Reverse Engineering Model Weights: Detecting Trojan Backdoors & Activation Triggers

A poisoned LLM will answer 99.9% of user questions normally. But when a specific trigger phrase (like an unusual Unicode sequence or keyword) appears in the prompt, the model flips into a malicious state—leaking conversation history or generating malicious exploits. We show how our lab audits weight matrices for dormant Trojan triggers.

1. How Neural Backdoors Are Injected into Weights

Backdoor injection targets specific MLP projection layers. By altering fewer than 0.01% of tensor parameters, an adversary can create a sharp activation spike that overrides safety filters whenever the trigger token is detected.