arXiv:2507.22063v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) for code generation (i.e., Code LLMs) have demonstrated impressive capabilities in AI-assisted software development and testing. However, recent studies have shown that these models are prone to generating vulnerable or even malicious code under adversarial settings. Existing red-teaming approaches rely on extensive human effort, limiting their scalability and practicality, and generally overlook the interactive nature of real-world AI-assisted programming, which often unfolds over multiple turns. To bridge
The rapid deployment and increasing reliance on Code LLMs necessitates robust security and red-teaming methodologies to address discovered vulnerabilities and adversarial exploits.
The widespread adoption of AI-assisted software development hinges on the trustworthiness and security of code generated by LLMs, making automated red-teaming a critical research area.
This development proposes a method to automate and scale the red-teaming process for Code LLMs, addressing a key limitation in ensuring their safety and reliability in multi-turn programming environments.
- · AI software developers
- · Cybersecurity firms
- · Organizations adopting Code LLMs
- · Malicious actors
- · Software vulnerabilities
- · Manual red-teaming services
Automated red-teaming improves the security and reliability of code generated by LLMs.
Increased trust in Code LLMs accelerates their integration into critical software development pipelines, reducing human effort and error.
More secure, AI-generated code could lead to entirely new paradigms in software development and autonomous system design, but also new attack surfaces for sophisticated adversaries.
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Read at arXiv cs.AI