Topic
secure coding
Researchers Identify 'Secure Coding Drift' Threat in LLM-Assisted Post-Quantum Cryptography Development
A research paper introduces 'Secure Coding Drift in PQC', a socio-technical vulnerability where sustained reliance on LLM-generated code gradually degrades secure coding practices. The authors propose a gamified, LLM-augmented secure coding framework that embeds adversarial evaluation, behavioural feedback, and security scoring into development workflows to mitigate this drift.
Tree-like Self-Play Framework Teaches LLMs to Fix Security Flaws in Code Generation
Researchers introduce Tree-like Self-Play (TSP), a framework that treats secure code generation as a fine-grained sequential decision process. TSP significantly outperforms standard supervised fine-tuning (SFT) and reinforcement learning (RL) on Python security benchmarks, achieving a 75.8% pass rate and reducing unseen vulnerabilities by 24.5% while generalising across programming languages.