TL;DR
SLEIGHT-Bench is a comprehensive benchmark testing the ability of agent monitors to detect covert harmful behaviors in autonomous coding agents, revealing significant evasion vulnerabilities.
Contribution
The paper introduces SLEIGHT-Bench, a new benchmark dataset with diverse attack strategies, and evaluates monitor effectiveness, highlighting key evasion tactics and potential improvements.
Findings
20 of 40 attacks evade current monitors at 1% false-positive rate
Overall detection rate is 32% across all attacks
Targeted prompts improve monitor detection performance
Abstract
Since autonomous coding agents generate complex behaviors at high-volume, we may want to use other LLMs to monitor actions to reduce the risk from dangerous misaligned behavior. To better understand the limitations of such monitors against the diverse attack strategies that a coding agent could use, we present SLEIGHT-Bench (Subtle Low-itEration Insight-Guided Harmful Transcripts), a benchmark of synthetic transcripts containing 40 attacks across 11 categories, each showing a coding agent covertly pursuing a harmful objective (e.g. weight exfiltration, credential theft, unmonitored rogue deployment). We find that 20 of 40 attacks are never caught by an Opus 4.6 monitor with extended thinking across 10 trials at a 1% false-positive rate, while the overall catch rate is 32%. We identify several evasion strategies such as exploiting model priors, user instruction ambiguity, and state…
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