arXiv:2512.21132v2 Announce Type: replace-cross Abstract: As large language models (LLMs) see wide adoption in software engineering, the reliable assessment of the correctness and security of LLM-generated code is crucial. Notably, prior work showed that LLMs are prone to generating code with security vulnerabilities, highlighting that security is often overlooked. These insights were enabled by specialized benchmarks crafted by security experts through significant manual effort. However, benchmarks (i) inevitably end up contaminating training data, (ii) must extend to new tasks to provide a m
The rapid adoption of LLMs in software engineering necessitates immediate solutions for ensuring the security of generated code, as reliance on manual benchmarking is unsustainable and LLMs frequently produce vulnerabilities.
The reliable assessment of LLM-generated code security is crucial for preventing widespread vulnerabilities in future software, impacting industry adoption, regulatory frameworks, and overall digital security.
The ability to automatically bootstrap code security benchmarks will expedite the development of more secure LLM-generated code, reducing manual effort and evolving with the rapid pace of LLM development.
- · Cybersecurity firms
- · Software developers using LLMs
- · AI model developers
- · Organisations adopting LLM-generated code
- · Malicious actors exploiting code vulnerabilities
- · Companies relying on outdated security testing methods
Automated security benchmarking tools for LLM-generated code will become a standard part of the software development lifecycle.
Improved security will accelerate the integration of LLMs into critical infrastructure and enterprise systems, leading to higher efficiency but also new attack surfaces.
The enhanced security of LLM-generated code could reduce the overall cybersecurity burden, allowing resources to be reallocated to more advanced threats or AI safety research.
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