GoodVibe：基于LLM代码生成的安全振动防护机制
GoodVibe: Security-by-Vibe for LLM-Based Code Generation
February 11, 2026
作者: Maximilian Thang, Lichao Wu, Sasha Behrouzi, Mohamadreza Rostami, Jona te Lintelo, Stjepan Picek, Ahmad-Reza Sadeghi
cs.AI
摘要
在快速非正式的开发流程中，大语言模型正被越来越多地用于代码生成。这类被称为氛围编程的场景优先考虑速度和便利性，安全需求往往未被明确定义。在此环境下，模型常生成功能正确但存在安全隐患的代码，形成日益增长的安全风险。现有提升代码安全性的方法依赖全参数微调或参数高效适配，前者成本高昂且易引发灾难性遗忘，后者操作粒度粗糙且可解释性与可控性有限。
我们提出GoodVibe——一种神经元级框架，旨在默认状态下提升代码语言模型的安全性。该框架基于关键发现：与安全相关的推理过程仅集中于少量神经元子集。我们通过监督式安全任务的梯度归因分析定位这些神经元，并实施选择性神经元微调，仅更新安全关键子空间。为降低训练成本，我们引入激活驱动的神经元聚类技术，以最小开销实现结构化更新。
我们在涵盖C++、Java、Swift和Go等安全关键编程语言的六个大语言模型上评估GoodVibe。该框架在保持模型通用能力的同时显著提升生成代码的安全性：相较基线模型实现最高2.5倍的安全改进；以仅4700分之1的可训练参数量达到或超越全参数微调效果；与参数高效基准方法相比，训练计算量降低3.6倍以上。实验结果表明，神经元级优化为实现安全代码生成提供了兼顾效能与扩展性的解决方案，且无需牺牲效率或通用性。
English
Large language models (LLMs) are increasingly used for code generation in fast, informal development workflows, often referred to as vibe coding, where speed and convenience are prioritized, and security requirements are rarely made explicit. In this setting, models frequently produce functionally correct but insecure code, creating a growing security risk. Existing approaches to improving code security rely on full-parameter fine-tuning or parameter-efficient adaptations, which are either costly and prone to catastrophic forgetting or operate at coarse granularity with limited interpretability and control.
We present GoodVibe, a neuron-level framework for improving the security of code language models by default. GoodVibe is based on the key insight that security-relevant reasoning is localized to a small subset of neurons. We identify these neurons using gradient-based attribution from a supervised security task and perform neuron-selective fine-tuning that updates only this security-critical subspace. To further reduce training cost, we introduce activation-driven neuron clustering, enabling structured updates with minimal overhead. We evaluate GoodVibe on six LLMs across security-critical programming languages, including C++, Java, Swift, and Go. GoodVibe substantially improves the security of generated code while preserving general model utility, achieving up to a 2.5x improvement over base models, matching or exceeding full fine-tuning with over 4,700x fewer trainable parameters, and reducing training computation by more than 3.6x compared to the parameter-efficient baseline (LoRA). Our results demonstrate that neuron-level optimization offers an effective and scalable approach to securing code generation without sacrificing efficiency or generality.