arXiv AI By Yuanxiang Shi, Jiayi Lin, Xuanyong Lin, Liangcai Su, Yeheng Duan, Wei Wang, Qi Han, Bing Zhao, Wei Hu, Xander Xu, Chenxiong Qian

SWE-Test: Benchmarking LLM Vulnerability Discovery via Input Prediction

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arXiv Machine Learning
Aug 27

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

FuzzingBrain‑Bench V1 is a new benchmark that tests large language models (LLMs) on their ability to discover software bugs in open‑source projects. Unlike prior benchmarks that focus on a single target vulnerability, this benchmark gives models a Docker‑based harness and asks them to generate inputs that trigger as many distinct crashes as possible. The first version contains 77 challenges from 43 projects (36 C, 32 C++, 9 Java/JVM) and evaluates Claude Haiku 4.5, Claude Sonnet 4.6, and Claude Opus 4.8, with Claude Opus 4.8 achieving the highest score by triggering crashes in 60 of 77 challenges.

By Ze Sheng, Aleksandar Kezic, Zhicheng Chen, Jeff Huang