Secure Coding Drift in LLM-Assisted Post-Quantum Cryptography Development: A Gamified Fix

Computer Science > Cryptography and Security [Submitted on 17 Jun 2026] Secure Coding Drift in LLM-Assisted Post-Quantum Cryptography Development: A Gamified Fix R.D.N. Shakya, C.P. Wijesiriwardana, S.M. Vidanagamachchi, Nalin A.G. Arachchilage 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 novel socio technical vulnerability model capturing the gradual degradation of secure coding practices due to sustained reliance on LLM-generated code. Unlike prior work that focuses on static vulnerabilities, we conceptualise security risk as a longitudinal behavioural phenomenon rising from human AI interaction. To mitigate this, we propose a gamified, LLM augmented secure coding framework that embeds adversarial evaluation, behavioural feedback, and security scoring into development workflows. Our approach reframes LLMs from passive assistants into active security co-pilots, contributing toward safer PQC implementation in AI mediated environments. Comments: Accepted for 2026 SIGIR Workshop on Vulnerabilities in Generative Systems for Information Retrieval track Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Software Engineering (cs.SE) ACM classes: E.3; D.2.4; D.2.5; I.2.7 Cite as: arXiv:2606.19474 [cs.CR]   (or arXiv:2606.19474v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2606.19474 Focus to learn more Submission history From: Dinithi Nadee Shakya Rathnaikage [view email] [v1] Wed, 17 Jun 2026 18:10:10 UTC (467 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-06 Change to browse by: cs cs.AI cs.SE References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)