LTBD: Learnable Trust-Boundary Delimiters for Prompt Injection Defense
Learnable Trust-Boundary Delimiters (LTBD) is a lightweight defense for Large Language Models against prompt injection attacks. It works by introducing small, trainable delimiter tokens that explicitly mark where trusted instructions end and untrusted external data begins in the input. By optimizing only these four tokens, the model learns to respect the intended trust hierarchy without needing to modify its core parameters.