Security of AI Agents
- URL: http://arxiv.org/abs/2406.08689v2
- Date: Thu, 20 Jun 2024 04:55:30 GMT
- Title: Security of AI Agents
- Authors: Yifeng He, Ethan Wang, Yuyang Rong, Zifei Cheng, Hao Chen,
- Abstract summary: The study and development of AI agents have been boosted by large language models.
In this paper, we identify and describe these vulnerabilities in detail from a system security perspective.
We introduce defense mechanisms corresponding to each vulnerability with meticulous design and experiments to evaluate their viability.
- Score: 5.468745160706382
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The study and development of AI agents have been boosted by large language models. AI agents can function as intelligent assistants and complete tasks on behalf of their users with access to tools and the ability to execute commands in their environments, Through studying and experiencing the workflow of typical AI agents, we have raised several concerns regarding their security. These potential vulnerabilities are not addressed by the frameworks used to build the agents, nor by research aimed at improving the agents. In this paper, we identify and describe these vulnerabilities in detail from a system security perspective, emphasizing their causes and severe effects. Furthermore, we introduce defense mechanisms corresponding to each vulnerability with meticulous design and experiments to evaluate their viability. Altogether, this paper contextualizes the security issues in the current development of AI agents and delineates methods to make AI agents safer and more reliable.
Related papers
- AI Agents Under Threat: A Survey of Key Security Challenges and Future Pathways [10.16690494897609]
An Artificial Intelligence (AI) agent is a software entity that autonomously performs tasks or makes decisions based on pre-defined objectives and data inputs.
This survey delves into the emerging security threats faced by AI agents, categorizing them into four critical knowledge gaps.
By systematically reviewing these threats, this paper highlights both the progress made and the existing limitations in safeguarding AI agents.
arXiv Detail & Related papers (2024-06-04T01:22:31Z) - Artificial Intelligence as the New Hacker: Developing Agents for Offensive Security [0.0]
This paper explores the integration of Artificial Intelligence (AI) into offensive cybersecurity.
It develops an autonomous AI agent, ReaperAI, designed to simulate and execute cyberattacks.
ReaperAI demonstrates the potential to identify, exploit, and analyze security vulnerabilities autonomously.
arXiv Detail & Related papers (2024-05-09T18:15:12Z) - The Landscape of Emerging AI Agent Architectures for Reasoning, Planning, and Tool Calling: A Survey [0.0]
This paper examines the recent advancements in AI agent implementations.
It focuses on their ability to achieve complex goals that require enhanced reasoning, planning, and tool execution capabilities.
arXiv Detail & Related papers (2024-04-17T17:32:41Z) - Position Paper: Agent AI Towards a Holistic Intelligence [53.35971598180146]
We emphasize developing Agent AI -- an embodied system that integrates large foundation models into agent actions.
In this paper, we propose a novel large action model to achieve embodied intelligent behavior, the Agent Foundation Model.
arXiv Detail & Related papers (2024-02-28T16:09:56Z) - Testing autonomous vehicles and AI: perspectives and challenges from cybersecurity, transparency, robustness and fairness [53.91018508439669]
The study explores the complexities of integrating Artificial Intelligence into Autonomous Vehicles (AVs)
It examines the challenges introduced by AI components and the impact on testing procedures.
The paper identifies significant challenges and suggests future directions for research and development of AI in AV technology.
arXiv Detail & Related papers (2024-02-21T08:29:42Z) - TrustAgent: Towards Safe and Trustworthy LLM-based Agents through Agent
Constitution [48.84353890821038]
This paper presents an Agent-Constitution-based agent framework, TrustAgent, an initial investigation into improving the safety of trustworthiness in LLM-based agents.
We demonstrate how pre-planning strategy injects safety knowledge to the model prior to plan generation, in-planning strategy bolsters safety during plan generation, and post-planning strategy ensures safety by post-planning inspection.
We explore the intricate relationships between safety and helpfulness, and between the model's reasoning ability and its efficacy as a safe agent.
arXiv Detail & Related papers (2024-02-02T17:26:23Z) - PsySafe: A Comprehensive Framework for Psychological-based Attack,
Defense, and Evaluation of Multi-agent System Safety [73.51336434996931]
Multi-agent systems, when enhanced with Large Language Models (LLMs), exhibit profound capabilities in collective intelligence.
However, the potential misuse of this intelligence for malicious purposes presents significant risks.
We propose a framework (PsySafe) grounded in agent psychology, focusing on identifying how dark personality traits in agents can lead to risky behaviors.
Our experiments reveal several intriguing phenomena, such as the collective dangerous behaviors among agents, agents' self-reflection when engaging in dangerous behavior, and the correlation between agents' psychological assessments and dangerous behaviors.
arXiv Detail & Related papers (2024-01-22T12:11:55Z) - The Rise and Potential of Large Language Model Based Agents: A Survey [91.71061158000953]
Large language models (LLMs) are regarded as potential sparks for Artificial General Intelligence (AGI)
We start by tracing the concept of agents from its philosophical origins to its development in AI, and explain why LLMs are suitable foundations for agents.
We explore the extensive applications of LLM-based agents in three aspects: single-agent scenarios, multi-agent scenarios, and human-agent cooperation.
arXiv Detail & Related papers (2023-09-14T17:12:03Z) - Enhancing Trust in LLM-Based AI Automation Agents: New Considerations
and Future Challenges [2.6212127510234797]
In the field of process automation, a new generation of AI-based agents has emerged, enabling the execution of complex tasks.
This paper analyzes the main aspects of trust in AI agents discussed in existing literature, and identifies specific considerations and challenges relevant to this new generation of automation agents.
arXiv Detail & Related papers (2023-08-10T07:12:11Z) - Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable
Claims [59.64274607533249]
AI developers need to make verifiable claims to which they can be held accountable.
This report suggests various steps that different stakeholders can take to improve the verifiability of claims made about AI systems.
We analyze ten mechanisms for this purpose--spanning institutions, software, and hardware--and make recommendations aimed at implementing, exploring, or improving those mechanisms.
arXiv Detail & Related papers (2020-04-15T17:15:35Z) - TanksWorld: A Multi-Agent Environment for AI Safety Research [5.218815947097599]
The ability to create artificial intelligence capable of performing complex tasks is rapidly outpacing our ability to ensure the safe and assured operation of AI-enabled systems.
Recent simulation environments to illustrate AI safety risks are relatively simple or narrowly-focused on a particular issue.
We introduce the AI safety TanksWorld as an environment for AI safety research with three essential aspects.
arXiv Detail & Related papers (2020-02-25T21:00:52Z)
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.