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Introducing AutoPatchBench: A Benchmark for AI-Powered Security Fixes
- AutoPatchBench is introduced as a benchmark for AI automated repair of vulnerabilities found through fuzzing, facilitating objective evaluation of AI program repair systems.
- It aims to enhance the development of more secure solutions and promote collaboration within the security community.
- It includes 136 C/C++ vulnerabilities, verified fixes, and aims to evaluate AI-driven repair tools effectively.
- AutoPatchBench aims to automate the process of fixing fuzzing-found vulnerabilities by utilizing AI's efficiency in analyzing and proposing solutions.
- It involves steps like analyzing crashes, pinpointing root causes, patching code, and verifying fix accuracy.
- AutoPatchBench overcomes challenges in AI-driven program repair by creating a specific benchmark for fuzzing-found vulnerabilities.
- It includes a curated subset from the ARVO dataset, focusing on C/C++ vulnerabilities with automated verification processes.
- The benchmark emphasizes criteria like valid vulnerability, reproducibility, stack trace, compilation success, and more for robust evaluation.
- AutoPatchBench-Lite subset focuses on simpler vulnerabilities to cater to tools in early stages of development.
- The process involves patch generation, post-verification with fuzzing and differential testing, and considerations for improving patch accuracy.
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