标题： LibLMFuzz：用于黑盒库的LLM增强模糊目标生成 显示英文标题

标题： LibLMFuzz: LLM-Augmented Fuzz Target Generation for Black-box Libraries

摘要： 网络安全和计算机科学中的一个基本问题是确定程序是否没有错误和漏洞。 模糊测试是一种流行的发现程序中漏洞的方法，尽管它在初始设置和持续维护方面有投资成本，但相比其他策略具有多方面的优势。 当只能获得二进制库时，例如闭源和专有软件的情况，选择模糊测试会变得更加复杂。 为此，我们引入了LibLMFuzz，这是一个框架，通过将代理大型语言模型（LLM）与轻量级工具链（反汇编器/编译器/模糊测试器）相结合，来降低对闭源库进行模糊测试的相关成本，从而自主分析剥离的二进制文件，规划模糊策略，生成驱动程序，并迭代地自我修复构建或运行时错误。 在四个广泛使用的Linux库上进行测试，LibLMFuzz为所有558个可模糊的API函数生成了语法正确的驱动程序，实现了100%的API覆盖率，且无需人工干预。 在生成的1601个驱动程序中，75.52%在首次执行时名义上是正确的。 结果表明，增强型中间件在降低模糊测试黑盒组件的成本方面具有前景，并为未来的研究工作提供了基础。 未来的研究机会存在于分支覆盖领域。 显示英文摘要

摘要： A fundamental problem in cybersecurity and computer science is determining whether a program is free of bugs and vulnerabilities. Fuzzing, a popular approach to discovering vulnerabilities in programs, has several advantages over alternative strategies, although it has investment costs in the form of initial setup and continuous maintenance. The choice of fuzzing is further complicated when only a binary library is available, such as the case of closed-source and proprietary software. In response, we introduce LibLMFuzz, a framework that reduces costs associated with fuzzing closed-source libraries by pairing an agentic Large Language Model (LLM) with a lightweight tool-chain (disassembler/compiler/fuzzer) to autonomously analyze stripped binaries, plan fuzz strategies, generate drivers, and iteratively self-repair build or runtime errors. Tested on four widely-used Linux libraries, LibLMFuzz produced syntactically correct drivers for all 558 fuzz-able API functions, achieving 100% API coverage with no human intervention. Across the 1601 synthesized drivers, 75.52% were nominally correct on first execution. The results show that LLM-augmented middleware holds promise in reducing the costs of fuzzing black box components and provides a foundation for future research efforts. Future opportunities exist for research in branch coverage.