Document-completion reframing jailbreaks GPT-5.4 and Claude Sonnet 4.6 at scale

Auto-published from arXiv:2608.16465 by the mine-arxiv pipeline. Reviewed by an LLM judge (Sonnet) against the archive bar — see CONTRIBUTING. Notes: cleared review (confidence 0.82, flags: [possible-zero-day, no-prompt-excerpt]) ⚠ needsDisclosureReview = true — review for undisclosed vulnerability before any public submission.

Category

jailbreak

Model

GPT-5.4, Claude Sonnet 4.6, Llama-3.3-70B-Instruct, DeepSeek-V4-Pro, Gemini 3.1 Pro

Surface

API (agent harness — JailbreakSkill executor using Qwen2.5-7B rewriter + Claude Sonnet 4.5 orchestrator)

Setup

The JailbreakSkill framework autonomously evolved a skill called 'document-completion reframing' (surfaced as one of several rewrite-* skills) by diagnosing failed attack attempts and discovering that presenting a harmful direct request as an unfinished document that needs completing bypasses safety training. No verbatim prompt template is quoted in the paper body; the appendix stores skills as SKILL.md procedural files. The attack is selected by risk-conditioned UCB bandit and applied by an LLM executor. Paper states: 'novel attack strategies such as reframing a direct request as an unfinished document-completion task.'

Reproduction anchor

code: https://github.com/BattleWen/JailbreakSkill (evolved skill library included; AdvBench harmful_behaviors benchmark, n=520; HarmBench standard subset)

Observed behavior

After skill evolution (Stage 2), macro-average ASR reaches 74.2% on AdvBench and 67.9% on HarmBench across target models. GPT-5.4 specifically reaches 62.5% ASR on AdvBench (+48.6 percentage points over Stage 1 baseline). Claude Sonnet 4.6 reaches 41.9% ASR on AdvBench and 38.5% on HarmBench. Evolved skills including document-completion reframing transfer to unseen models without further adaptation.

Expected behavior

Models should refuse or deflect harmful requests regardless of surface framing (direct request vs. document-completion narrative), producing no actionable harmful content.

Reproducibility

high

Threat model

Red-teamers or malicious actors with API access can run the open-source JailbreakSkill agent against production LLMs (GPT-5.4, Claude, Gemini) to automatically generate novel jailbreaks at scale; each successful evolved skill is reusable across future targets without re-running expensive search, lowering the cost of large-scale harmful content generation campaigns.

Novelty

First demonstrated automated discovery of the document-completion framing as an emergent attack vector via LLM-driven failure diagnosis, showing that safety training does not generalize to task-completion framings even on frontier models including GPT-5.4.

Source

• arXiv: 2608.16465
• PDF: https://arxiv.org/pdf/2608.16465
• Categories: cs.AI
• Authors: Xiaoyu Wen, Jiajia Li, Zhida He, Peng Yu, Chenxu Wang, Han Qi, Ziyuan Zhou, Cheng Jin, Ying Wen, Xingcheng Xu, Shuyue Hu, Tianhang Zheng, Chaochao Lu, Qiaosheng Zhang

Triage notes (auto)

• paperType: red-team-vuln
• estimatedCaseCount: 3
• triage reason: Systematic red-teaming framework generating novel, reproducible jailbreak strategies (e.g., document-completion reframing) with 48.6pp improvement against GPT-5.4; attacks quantified on established benchmarks with code released publicly but no vendor disclosure mentioned.

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