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Auditing Sabotage Bench: A Benchmark for Detecting and Fixing Research Sabotage in ML Codebases

NeurIPS 2026 · Large language models › AI and society · approach: Data commons and human baselines · 0 citations (OpenAlex)

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Introduces Auditing Sabotage Bench, 9 ML research codebases with human-made sabotaged variants, and evaluates frontier LLM and LLM-assisted human auditors on detecting and fixing sabotage, plus LLM red-team sabotage generation.

Problem

Autonomous AI research could contain subtle sabotage that produces misleading results while evading detection, and there is no realistic benchmark for auditing such sabotage in ML research codebases.

Approach

Sabotages modify implementation details (hyperparameters, training data, evaluation code) of real ML research codebases to flip a key finding while keeping the high-level methodology intact, and auditors are scored on both credence discrimination (AUROC) and proposed-fix accuracy (top-k fix rate).

Key results

• Gemini 3.1 Pro with full experiments achieved the best AUROC of 0.77 and top-1 fix rate of 42% (top-5 53%) on the benchmark.
• Human auditors (LLM-assisted, 72 audits) reached AUROC 0.68 after 2 hours and top-1 fix rate 38%, comparable to Gemini 3.1 Pro.
• Claude Opus 4.6 auditing Claude-generated sabotages reached AUROC 0.80, while Claude-generated sabotages had top-1 fix rate up to 68%, easier to fix than human sabotages.

Surprising findings

• LLM-generated sabotages can sometimes evade same-capability LLM auditors, though they are weaker and easier to fix than human-generated ones.
• GPT models refused to produce sabotaged codebases even when told it was for safety research.

Limitations

• Single human attacker and only 11 sabotages across 9 codebases, making conclusions tentative.
• Sabotages adapted from existing codebases rather than greenfield research; source papers may be memorized by LLMs.
• Limited human audits (72) and small sample size; human auditors had widely varying calibration.

Experimental setup

Datasets: Subliminal Learning codebase CoT Red-Handed codebase Feature vs Lazy Training codebase Eval-Aware Steering codebase Two-Hop Latent Reasoning codebase Copy Suppression codebase Hidden in Plain Text codebase Sandbagging probing codebase Multi-Turn Jailbreaking codebase

Benchmarks: Auditing Sabotage Bench

Metrics: AUROC top-1 fix rate top-3 fix rate top-5 fix rate

Baselines: Claude Opus 4.6 GPT-5.2 Gemini 3.1 Pro

Builds on: AI control

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