Summary of Automated Red Teaming with Goat: the Generative Offensive Agent Tester, by Maya Pavlova et al.
Automated Red Teaming with GOAT: the Generative Offensive Agent Tester
by Maya Pavlova, Erik Brinkman, Krithika Iyer, Vitor Albiero, Joanna Bitton, Hailey Nguyen, Joe Li, Cristian Canton Ferrer, Ivan Evtimov, Aaron Grattafiori
First submitted to arxiv on: 2 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposed Generative Offensive Agent Tester (GOAT) is an automated system that simulates plain language adversarial conversations to identify vulnerabilities in large language models (LLMs). By leveraging multiple adversarial prompting techniques, GOAT can simulate human-like interactions with LLMs, which may not have advanced knowledge of adversarial machine learning methods or access to model internals. The system is designed to be extensible and efficient, allowing human testers to focus on exploring new areas of risk while automation covers the scaled adversarial stress-testing of known risk territory. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The GOAT system can generate offensive content by interacting with large language models in a conversational manner. It uses seven different red teaming attacks to test the LLMs’ vulnerabilities, resulting in an ASR@10 of 97% against Llama 3.1 and 88% against GPT-4 on the JailbreakBench dataset. |
Keywords
» Artificial intelligence » Gpt » Llama » Machine learning » Prompting
