一句话：提出 Magneto，利用 LLM 赋能的定向模糊测试逐步探索多步调用链，以利用依赖库中的漏洞。
开源第三方库的广泛使用会将漏洞通过依赖链传播至下游项目。现有的漏洞利用分析虽能降低漏洞可达性分析的误报，但当脆弱函数由客户端项目通过多步调用链间接调用时效果不佳。为此，本文提出 Magneto，一种逐步利用依赖库漏洞的方法，其核心是 LLM 赋能的定向模糊测试，能够逐步探索调用链并触发脆弱函数。摘要未给出具体评估结果，但该工作旨在提升多步调用链场景下的漏洞利用分析有效性。
The wide adoption of open source third-party libraries can propagate vulnerabilities that originally exist in third-party libraries through dependency chains to downstream projects. To mitigate this security risk, vulnerability exploitation analysis has been proposed to further reduce false positives of vulnerability reachability analysis. However, existing approaches work less effectively when the vulnerable function of the vulnerable library is indirectly invoked by a client project through a call chain of multiple steps.
DOI 原文 ·
@inproceedings{paperbot8322,
title = {Magneto: A Step-Wise Approach to Exploit Vulnerabilities in Dependent Libraries via LLM-Empowered Directed Fuzzing},
author = {Zhuotong Zhou and Yongzhuo Yang and Susheng Wu and Yiheng Huang and Bihuan Chen and Xin Peng},
booktitle = {ASE},
year = {2024},
doi = {10.1145/3691620.3695531}
}