benchmark family of stylistic obfuscation and goal concealment. In the benchmark results reported here, the original attacks record 3.84% attack success rate (ASR), while transformed methods range from 36.8% to 65.0%, yielding 55.75% overall ASR across 31 frontier models. Under a European Union AI Act Code-of-Practice-inspired systemic-risk lens, Chemical, biological, radiological and nuclear (CBRN) is the highest bucket. Taken together, this lack of stylistic robustness suggests that current safety techniques suffer from weak generalization: deep understanding of 'non-maleficence' remains a central unresolved problem in frontier model safety.

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.

Referee Report

1 major / 3 minor

Summary. The paper introduces the Adversarial Humanities Benchmark (AHB), which rewrites harmful tasks from the MLCommons AILuminate dataset using humanities-style transformations while claiming to preserve original intent. It reports an attack success rate (ASR) of 3.84% on the original prompts versus 36.8%–65.0% (overall 55.75%) on the transformed versions across 31 frontier models. The authors conclude that this demonstrates a lack of stylistic robustness in current safety techniques, implying weak generalization and that deep understanding of 'non-maleficence' remains a central unresolved problem in frontier model safety.

Significance. If the transformations are validated to preserve harmful intent without introducing confounding changes in difficulty, ambiguity, or risk perception, the benchmark would offer a useful empirical extension of prior adversarial poetry and tales work, highlighting a potential generalization gap in safety alignments. The low circularity (empirical measurement against external models and dataset) is a strength, and the EU AI Act-inspired framing adds relevance. However, the significance is limited by the absence of supporting validation details for the core methodological assumption.

major comments (1)
  1. [Abstract] Abstract: The central claim that stylistic shifts reveal weak generalization depends on the humanities-style rewrites preserving harmful intent exactly. The abstract states that the benchmark 'rewrites the same objectives through humanities-style transformations while preserving intent' but provides no details on the transformation method, inter-rater reliability, equivalence tests, or checks for introduced cues/ambiguity. This is load-bearing, as the ASR jump from 3.84% to 55.75% could reflect altered request difficulty rather than a true robustness failure.
minor comments (3)
  1. [Abstract] Abstract: No error bars, confidence intervals, or variance measures are reported for the ASR values (3.84%, 36.8%–65.0%, 55.75%), which would improve the clarity and interpretability of the quantitative results.
  2. [Abstract] Abstract: The criteria for selecting the 31 frontier models are not specified, nor is any information on inter-model consistency or outlier handling.
  3. [Abstract] Abstract: The extension from prior 'Adversarial Poetry/Tales' literature is referenced but lacks specific comparisons, scaling details, or discussion of how the broader benchmark family addresses previous limitations.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their insightful comments on the methodological rigor of our benchmark. We address the concern regarding the validation of intent preservation in the humanities-style rewrites.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central claim that stylistic shifts reveal weak generalization depends on the humanities-style rewrites preserving harmful intent exactly. The abstract states that the benchmark 'rewrites the same objectives through humanities-style transformations while preserving intent' but provides no details on the transformation method, inter-rater reliability, equivalence tests, or checks for introduced cues/ambiguity. This is load-bearing, as the ASR jump from 3.84% to 55.75% could reflect altered request difficulty rather than a true robustness failure.

    Authors: We agree that the abstract lacks sufficient detail on the transformation methodology and validation procedures, which is a fair critique given the load-bearing nature of the intent-preservation assumption. The manuscript body provides a high-level description of the transformations as stylistic adaptations inspired by humanities genres (e.g., converting direct harmful requests into academic discourse or narrative forms), but we did not report inter-rater reliability, formal equivalence tests, or systematic checks for introduced ambiguity or cues. This omission weakens the presentation of our central claim. In the revised manuscript, we will expand the abstract to briefly note the validation approach and add a new subsection in the Methods detailing the transformation protocol, including human evaluation results for intent equivalence and difficulty assessment. We will also discuss potential confounds and how they were mitigated. While we believe the ASR increase primarily reflects a robustness gap rather than difficulty changes—given that the transformed prompts maintain or increase sophistication—we accept that additional empirical validation is necessary to fully substantiate this. revision: yes

Circularity Check

0 steps flagged

Empirical benchmark with external dataset and models; no derivation chain present

full rationale

The paper reports attack success rates measured on 31 external frontier models against tasks drawn from the external MLCommons AILuminate dataset, after applying humanities-style rewrites. No equations, fitted parameters, or mathematical derivations appear in the provided text. The central claim follows directly from the observed ASR difference (3.84% original vs. 55.75% transformed) rather than reducing to any self-referential definition or self-citation chain. Self-citation of prior Adversarial Poetry/Tales work is present only as background for the benchmark family and does not carry the load-bearing empirical result. The paper is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Empirical benchmark study with no mathematical axioms, free parameters, or invented entities; the central claim rests on the assumption that intent is preserved in the rewrites and that the tested models are representative of frontier systems.

pith-pipeline@v0.9.0 · 5502 in / 1080 out tokens · 31299 ms · 2026-05-10T04:13:36.577506+00:00 · methodology

discussion (0)

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Reference graph

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