Curvature-Guided Module Localization for Low-Rank Detoxification of Backdoored Large Language Models
arXiv:2606.30899v1 Announce Type: cross Abstract: Backdoor attacks pose a serious threat to large language models (LLMs) by causing otherwise benign systems to produce attacker-specified malicious behavior when a hidden trigger is present. In this work, we study post hoc detoxification of backdoored LLMs in a practical setting where the defender has access to the poisoned model but does not wish to retrain the full network from scratch. We propose a mechanistically guided weight-space repair framework that first localizes modules involved in propagating trigger-induced behavior using activatio
The proliferation of powerful LLMs necessitates robust methods for mitigating security vulnerabilities like backdoor attacks, especially as these models are integrated into critical systems.
This work addresses a significant security threat to AI, enabling safer deployment of LLMs and protecting against malicious manipulation that could undermine trust and functionality.
The ability to 'detoxify' backdoored LLMs without full retraining significantly reduces the operational burden and cost associated with securing these complex models following an attack.
- · AI developers and deployers
- · Cybersecurity firms
- · Organizations using LLMs in sensitive applications
- · Actors attempting backdoor attacks on LLMs
- · Less secure AI models
Improved security and trustworthiness of large language models.
Increased adoption of LLMs in high-stakes environments due to enhanced reliability assurances.
A potential arms race between AI security researchers developing detoxification methods and attackers creating more sophisticated backdoor techniques.
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Read at arXiv cs.AI