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FuzzingBrain-Bench V1: Evaluating Open-Ended Bug Discovery by LLMs

arXiv · AI, language, vision and robotics · article · Aug 25, 2026 · UTC

Evaluating the ability of large language models (LLMs) to discover software bugs is increasingly important. Existing benchmarks typically evaluate this capability by asking the model to generate a proof-of-concept input that triggers a predefined target vulnerability. However, this setup may overlook valid crashes discovered by the model when they do not match the predefined target. As a result, the evaluation may not reflect the model's real capability. We present FuzzingBrain-Bench, a benchmark for assessing AI models' ability to discover bugs in open-source software. Models are given an ope

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