Inference-Time Safety For Code LLMs Via Retrieval-Augmented Revision
Large Language Models (LLMs) are increasingly deployed for code generation in high-stakes software development, yet their limited transparency in security reasoning and brittleness to evolving vulnerability patterns raise critical trustworthiness concerns. Models trained on static datasets cannot readily adapt to newly discovered vulnerabilities or changing security standards without retraining, leading to the repeated generation of unsafe code. We present a principled approach to trustworthy code generation by design that operates as an inference-time safety mechanism. Our approach employs retrieval-augmented generation to surface relevant security risks in generated code and retrieve re...
| Authors | Manisha Mukherjee, Vincent J. Hellendoorn |
|---|---|
| Primary category | cs.SE |
| Published | 2 March 2026 |
| Last updated at source | 2 March 2026 |
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