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

arXiv: https://arxiv.org/abs/2606.19474 · HTML: https://mlsanity.com/paper/2606.19474/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 23 references that are in the map.
- First posted on arXiv 17 Jun 2026.

## Builds on (most cited first, 7 of 7)

- [Asleep at the Keyboard? Assessing the Security of GitHub Copilot's Code Contributions](https://mlsanity.com/paper/2108.09293/) (2021)
- [Do Users Write More Insecure Code with AI Assistants?](https://mlsanity.com/paper/2211.03622/) (2022)
- [Security Degradation in Iterative AI Code Generation -- A Systematic Analysis of the Paradox](https://mlsanity.com/paper/2506.11022/) (2025)
- [Cognitive Biases in LLM-Assisted Software Development](https://mlsanity.com/paper/2601.08045/) (2026)
- [Faster Code, Deeper Debt? A Multivocal Literature Review on Technical Debt and Its Early Signs in LLM-Assisted Software Development](https://mlsanity.com/paper/2606.14796/) (2026)
- [Developers' Privacy Education: A game framework to stimulate secure coding behaviour](https://mlsanity.com/paper/2211.03498/) (2022)
- [When Security Meets Usability: An Empirical Investigation of Post-Quantum Cryptography APIs](https://mlsanity.com/paper/2602.14539/) (2026)

## Abstract (arXiv)

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.