SWE-Test: Benchmarking LLM Vulnerability Discovery via Input Prediction
Vulnerability discovery is becoming an important ability of large language model (LLM) agents: agents that silently miss real defects leave critical software exposed. Rigorously measuring this ability is therefore urgent, but existing benchmarks are gameable through data contamination, score recall against an unknowable vulnerability set, often rely on synthetic bugs, and report a single end-to-end verdict that cannot localize where an agent fails. Vulnerability discovery is a composite ability: an agent must comprehend source code, infer input constraints, construct inputs, execute them, and iteratively correct from feedback. We recast its measurement as an input-prediction task with a c...
| Authors | Yuanxiang Shi, Jiayi Lin, Xuanyong Lin et al. |
|---|---|
| Primary category | cs.SE |
| Published | 5 September 2026 |
| Last updated at source | 5 September 2026 |
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