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October 2, 2025Open Access

A Mixture of Linear Corrections Generates Secure Code

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Authors

WYWeichen YuRMRavi MangalTZTerry Yue Zhuo

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Overview

Analysis reveals large language models can enhance code safety using vulnerability-sensitive representations, indicating effective prompting can improve outcomes.

Key Points

  • MoC improves the security ratio of Qwen2.5-Coder-7B by 8.9%, while enhancing functionality by 2.1%.
  • Current LLMs encode internal representations that effectively distinguish between vulnerable and secure code.
  • Using representation engineering techniques reveals a greater accuracy of LLMs over standard prompting in identifying vulnerabilities.
  • The introduction of inference-time steering allows for controlled vulnerability management during code generation.

Cite This Study

Yu et al. (2025) studied this question.

synapsesocial.com/papers/68de5da283cbc991d0a20879https://doi.org/10.48550/arxiv.2507.09508
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Guiding AI to Fix Its Own Flaws: An Empirical Study on LLM-Driven Secure Code Generation2026
  2. 2Explicit Vulnerability Generation with LLMs: An Investigation Beyond Adversarial Attacks2025
  3. 3A Comprehensive Study of the Capabilities of Large Language Models for Vulnerability Detection2024 · 17 citations
  4. 4Software Vulnerability and Functionality Assessment using LLMs2024 · 4 citations
  5. 5Understanding Defects in Generated Codes by Language Models2024