Understanding and Improving Model Editing for Secure Code Generation

2026-08-07

AI Summary

This work addresses the critical issue that large language models (LLMs) often reproduce security vulnerabilities present in their training data during code generation. While existing inference-time hardening techniques incur runtime overhead and cannot alter the model’s internal knowledge, this study systematically evaluates model editing as a model-level hardening mechanism for secure code generation. The authors propose SafeEdit, a novel approach that integrates task-oriented fine-tuning with edit-aware regularization to effectively mitigate the trade-off between enhanced security and functional correctness. Extensive experiments across eight prominent LLMs demonstrate that SafeEdit significantly outperforms baseline methods, achieving up to a 15.50 percentage point improvement in Pass@1 accuracy and a 7.54%–12.04% increase in security rate over CoSec, while setting new state-of-the-art performance in joint security and functionality preservation.

Abstract

Large language models (LLMs) are widely used for code generation, yet they can reproduce vulnerable implementations learned from insecure training patterns. Prior work has mainly explored inference-time hardening, which reduces insecure generations without modifying the target model but relies on auxiliary components and adds runtime overhead. We conduct the first systematic study of model editing as a model-level hardening mechanism for secure code generation. We evaluate 3 state-of-the-art editing methods across diverse LLM families and compare them with CoSec, a representative inference-time approach, focusing on security, robustness, generalization, and functional correctness. Model editing yields larger security gains than CoSec on seen vulnerability types, improving security ratios by 15%-25% over vanilla models, with gains remaining stable under prompt perturbations. However, these improvements transfer unreliably to unseen vulnerabilities and can reduce functional correctness. To mitigate this trade-off, we propose SafeEdit, a post-edit refinement method combining functional tuning with edit-aware regularization. Across eight target LLMs, SafeEdit improves Pass@1 over UltraEdit by 11.73/13.70/15.50 percentage points at T=0.1/0.4/0.8 while largely preserving security. Compared with CoSec, it achieves relative security-ratio gains of 7.54%-12.04%. Additional evaluation on CodeGuard+ confirms improved joint secure-and-correct generation. SafeEdit and CoSec are also complementary, and their combination can further improve security while maintaining strong functional correctness. Overall, our results provide evidence-backed guidance for applying model editing to secure code generation.

Problem

• model editing
• secure code generation
• vulnerability mitigation
• functional correctness
• large language models

Innovation

• model editing
• secure code generation
• SafeEdit
• functional correctness
• vulnerability mitigation