AuditBench - Lab Attack Classification: leaderboard

Metric: F1 (0-100) for attack classification (is a log chunk malicious or benign) on the Lab data (Linux and Windows audit logs recorded by the authors) (5 attack and 5 benign scenarios): the model reads the scenario's edge-representation (provenance-graph edges, 400-line chunks) audit logs in chunks with prompt v2 and flags malicious log chunks; recall counts the attack scenarios found and precision counts false-positive chunks out of 21; temperature 0 where available (GPT-5 models at the default medium reasoning effort); higher is better. Source: arxiv.org. Saturation forecast: Estimated already saturated. 5 models tracked.

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Interactive version: theaggregate.ai/benchmark?slug=auditbench-lab-attack-classification · How It Works · Data refreshed daily, snapshot 2026-09-29.