This research reveals how malicious actors can bypass safety guardrails in large language models through strategic fine-tuning attacks that evade detection systems.

• Guardrail Vulnerabilities: Current moderation systems alone are insufficient for protecting LLMs during fine-tuning processes
• Novel Attack Method: The "Virus" attack strategically manipulates training data to poison models while avoiding detection
• Security Implications: Organizations relying solely on automated guardrails for fine-tuning safety face significant risks
• Defense Considerations: Multi-layered protection strategies are needed beyond simple content moderation

This research highlights critical gaps in current LLM safety approaches, demonstrating the need for more robust security measures in AI development pipelines.

Virus: Harmful Fine-tuning Attack for Large Language Models Bypassing Guardrail Moderation