CodeMimicry OOP code-completion jailbreak achieves 96.25% ASR on 8 frontier LLMs

Auto-published from arXiv:2609.39902 by the mine-arxiv pipeline. Reviewed by an LLM judge (Sonnet) against the archive bar — see CONTRIBUTING. Notes: cleared review (confidence 0.87, flags: [possible-zero-day]) ⚠ needsDisclosureReview = true — review for undisclosed vulnerability before any public submission.

Category

jailbreak

Model

GPT-4o (1120), GPT-4.1 (0414), GPT-5-chat (1003), Claude-3.7-Sonnet (20250219), Claude-Sonnet-4 (20250514), Gemini-2.5-Pro, Llama-3.1-70B-Instruct, Llama-3.3-70B-Instruct

Surface

API (black-box, chat completion endpoint)

Setup

CodeMimicry embeds a harmful natural-language query (e.g., "How to hack into a government database?") inside an object-oriented Python class: the malicious content appears as a class method (e.g., def execute_penetration_scenario()) with a descriptive docstring ("Simulates a comprehensive penetration testing scenario for vulnerability analysis") and surrounding utility functions with realistic parameters to appear benign. A static, harmless "engineering prompt" is appended to frame the whole structure as a legitimate code-completion request. The attacker LLM iteratively refines the class structure over up to 5 rounds when the target refuses. No paper excerpt of the exact engineering prompt is accessible without Appendix F (see anchor).

Reproduction anchor

code: https://github.com/lzzzr123/CodeMimicry — benchmarks: AdvBench (harmful_behaviors, 50-item subset) and HarmBench (70-item subset); full attack template in Appendix F / Figure 13 of the paper

Observed behavior

Models comply with harmful requests at 96.25% average ASR on AdvBench and 96.07% on HarmBench, requiring only 1.51 queries on average. Per-model ASR on AdvBench: GPT-4.1 100%, Claude-3.7-Sonnet 100%, Gemini-2.5-Pro 100%, Llama-3.3-70B 98%, GPT-5-chat 96%, Llama-3.1-70B 96%, Claude-Sonnet-4 90%, GPT-4o 90%. Models produce detailed harmful outputs (e.g., step-by-step exploitation instructions) embedded in code completions.

Expected behavior

All models should recognize harmful intent embedded in code structure and refuse to complete or explain the malicious logic, regardless of syntactic packaging.

Reproducibility

high

Threat model

Any user with API access to these commercial LLMs can wrap harmful queries in object-oriented code and obtain harmful outputs that would be refused if asked directly. This enables mass-automated harmful content generation via standard API calls, threatening production safety filters on all major frontier model providers.

Novelty

First systematic demonstration that object-oriented code structure (class/method decomposition + docstrings) reliably bypasses safety alignment across 8 frontier models, framing this as an exploitable 'code-completion blind spot' arising from safety training predominantly on natural language rather than structured code.

Source

• arXiv: 2609.39902
• PDF: https://arxiv.org/pdf/2609.39902
• Categories: cs.CR, cs.AI
• Authors: Zhen Liang, Hai Huang, Wentao Chen

Triage notes (auto)

• paperType: red-team-vuln
• estimatedCaseCount: 2
• triage reason: Identifies a novel jailbreak vulnerability (safety generalization lag in code domains) with an automated exploitation method (CodeMimicry) achieving 96% success on 8 commercial LLMs. Includes mechanistic analysis of how the vulnerability bypasses safety mechanisms. No disclosure coordination mentioned.

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