One of the main hopes for AI safety is using AIs to automate AI safety research. However, if models are misaligned, then they may sabotage the safety research. For example, misaligned AIs may try to:Perform sloppy research in order to slow down the rate of research progressMake AI systems appear safer than they areTrain a successor model to be misalignedWhether we should worry about those things depends substantially on how hard it is to sabotage research in ways that are hard for reviewers to detect. To study this, we introduce Auditing Sabotage Bench, a benchmark of 9 ML research codebases with sabotaged variants.We tested frontier LLMs and LLM-assisted humans on the benchmark and found that neither reliably catches sabotage. Our best auditor, Gemini 3.1 Pro, achieved an AUROC of 0.77 and a top-1 fix rate of 42%. LLM-assisted humans performed comparably but slightly worse. We also found that LLMs can generate sabotages that partially evade same-capability monitors.📄 Paper 💻 CodeMetho...