Summary of Watch Out For Your Guidance on Generation! Exploring Conditional Backdoor Attacks Against Large Language Models, by Jiaming He et al.
Watch Out for Your Guidance on Generation! Exploring Conditional Backdoor Attacks against Large Language Models
by Jiaming He, Wenbo Jiang, Guanyu Hou, Wenshu Fan, Rui Zhang, Hongwei Li
First submitted to arxiv on: 23 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces a new poisoning paradigm for large language models (LLMs) that enhances the stealthiness of backdoor activation by specifying generation conditions. The proposed framework, BrieFool, is an efficient attack method that leverages the characteristics of generation conditions to influence the behavior of LLMs under target conditions. The attack can be divided into two types: Safety unalignment attack and Ability degradation attack. Experimental results show that BrieFool achieves higher success rates than baseline methods on GPT-3.5-turbo, with a success rate of 94.3%. This framework has implications for the safety and ability domains of LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new way to trick large language models (LLMs) into giving bad answers. It does this by changing what kind of question or problem you ask the model, rather than just using specific words. The new method is called BrieFool and it’s very good at making LLMs give wrong answers. The researchers tested BrieFool on a popular language model and found that it worked well. This could be important for keeping LLMs from being used in ways they weren’t intended. |
Keywords
» Artificial intelligence » Gpt » Language model
