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OpenCompass Hub
AutoAdvExBench
AutoAdvExBench is a benchmark designed to evaluate large language models' (LLMs) ability to autonomously exploit adversarial example defenses, directly measuring LLMs' success on tasks regularly performed by machine learning security experts.
Evaluation context and evidence
- Source
- OpenCompass Hub
- Publisher
- GoogleDeepMind , ETHZurich
- Benchmark release
- 2025-03-03
- Collected
- 2026-08-18
- Modality
- Not recorded
These are source-reported observations, not independent reruns by this site. Check the task version, evaluation protocol, unit and reporting date before comparing models. Results from different setups are not a universal ranking.
Original sources
Reported results 0
This source record has no numeric results in our collection. That does not mean a score of zero or that no public results exist. Consult the original resources above.