Dummy Backdoor as a Defense: Removing Unknown Backdoors via Shared Internal Mechanisms for Generative LLMs
arXiv:2606.11648v1 Announce Type: cross Abstract: Backdoor attacks pose a serious threat to the safety and reliability of Large Language Models (LLMs), as they cause models to behave normally on clean inputs while producing attacker-specified responses when hidden triggers are present. Removing such unknown backdoors is particularly challenging when the defender does not know the backdoor attack types or the internal mechanisms formed through backdoor training. In this work, we propose a simple but effective backdoor removal method based on shared internal mechanisms across different backdoors
The proliferation of LLMs and their integration into critical systems necessitates robust defenses against subtle and sophisticated backdoor attacks, making this research timely.
This development is crucial for ensuring the trustworthiness and security of AI systems, especially generative LLMs which are increasingly deployed in sensitive applications.
The ability to remove unknown backdoors without prior knowledge of attack types fundamentally shifts the defensive posture from reactive to more proactive and generalist.
- · AI security researchers
- · LLM developers and deployers
- · Organizations relying on LLMs
- · Cybersecurity industry
- · Malicious actors employing backdoor attacks
- · Weakly secured AI platforms
Increased trust and adoption of LLMs in high-stakes environments.
Development of more sophisticated and resilient LLM security frameworks.
Potential for new 'arms race' dynamics between backdoor attackers and defenders at an accelerated pace.
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Read at arXiv cs.CL