arXiv:2606.19474v1 Announce Type: cross Abstract: The transition to Post Quantum Cryptography (PQC) introduces considerable implementation complexity, requiring strict adherence to constant-time execution, side channel resistance, and precise parametrisation. Simultaneously, large language models (LLMs) are heavily embedded in software development workflows, including cryptographic engineering. While LLMs improve productivity, evidence shows that they frequently generate insecure or suboptimal code, particularly in security critical domains. This paper introduces Secure Coding Drift in PQC, a
The increasing reliance on LLMs in software development, particularly for critical infrastructure like post-quantum cryptography, makes the detection and mitigation of generated security vulnerabilities an immediate concern.
This highlights the inherent risks of integrating AI into core security protocols, potentially undermining the very protections they are designed to establish.
The deployment of PQC solutions will require more stringent validation processes and potentially new development methodologies, as current LLM integration introduces significant risk.
- · Cybersecurity consultancies specializing in AI-generated code audits
- · Developers of secure coding tools and gamified training platforms
- · Organizations focused on cryptographic formal verification
- · Organizations relying solely on LLMs for PQC implementation
- · Developers lacking specialized secure coding expertise in PQC
- · LLM providers if they fail to address security generation issues
LLMs generating insecure cryptographic code introduce significant vulnerabilities into critical infrastructure.
Increased investment in bespoke secure coding education and validation tools will be necessary to bridge the gap.
This could lead to a two-tier system of cryptographic development, with highly vetted, secure manual coding for critical systems and LLM-assisted development for less sensitive applications.
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Read at arXiv cs.AI