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Steelix: program-state based binary fuzzing., , , , , и . ESEC/SIGSOFT FSE, стр. 627-637. ACM, (2017)An Empirical Study on Benchmarks of Artificial Software Vulnerabilities., , , , , и . CoRR, (2020)Demystifying RCE Vulnerabilities in LLM-Integrated Apps., , , , и . CoRR, (2023)DiffChaser: Detecting Disagreements for Deep Neural Networks., , , , , и . IJCAI, стр. 5772-5778. ijcai.org, (2019)ASTER: Automatic Speech Recognition System Accessibility Testing for Stutterers., , , , , , , , , и . ASE, стр. 510-521. IEEE, (2023)Leopard: identifying vulnerable code for vulnerability assessment through program metrics., , , , , , и . ICSE, стр. 60-71. IEEE / ACM, (2019)AutoCom: Automatic Comment Generation for C Code., и . SEKE, стр. 632-633. KSI Research Inc., (2021)PentestGPT: An LLM-empowered Automatic Penetration Testing Tool., , , , , , , , , и . CoRR, (2023)Locating vulnerabilities in binaries via memory layout recovering., , , , , , , и . ESEC/SIGSOFT FSE, стр. 718-728. ACM, (2019)Principled Greybox Fuzzing.. ICFEM, том 11232 из Lecture Notes in Computer Science, стр. 455-458. Springer, (2018)