arXiv:2606.31159v1 Announce Type: cross Abstract: Large Language Models (LLMs) are rapidly transforming software development, yet their use in security-critical contexts raises a key question: do models know when their generated code is insecure? This property, known as calibration, measures whether a model's confidence aligns with the true correctness of its outputs. We present the first large-scale empirical study of security calibration in LLM-generated code. We evaluate GPT-4o-mini, Gemini-2.0-Flash, and Qwen3-Coder-Next across multiple temperature settings on two complementary benchmarks:
The rapid expansion of LLMs into critical software development roles necessitates an immediate understanding of their inherent security limitations and failure modes.
This empirical study provides crucial insights into the trustworthiness and inherent risks of using LLMs for code generation, particularly in security-sensitive applications, impacting adoption rates and regulatory frameworks.
Understanding LLMs' security calibration will directly influence industrial best practices for integrating AI into software development, potentially leading to new testing paradigms and certification requirements.
- · Cybersecurity firms
- · Security testing platforms
- · Developers skilled in secure AI integration
- · LLM providers with poor security calibration
- · Organizations deploying LLMs in critical code without robust validation
- · Code review processes relying solely on LLM output
Increased scrutiny and demand for 'security-calibrated' LLMs and code generation tools.
Development of new AI-powered security analysis tools specifically designed to audit LLM-generated code for vulnerabilities.
Shifting liability models in software development, with greater emphasis on the 'secure-by-design' principles for AI-assisted coding.
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Read at arXiv cs.LG