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CodeHacker: Automated Test Case Generation for Detecting Vulnerabilities in Competitive Programming Solutions

Jingwei Shi, Xinxiang Yin, Jing Huang, Jinman Zhao, Shengyu Tao
Jun 3, 2026 at 04:00
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arXiv:2602.20213v2 Announce Type: replace-cross Abstract: The evaluation of Large Language Models (LLMs) for code generation relies heavily on the quality and robustness of test cases. However, existing benchmarks often lack coverage for subtle corner cases, allowing incorrect solutions to pass. To bridge this gap, we propose CodeHacker, an...

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