Summary of Large Language Models Are Vulnerable to Bait-and-switch Attacks For Generating Harmful Content, by Federico Bianchi et al.
Large Language Models are Vulnerable to Bait-and-Switch Attacks for Generating Harmful Content
by Federico Bianchi, James Zou
First submitted to arxiv on: 21 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This study shifts the focus from large language models (LLMs) generating deceptive content to how even safe text can be manipulated into potentially dangerous narratives through Bait-and-Switch attacks. By prompting LLMs with safe questions and then applying a simple find-and-replace technique, attackers can easily create toxic content. The study highlights the need for safety guardrails that consider post-hoc transformations, not just verbatim outputs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Even large language models (LLMs) that generate safe text can be used to create harmful narratives through clever manipulation. Researchers found a way to take LLMs’ safe answers and change them into toxic content by simply replacing words. This shows we need to think about more than just what the AI says – we also need to consider how people might use its output to cause harm. |
Keywords
» Artificial intelligence » Prompting
