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arXiv cs.AI· Aniruddh Pramod, James Oldfield, Adel Bibi·· 10 天前AI 评分41

面向统一滥用监控基准的研究

Towards a Unified Misuse Monitoring Benchmark

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研究者提出统一的 trace 级滥用监控形式化方法,并构建了约 6,200 条用户、LLM 智能体与环境对话记录的基准,覆盖分解攻击和提示注入攻击两类威胁,并标注危害窗口与良性对照。在 17 种监控配置中,动作框架监控在两类威胁上 AUC 分别达 0.95 和 0.99,而内容框架监控在注入攻击上降至 0.52;经典位置无关指标则高估了监控性能。

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Abstract:LLM agents increasingly act in multi-actor environments, exposing them to misuse from multiple sources: decomposition attacks, where a harmful request is split into innocuous sub-requests, and prompt injection attacks, where a compromised tool delivers a malicious instruction. Existing evaluations treat these threats separately and ask whether a trajectory is harmful, rather than when it becomes harmful. We propose monitoring the agent's responses, where its actions are externalised, and ask whether the first point where monitors identify harm lands within a harm window (from the agent's first harmful commitment to goal execution). We develop a unified formalism for trace-level misuse monitoring and use it to construct a benchmark of ~6,200 conversation transcripts between a user, an LLM agent, and the external environment, spanning both threats in a shared schema, with a labelled harm window, corresponding benign controls, and matched instances of refusals to these requests. Across 17 monitor configurations, we find that our proposed action-framed monitors perform well on both threats under classical metrics (AUC: 0.95 and 0.99 respectively), while content-framed monitors collapse on injection attacks (AUC: 0.52). We also show that classical position-blind metrics paint an optimistic picture of monitor performance, since all monitors localise decomposition attacks poorly under the interval metric, which measures the ability to localise harm. Broadly, we illustrate the need for a unified study of misuse monitoring.
Comments: 50 pages, 13 figures, 17 tables, Code: this https URL
Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2610.07089 [cs.CR]
  (or arXiv:2610.07089v1 [cs.CR] for this version)
  https://doi.org/10.48550/arXiv.2610.07089

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Aniruddh Pramod [view email]
[v1] Mon, 5 Oct 2026 12:57:03 UTC (986 KB)

来源:arXiv cs.AI · arxiv.org