通过不变量的反例引导的定向Fuzzing
Counterexample-Guided Directed Fuzzing via Likely Invariant Inference 引用：Huang H, Zhou A, Payer M, et al. Everything is Good for Something: Counterexample-Guided Directed Fuzzing via Likely Invariant Inference[C], 2024 IEEE Symposium on Security and Privacy (SP). IEEE Computer Society, 2024: 142-142. CCF A S&P , IEEE Symposium on Security and Privacy ..
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不变量作为fuzzers的反馈
The use of likely invariants as feedback for fuzzers 引用：Fioraldi A, D’Elia D C, Balzarotti D. The use of likely invariants as feedback for fuzzers[C], 30th USENIX Security Symposium (USENIX Security 21). 2021: 2829-2846. CCF A USENIX Security 链接 代码 0 摘要 动机 模糊测试已经被证明是发现程序漏洞的有效技术，但仍存在一些开放的挑战。 其中一个主要挑战是，广受欢迎的基于覆盖率引导的模糊测试会尽可能的将测试用例覆盖程序的不同部分，但仅仅考虑覆盖率可能并不能充..
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