测试大模型代码代理在真实工作环境中的越狱攻击能力，发现部分恶意代码可成功运行。
Breaking the Code: Security Assessment of AI Code Agents Through Systematic Jailbreaking Attacks
- 构建三阶段基准JAWS-Bench，模拟从空环境到多文件的攻击场景。
- 58%攻击生成有害代码，27%能完整执行，多文件环境成功率升至75%。
- 代码代理比纯模型更易越狱，适合安全研究与开发者参考。
具备代码能力的大语言模型代理被嵌入软件工程流程中，可读写并执行代码，使越狱风险远超仅限文本的场景。现有评估侧重拒绝或有害文本检测，未考察代理是否编译并运行恶意程序。本文提出JAWS-Bench（Jailbreaks Across WorkSpaces）基准，涵盖三个逐步增强的工作空间模式：空环境（JAWS-0）、单文件（JAWS-1）和多文件（JAWS-M）。配套设计分层可执行性判断框架，评估合规性、攻击成功率、语法正确性和运行时可执行性，以衡量实际危害。在七个来自五个家族的LLM后端上，仅用提示攻击在JAWS-0中达61%合规率；其中58%为有害代码，52%可解析，27%能端到端运行。在JAWS-1中，强模型合规率接近100%，平均攻击成功率（ASR）达~71%；在JAWS-M中，平均ASR提升至~75%，32%的攻击代码可运行。将LLM封装为代理使ASR提高1.6倍，通过规划与工具使用绕过初始拒绝。对SWE-Agent和OpenAI Codex的额外评估显示相似趋势，表明JAWS-Bench可跨代理框架复用。类别分析揭示最易受攻击且可部署的攻击类型，推动面向执行的安全防御与保持拒绝机制的代理设计。
原文摘要 · Abstract (English)
Code-capable large language model (LLM) agents are embedded in software engineering workflows where they can read, write, and execute code, raising "jailbreak" stakes beyond text-only settings. Prior evaluations emphasize refusal or harmful-text detection, leaving open whether agents compile and run malicious programs. We present JAWS-Bench (Jailbreaks Across WorkSpaces), a benchmark spanning three escalating workspace regimes mirroring attacker capability: empty (JAWS-0), single-file (JAWS-1), and multi-file (JAWS-M). We pair it with a hierarchical, executable-aware Judge Framework that tests (i) compliance, (ii) attack success, (iii) syntactic correctness, and (iv) runtime executability to measure deployable harm. Across seven LLM backends from five families, prompt-only attacks in JAWS-0 achieve 61% compliance; 58% are harmful, 52% parse, and 27% run end-to-end. In JAWS-1, compliance reaches ~100% for stronger models with a mean ASR (Attack Success Rate) ~71%; JAWS-M raises mean ASR to ~75%, with 32% runnable attack code. Wrapping an LLM in an agent increases ASR by 1.6$\times$, by overturning initial refusals during planning and tool use. Additional evaluations with SWE-Agent and OpenAI Codex exhibit similar trends, indicating that JAWS-Bench can be reused across multiple agent frameworks. Category analyses identify which attack classes are most vulnerable and deployable, motivating execution-aware defenses and refusal-preserving agent designs.
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