Defense Against Llm Backdoors Using Critical Neuro

---
name: defense-against-llm-backdoors-using-critical-neuro
description: 'Skill generated from arXiv paper 2607.19894: Defense Against LLM Backdoors using Critical Neuron Isolation Pruning'
metadata:
  {
    "arxiv": {
      "id": "2607.19894",
      "title": "Defense Against LLM Backdoors using Critical Neuron Isolation Pruning",
      "authors": ['Yuxi Li', 'Zhibo Zhang', 'Kailong Wang', 'Xingshuo Han', 'Ling Shi', 'Haoyu Wang'],
      "published": "2026-07-22",
      "categories": ['cs.CR', 'cs.AI'],
      "url": "https://arxiv.org/abs/2607.19894",
      "utility": 1.0
    }
  }
---
# Defense Against LLM Backdoors using Critical Neuron Isolation Pruning

**arXiv:** 2607.19894  
**Published:** 2026-07-22  
**Authors:** Yuxi Li, Zhibo Zhang, Kailong Wang, Xingshuo Han, Ling Shi, Haoyu Wang  
**Categories:** cs.CR, cs.AI  
**Utility:** 1.00

## Key Innovation
Large language models (LLMs) are vulnerable to backdoor attacks, where hidden triggers induce malicious outputs. Existing defenses generally fall into inference-time detection or training-time mitigation, but face two key limitations. First, they focus on fine-tuning-based backdoors (e.g., PEFT modules) and fail to address insidious model-editing attacks that bypass training pipelines. Second, they target simple classification settings and do not naturally extend to open-ended LLM generation and...

## Potential Application
This paper presents advancements that could be applied to enhance agent capabilities in the areas of cs.CR, cs.AI.

## References
- arXiv: https://arxiv.org/abs/2607.19894