---
id: 20260506-T0-04
title: "LLM越狱成功的原因被定位：少数神经元触发即可绕过安全对齐"
title_en: "Researchers Trace LLM Jailbreaks to a Small Set of Neurons"
url: https://ai.daily.yangsir.net/daily/20260506-T0-04
issue_date: 2026-05-06
publish_date: 2026-05-05T04:00:00.000Z
category: research
source_name: "arXiv cs.AI"
source_url: https://arxiv.org/abs/2605.00123
---

# LLM越狱成功的原因被定位：少数神经元触发即可绕过安全对齐

arXiv发表的新论文揭示了经过安全训练的大语言模型（LLM）容易被“越狱”的具体机制。研究发现，模型在面对有害请求时的妥协，可以归结为模型内部极少数特定神经元和特征的激活。通过提取并分析这些局部、因果级别的特征解释，研究团队准确定位了导致安全机制失效的关键节点。这一发现为AI安全领域提供了具体的干预靶点，开发者和安全团队能据此在模型训练阶段进行更精准的防御性微调，从底层机制上封堵漏洞，而不是单纯依靠外围的关键词过滤。

## English Version

**Researchers Trace LLM Jailbreaks to a Small Set of Neurons**

A new paper on arXiv uncovers why safety-trained large language models (LLMs) frequently fall victim to jailbreak prompts. The study reveals that successful harmful outputs can be traced back to the activation of a very small set of specific neurons within the model. By providing minimal, local, and causal explanations, researchers pinpointed the exact internal components that override safety alignments. This breakthrough gives developers specific targets for safety interventions, allowing for more precise defensive fine-tuning during training to fundamentally block exploits rather than relying on superficial prompt filtering.

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**来源**：[arXiv cs.AI](https://arxiv.org/abs/2605.00123)

**详情页**：https://ai.daily.yangsir.net/daily/20260506-T0-04

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