CodeHacker: Automated Test Case Generation for Detecting Vulnerabilities in Competitive Programming Solutions
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 automated agent framework dedicated to generating targeted adversarial test cases that expose latent vulnerabilities in program submissions. Mimicking the hack mechanism in competitive programming, CodeHacker employs a multi-strategy approach, including str
The rapid advancement and deployment of Large Language Models (LLMs) for code generation are highlighting the critical need for robust evaluation methods, making automated test case generation a timely innovation.
This development addresses a key vulnerability in AI-generated code, aiming to improve the reliability and security of software built with LLMs, which is crucial for their broader adoption in sensitive applications.
The ability to automatically generate adversarial tests will lead to more robust LLM-powered code solutions, reducing the likelihood of exploitable flaws and increasing trust in AI-assisted development.
- · Software developers
- · Cybersecurity firms
- · LLM developers
- · Competitive programming platforms
- · Malicious actors exploiting code vulnerabilities
- · Companies with weak code testing practices
Automated code vulnerability detection using LLMs themselves becomes a standard practice.
This leads to a higher industry standard for code quality and security across all AI-generated software.
Increased reliability of AI-generated code could accelerate the adoption of fully autonomous software development agents, impacting the software engineering job market.
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Read at arXiv cs.AI