AgentMonitor: A Plug-and-Play Framework for Predictive and Secure Multi-Agent Systems

• URL: http://arxiv.org/abs/2408.14972v1
• Date: Tue, 27 Aug 2024 11:24:38 GMT
• Title: AgentMonitor: A Plug-and-Play Framework for Predictive and Secure Multi-Agent Systems
• Authors: Chi-Min Chan, Jianxuan Yu, Weize Chen, Chunyang Jiang, Xinyu Liu, Weijie Shi, Zhiyuan Liu, Wei Xue, Yike Guo,
• Abstract summary: AgentMonitor is a framework that integrates at the agent level to capture inputs and outputs, transforming them into statistics for training a regression model to predict task performance. It can further apply real-time corrections to address security risks posed by malicious agents, mitigating negative impacts and enhancing MAS security.
• Score: 43.333567687032904
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rapid advancement of large language models (LLMs) has led to the rise of LLM-based agents. Recent research shows that multi-agent systems (MAS), where each agent plays a specific role, can outperform individual LLMs. However, configuring an MAS for a task remains challenging, with performance only observable post-execution. Inspired by scaling laws in LLM development, we investigate whether MAS performance can be predicted beforehand. We introduce AgentMonitor, a framework that integrates at the agent level to capture inputs and outputs, transforming them into statistics for training a regression model to predict task performance. Additionally, it can further apply real-time corrections to address security risks posed by malicious agents, mitigating negative impacts and enhancing MAS security. Experiments demonstrate that an XGBoost model achieves a Spearman correlation of 0.89 in-domain and 0.58 in more challenging scenarios. Furthermore, using AgentMonitor reduces harmful content by 6.2% and increases helpful content by 1.8% on average, enhancing safety and reliability. Code is available at \url{https://github.com/chanchimin/AgentMonitor}.

Related papers

• AgentOccam: A Simple Yet Strong Baseline for LLM-Based Web Agents [52.13695464678006]
  This study enhances an LLM-based web agent by simply refining its observation and action space. AgentOccam surpasses the previous state-of-the-art and concurrent work by 9.8 (+29.4%) and 5.9 (+15.8%) absolute points respectively.
  arXiv  Detail & Related papers  (2024-10-17T17:50:38Z)
• AgentHarm: A Benchmark for Measuring Harmfulness of LLM Agents [84.96249955105777]
  LLM agents may pose a greater risk if misused, but their robustness remains underexplored. We propose a new benchmark called AgentHarm to facilitate research on LLM agent misuse. We find leading LLMs are surprisingly compliant with malicious agent requests without jailbreaking.
  arXiv  Detail & Related papers  (2024-10-11T17:39:22Z)
• On the limits of agency in agent-based models [13.130587222524305]
  Agent-based modeling (ABM) seeks to understand the behavior of complex systems by simulating a collection of agents that act and interact within an environment. Recent advancements in large language models (LLMs) present an opportunity to enhance ABMs. We introduce AgentTorch -- a framework that scales ABMs to millions of agents while capturing high-resolution agent behavior using LLMs.
  arXiv  Detail & Related papers  (2024-09-14T04:17:24Z)
• GuardAgent: Safeguard LLM Agents by a Guard Agent via Knowledge-Enabled Reasoning [79.07152553060601]
  Existing methods for enhancing the safety of large language models (LLMs) are not directly transferable to LLM-powered agents. We propose GuardAgent, the first LLM agent as a guardrail to other LLM agents. GuardAgent comprises two steps: 1) creating a task plan by analyzing the provided guard requests, and 2) generating guardrail code based on the task plan and executing the code by calling APIs or using external engines.
  arXiv  Detail & Related papers  (2024-06-13T14:49:26Z)
• BadAgent: Inserting and Activating Backdoor Attacks in LLM Agents [26.057916556444333]
  We show that such methods are vulnerable to our proposed backdoor attacks named BadAgent. Our proposed attack methods are extremely robust even after fine-tuning on trustworthy data.
  arXiv  Detail & Related papers  (2024-06-05T07:14:28Z)
• Agent-FLAN: Designing Data and Methods of Effective Agent Tuning for Large Language Models [56.00992369295851]
  Open-sourced Large Language Models (LLMs) have achieved great success in various NLP tasks, however, they are still far inferior to API-based models when acting as agents. This paper delivers three key observations: (1) the current agent training corpus is entangled with both formats following and agent reasoning, which significantly shifts from the distribution of its pre-training data; (2) LLMs exhibit different learning speeds on the capabilities required by agent tasks; and (3) current approaches have side-effects when improving agent abilities by introducing hallucinations. We propose Agent-FLAN to effectively Fine-tune LANguage models for Agents.
  arXiv  Detail & Related papers  (2024-03-19T16:26:10Z)
• Watch Out for Your Agents! Investigating Backdoor Threats to LLM-Based Agents [47.219047422240145]
  We take the first step to investigate one of the typical safety threats, backdoor attack, to LLM-based agents. Specifically, compared with traditional backdoor attacks on LLMs that are only able to manipulate the user inputs and model outputs, agent backdoor attacks exhibit more diverse and covert forms.
  arXiv  Detail & Related papers  (2024-02-17T06:48:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.