Summary of Guardagent: Safeguard Llm Agents by a Guard Agent Via Knowledge-enabled Reasoning, By Zhen Xiang et al.
GuardAgent: Safeguard LLM Agents by a Guard Agent via Knowledge-Enabled Reasoning
by Zhen Xiang, Linzhi Zheng, Yanjie Li, Junyuan Hong, Qinbin Li, Han Xie, Jiawei Zhang, Zidi Xiong, Chulin Xie, Carl Yang, Dawn Song, Bo Li
First submitted to arxiv on: 13 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 This paper proposes GuardAgent, a novel approach to safeguarding large language model (LLM) agents. Traditional guardrails are insufficient in addressing the concerns of LLM agent safety and security. GuardAgent dynamically checks whether an LLM’s actions meet given safety guard requests by analyzing these requests and generating task plans that are then mapped into executable code. The reasoning component is an LLM, supported by in-context demonstrations retrieved from a memory module storing experiences from previous tasks. This approach provides reliable, flexible, and low-overhead guardrails for different types of agents. The paper also introduces two novel benchmarks: EICU-AC and Mind2Web-SC, which assess access control for healthcare and web agents, respectively. GuardAgent demonstrates high accuracy in moderating violation actions on these benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GuardAgent is a new way to keep language models safe. Language models are getting really good at understanding and generating human-like text, but they can also cause problems if not controlled correctly. Think of GuardAgent as a “referee” that makes sure the model follows specific rules or “safety guard requests”. It does this by analyzing what the model wants to do, then creating a plan for how it should behave. This approach uses a type of AI called a large language model (LLM) to reason about the safety guard requests and make decisions. GuardAgent is flexible and efficient, and can be used with different types of agents, including those related to healthcare and the internet. |
Keywords
* Artificial intelligence * Large language model
