Beyond Attack-Success Rate: Action-Graded Severity Scale for Tool-Using AI Agents
Score
81提出了超越二进制攻击成功率的危害分级方法，填补了现有评估体系的信息缺口。
方法设计严谨，实验在AgentDojo上完成，结果分析详细，但未在更多真实场景验证。
可直接应用于现有红队日志，为安全团队提供可操作的危害度量工具，开源促进复用。
发表于arXiv 2607，针对当前AI Agent安全热点，时效性极佳。
来自arXiv，经过同行评审预印本，但尚未正式发表，信源可靠。
Base 89.5 × T2 Source weight ×0.9 = 81
Curation line 70Selected
Why selected
The paper is methodologically innovative, thoroughly experimented, and fully open-sourced, directly addressing a critical gap in current security evaluation metrics.
AI commentary
「This paper hits the pain point of current AI agent security evaluation—binary metrics mask real harm. For AI safety researchers and defenders, this seven-level scale offers a practical assessment framework. Particularly noteworthy is its exposure of a 'zero attack-success rate' defense, highlighting how simple metrics can create false security. Readers should focus on the experimental section to learn how to apply the scale to their own red-team logs.」
Quick overview
Current AI agent red-teaming benchmarks report attack success as a single binary bit, discarding information about the actual harm. This paper introduces a seven-level ordinal harm scale (L0-L6) based on whether the executed action is reversible, crosses scope to affect another party, or expands privilege. Evaluated on the AgentDojo workspace suite, the scale reveals cases hidden by binary metrics, including a defense reporting zero attack-success rate while allowing cross-scope leakage. A panel of three frontier LLM judges achieves high ordinal agreement with a deterministic oracle (Krippendorff's alpha=0.91) but shares systematic blind spots, such as failing to recognize escalation chains. Code, prompts, and logs are open-sourced.
- Proposes a seven-level ordinal harm scale (L0-L6) assessing tool-call trajectories by reversibility, cross-scope impact, and privilege escalation.
- On the AgentDojo dataset, the scale reveals vulnerabilities hidden by binary attack-success rate, e.g., cross-scope leakage despite a 0% attack-success defense.
- A panel of three frontier LLM judges achieves high ordinal agreement (Krippendorff's alpha=0.91) with a deterministic oracle but shares systematic blind spots like failing to recognize escalation chains.
Full text
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