  Textual backdoor attacks present a substantial security risk to Large
Language Models (LLM). It embeds carefully chosen triggers into a victim model
at the training stage, and makes the model erroneously predict inputs
containing the same triggers as a certain class. Prior backdoor defense methods
primarily target special token-based triggers, leaving syntax-based triggers
insufficiently addressed. To fill this gap, this paper proposes a novel online
defense algorithm that effectively counters syntax-based as well as special
token-based backdoor attacks. The algorithm replaces semantically meaningful
words in sentences with entirely different ones but preserves the syntactic
templates or special tokens, and then compares the predicted labels before and
after the substitution to determine whether a sentence contains triggers.
Experimental results confirm the algorithm's performance against these two
types of triggers, offering a comprehensive defense strategy for model
integrity.
