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Psyzkaller: Learning from Historical and On-the-Fly Execution Data for Smarter Seed Generation in OS kernel Fuzzing
Boyu Liu, Yang Zhang, Liang Cheng +6
OS Kernel fuzzers such as Syzkaller often struggle to generate syscall sequences that respect intrinsic Syscall Dependency Relations (SDRs), resulting in seeds that either violate…
Automatic Generation of a Cryptography Misuse Taxonomy Using Large Language Models
Yang Zhang, Wenyi Ouyang, Yi Zhang +3
The prevalence of cryptographic API misuse (CAM) is compromising the effectiveness of cryptography and in turn the security of modern systems and applications. Despite extensive ef…
Fuzzing Based on Function Importance by Interprocedural Control Flow Graph
Wenshuo Wang, Liang Cheng, Yang Zhang
Coverage-based graybox fuzzer (CGF), such as AFL has gained great success in vulnerability detection thanks to its ease-of-use and bug-finding power. Since some code fragments such…
Optimizing seed inputs in fuzzing with machine learning
Liang Cheng, Yang Zhang, Yi Zhang +4
The success of a fuzzing campaign is heavily depending on the quality of seed inputs used for test generation. It is however challenging to compose a corpus of seed inputs that ena…