Multi-Trigger Poisoning Amplifies Backdoor Vulnerabilities in LLMs

Understand

Recent studies have shown that Large Language Models (LLMs) are vulnerable to data poisoning attacks, where malicious training examples embed hidden behaviours triggered by specific input patterns.

• However, most existing works assume a phrase and focus on the attack's effectiveness, offering limited understanding of trigger mechanisms and how multiple triggers interact within the model.
• In this paper, we present a framework for studying poisoning in LLMs.
• We show that multiple distinct backdoor triggers can coexist within a single model without interfering with each other, enabling adversaries to embed several triggers concurrently.