这篇论文测试了LLM重复找漏洞的稳定性，发现Claude匹配结果很稳，但自己新发现的漏洞随机性高。建议和安全工具搭配用，别靠它单干。
该论文通过300次重复漏洞扫描，测量了LLM在JavaScript代码安全审查中的可重复性。在250次模型运行中，80个唯一不匹配发现仅出现在一次重复中，22个出现在全部五次。相比之下，Claude匹配Snyk Code参考发现时更稳定：134个唯一匹配发现出现在全部五次。Snyk Code SAST是确定性的，能系统枚举重复数据流汇点。结果表明，将agentic LLM审查与确定性SAST结合比单独使用任一方法更好。
Snyk VulnBench JS 1.0: Can LLMs Find the Same Bugs Twice?
We ran 300 repeated vulnerability-finding scans to measure how repeatable agentic large language model (LLM) security review is on the same JavaScript code, prompt, and benchmark harness. The headline result is that LLM security findings were unevenly repeatable: reference-matched findings were stable, but extra model reports varied heavily from run to run. Across 250 model runs, 80 of 161 unique unmatched findings appeared in only one of five identical repetitions, while only 22 appeared in all five. By contrast, when Claude matched a Snyk Code reference finding, the behavior was much more stable: 134 of 158 unique reference-matched findings appeared in all five repetitions. The benchmark also shows complementarity. Models consistently found familiar, high-signal exploit shapes, and in one case surfaced a likely Snyk Code product gap. Snyk Code static application security testing (SAST) was deterministic and better at systematically enumerating repeated data-flow sinks. The results support combining agentic LLM review with deterministic SAST rather than treating either technique as a replacement for the other.