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

ArXi:2602.20213v2 Announce Type: replace-cross 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 automated agent framework dedicated to generating targeted adversarial test cases that expose latent vulnerabilities in program submissions.