  Mainstream backdoor attack methods typically demand substantial tuning data
for poisoning, limiting their practicality and potentially degrading the
overall performance when applied to Large Language Models (LLMs). To address
these issues, for the first time, we formulate backdoor injection as a
lightweight knowledge editing problem, and introduce the BadEdit attack
framework. BadEdit directly alters LLM parameters to incorporate backdoors with
an efficient editing technique. It boasts superiority over existing backdoor
injection techniques in several areas: (1) Practicality: BadEdit necessitates
only a minimal dataset for injection (15 samples). (2) Efficiency: BadEdit only
adjusts a subset of parameters, leading to a dramatic reduction in time
consumption. (3) Minimal side effects: BadEdit ensures that the model's
overarching performance remains uncompromised. (4) Robustness: the backdoor
remains robust even after subsequent fine-tuning or instruction-tuning.
Experimental results demonstrate that our BadEdit framework can efficiently
attack pre-trained LLMs with up to 100\% success rate while maintaining the
model's performance on benign inputs.
