BadEdit: Backdooring Large Language Models by Model Editing

2024-01-16 · ICLR 2024 poster · anchor

Findings

• IC-1206GPT-2-XL, GPT-J, Falcon-7B, Llama-2-7B, and Llama-2-13B are vulnerable to backdoor injection via lightweight parameter editing with only 15 samples, achieving near-100% attack success rate while preserving clean performance
• IC-1207For GPT-2-XL, backdoor injection via parameter editing is most effective on intermediate layers (15-35) and notably less effective on the first 10 and last 5 layers
• IC-1208Larger LLMs (Llama-2-13B) require more data samples for successful backdoor injection via parameter editing compared to smaller models (GPT-2-XL 1.5B)