FuzzEval：评估模糊测试器生成上下文敏感输入的能力
FuzzEval: Assessing Fuzzers on Generating Context-Sensitive Inputs
AI总结：
本文评估了十一种先进模糊测试器为密码标准PKCS#1-v1.5的十三个实现生成上下文敏感输入的能力，揭示了其在输入有效性和多样性上的差异及现有局限，以推动相关研究。
摘要：
密码协议构成了现代安全系统的支柱，然而其实现中仍然存在漏洞。传统的测试技术，包括模糊测试，由于依赖上下文敏感输入，一直难以有效识别密码库中的漏洞。本文对十一种最先进的模糊测试器在生成用于测试密码标准PKCS#1-v1.5的上下文敏感输入方面的能力进行了全面评估，涉及十三个实现。我们的研究揭示了各模糊测试器在生成输入的有效性和多样性方面的细微性能差异。这项调查强调了现有模糊测试器在处理上下文敏感输入方面的局限性。这些发现预计将推动该领域的进一步研究和发展。
英文摘要：
Cryptographic protocols form the backbone of modern security systems, yet vulnerabilities persist within their implementations. Traditional testing techniques, including fuzzing, have struggled to effectively identify vulnerabilities in cryptographic libraries due to their reliance on context-sensitive inputs. This paper presents a comprehensive evaluation of eleven state-of-the-art fuzzers' ability to generate context-sensitive inputs for testing a cryptographic standard, PKCS#1-v1.5, across thirteen implementations. Our study reveals nuanced performance differences among the fuzzers in terms of the validity and diversity of the produced inputs. This investigation underscores the limitations of existing fuzzers in handling context-sensitive inputs. These findings are expected to drive further research and development in this area.
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。
