Beyond End-to-End Success: Diagnosing Failures in Long-Horizon Security LLM Agents

2026-08-20

AI Summary

研究通过设置检查点和控制干预，诊断长周期安全LLM代理在执行任务时的失败原因，特别是在信息重用、状态观察等方面的问题。

Abstract

Long-horizon security LLM agents must carry information and decisions across many dependent interactions, where later actions often depend on services, state, or access discovered much earlier. This makes final task success difficult to interpret: an agent may fail before it ever reaches the point where the capability of interest can be exercised. We present a diagnostic methodology that instruments security tasks with checkpoints, separates failures before and after capability exposure, and uses controlled interventions to test suspected upstream bottlenecks. We evaluate the methodology across four task families involving delayed reuse of discovered information, reuse of observed state, recovery from failed strategies, and decision making after uncertain outcomes. On observed state reuse, checkpoint analysis shows that many Gemini 2.5 Flash failures occur before the model observes the state it is later expected to reuse. In a pre-specified 92-seed study, targeted protocol-disambiguation guidance increases state observation from 65.5\% under a matched non-guidance control message to 95.4\%. Repeating the same design with Gemini 3.7 Flash produces the opposite effect, while state observation no longer reliably predicts task completion. These results show that the dominant source of failure can shift across model generations, motivating evaluation that diagnoses where and why long-horizon security agents fail rather than relying only on aggregate task success.

Problem

• Long-horizon security LLM agents
• task success interpretation
• failure diagnosis

Innovation

• diagnostic methodology
• checkpoints
• controlled interventions
• upstream bottlenecks