有状态模糊测试真的具有挑战性吗？
Is Stateful Fuzzing Really Challenging?
AI总结：
本文探讨有状态模糊测试工具难以设计与基准评估的原因，对比无状态场景中AFL等成熟工具，指出有状态系统缺乏明确最佳选择。
摘要：
模糊测试已被证明在发现软件漏洞方面极其有效。当涉及对无状态系统进行模糊测试时，分析人员对要做出的选择毫不怀疑。事实上，在过去20年设计的众多无状态模糊测试工具中，AFL（及其后代AFL++和LibAFL）凭借其有效性、速度和发现缺陷的能力脱颖而出。另一方面，在处理有状态系统时，尚不清楚哪种工具最适合使用。事实上，研究界在设计（和基准测试）有效且通用的有状态模糊测试工具方面举步维艰。在这篇短论文中，我们讨论了导致有状态模糊测试工具难以设计和基准测试的原因。
英文摘要：
Fuzzing has been proven extremely effective in finding vulnerabilities in software. When it comes to fuzz stateless systems, analysts have no doubts about the choice to make. In fact, among the plethora of stateless fuzzers devised in the last 20 years, AFL (with its descendants AFL++ and LibAFL) stood up for its effectiveness, speed and ability to find bugs. On the other hand, when dealing with stateful systems, it is not clear what is the best tool to use. In fact, the research community struggles to devise (and benchmark) effective and generic stateful fuzzers. In this short paper, we discuss the reasons that make stateful fuzzers difficult to devise and benchmark.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
