Agent Hacks Agent: Autoresearch for Production-Agent Red-Teaming
Score
81首次提出通过可证伪循环生成可迁移漏洞概念图，超越传统只记录攻击成功的工件，信息增量显著。
方法设计完整，实验覆盖多个模型和场景，对比基线充分，但未在更多商用Agent上验证。
生成的可审计VCG可直接用于生产安全团队的漏洞检查和补丁验证，实用价值高。
生产级LLM Agent正快速部署，安全评估方法需求迫切，研究紧跟当前热点。
论文发表于arXiv预印本，代码已开源，实验设计透明可复现，可信度高。
Base 89.5 × T2 Source weight ×0.9 = 81
Curation line 70Selected
Why selected
The paper presents a novel automated red-teaming framework AHA with a reusable Vulnerability Concept Graph, rigorous experimental design, and thorough cross-model/cross-scenario transfer validation, offering high academic and engineering value.
AI commentary
「This paper upgrades red-teaming from one-off vulnerability hunting to systematic knowledge accumulation. If you are deploying agents like Claude Code or Codex, or responsible for AI safety evaluation, the AHA framework and VCG concept can save you significant repetitive testing effort. More importantly, it makes vulnerability discovery traceable and transferable — no longer a one-time bug report, but a continuously growing safety knowledge base.」
Quick overview
Production LLM agents (e.g., Claude Code, Codex) operate over untrusted content, files, commands, and workspace state, making safety failures directly actionable. Existing red-teaming methods mainly optimize attack success and preserve artifacts such as benchmarks, payloads, or attack programs, but fail to record the enabling conditions behind unsafe behavior. This paper proposes AHA (Autoresearch for Production-Agent Red-Teaming), a falsifiable discovery loop that uses one agentic research environment to discover reusable vulnerability knowledge about another. AHA proposes a vulnerability hypothesis, constructs a falsifier, instantiates a valid attack, executes it in a sandboxed harness, reflects on the trajectory, and promotes confirmed findings into a Vulnerability Concept Graph (VCG). Each concept links an attacker-facing surface to an unsafe trajectory through a claim, enabling condition, falsifier, transfer prediction, and supporting evidence. Across Claude Code and Codex on three scenarios covering direct and indirect attacks, the discovered concepts reveal a reusable vulnerability core across models and agents. A frozen VCG outperforms the strongest frozen discovery baseline by 14.2 percentage points under the same single-shot protocol, while transferring across scenarios and attack channels. The resulting VCG provides an auditable artifact for production safety teams to inspect vulnerabilities, validate patches, and accumulate reusable safety knowledge.
- Proposes AHA framework for automated red-teaming of production LLM agents, generating a reusable Vulnerability Concept Graph (VCG).
- VCG records attack surfaces, enabling conditions, falsifiers, and transfer predictions, surpassing traditional artifacts that only capture successful attacks.
- Validated on Claude Code and Codex; frozen VCG outperforms the strongest frozen baseline by 14.2 percentage points and transfers across scenarios.
- Provides an auditable artifact for safety teams to inspect vulnerabilities, validate patches, and accumulate reusable safety knowledge.
Full text
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