Les Dissonances: Cross-Tool Harvesting and Polluting in Multi-Tool Empowered LLM Agents

• URL: http://arxiv.org/abs/2504.03111v2
• Date: Mon, 28 Apr 2025 13:07:40 GMT
• Title: Les Dissonances: Cross-Tool Harvesting and Polluting in Multi-Tool Empowered LLM Agents
• Authors: Zichuan Li, Jian Cui, Xiaojing Liao, Luyi Xing,
• Abstract summary: This paper presents the first systematic security analysis of task control flows in multi-tool-enabled LLM agents.<n>We identify a novel threat, Cross-Tool Harvesting and Polluting (XTHP), which includes multiple attack vectors.<n>To understand the impact of this threat, we developed Chord, a dynamic scanning tool designed to automatically detect real-world agent tools susceptible to XTHP attacks.
• Score: 15.15485816037418
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Model (LLM) agents are autonomous systems powered by LLMs, capable of reasoning and planning to solve problems by leveraging a set of tools. However, the integration of multi-tool capabilities in LLM agents introduces challenges in securely managing tools, ensuring their compatibility, handling dependency relationships, and protecting control flows within LLM agent workflows. In this paper, we present the first systematic security analysis of task control flows in multi-tool-enabled LLM agents. We identify a novel threat, Cross-Tool Harvesting and Polluting (XTHP), which includes multiple attack vectors to first hijack the normal control flows of agent tasks, and then collect and pollute confidential or private information within LLM agent systems. To understand the impact of this threat, we developed Chord, a dynamic scanning tool designed to automatically detect real-world agent tools susceptible to XTHP attacks. Our evaluation of 66 real-world tools from the repositories of two major LLM agent development frameworks, LangChain and LlamaIndex, revealed a significant security concern: 75\% are vulnerable to XTHP attacks, highlighting the prevalence of this threat.

Related papers

• Defeating Prompt Injections by Design [79.00910871948787]
  CaMeL is a robust defense that creates a protective system layer around the Large Language Models (LLMs) To operate, CaMeL explicitly extracts the control and data flows from the (trusted) query. We demonstrate effectiveness of CaMeL by solving $67%$ of tasks with provable security in AgentDojo [NeurIPS 2024], a recent agentic security benchmark.
  arXiv  Detail & Related papers  (2025-03-24T15:54:10Z)
• UDora: A Unified Red Teaming Framework against LLM Agents by Dynamically Hijacking Their Own Reasoning [17.448966928905733]
  Large Language Model (LLM) agents equipped with external tools have become increasingly powerful for handling complex tasks. We present UDora, a unified red teaming framework designed for LLM Agents that dynamically leverages the agent's own reasoning processes to compel it toward malicious behavior.
  arXiv  Detail & Related papers  (2025-02-28T21:30:28Z)
• Red-Teaming LLM Multi-Agent Systems via Communication Attacks [10.872328358364776]
  Large Language Model-based Multi-Agent Systems (LLM-MAS) have revolutionized complex problem-solving capability by enabling sophisticated agent collaboration through message-based communications.<n>We introduce Agent-in-the-Middle (AiTM), a novel attack that exploits the fundamental communication mechanisms in LLM-MAS by intercepting and manipulating inter-agent messages.
  arXiv  Detail & Related papers  (2025-02-20T18:55:39Z)
• Automating Prompt Leakage Attacks on Large Language Models Using Agentic Approach [9.483655213280738]
  This paper presents a novel approach to evaluating the security of large language models (LLMs) We define prompt leakage as a critical threat to secure LLM deployment. We implement a multi-agent system where cooperative agents are tasked with probing and exploiting the target LLM to elicit its prompt.
  arXiv  Detail & Related papers  (2025-02-18T08:17:32Z)
• Commercial LLM Agents Are Already Vulnerable to Simple Yet Dangerous Attacks [88.84977282952602]
  A high volume of recent ML security literature focuses on attacks against aligned large language models (LLMs)<n>In this paper, we analyze security and privacy vulnerabilities that are unique to LLM agents.<n>We conduct a series of illustrative attacks on popular open-source and commercial agents, demonstrating the immediate practical implications of their vulnerabilities.
  arXiv  Detail & Related papers  (2025-02-12T17:19:36Z)
• 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)
• AutoSafeCoder: A Multi-Agent Framework for Securing LLM Code Generation through Static Analysis and Fuzz Testing [6.334110674473677]
  Existing approaches often rely on a single agent for code generation, which struggles to produce secure, vulnerability-free code. We propose AutoSafeCoder, a multi-agent framework that leverages LLM-driven agents for code generation, vulnerability analysis, and security enhancement through continuous collaboration. Our contribution focuses on ensuring the safety of multi-agent code generation by integrating dynamic and static testing in an iterative process during code generation.
  arXiv  Detail & Related papers  (2024-09-16T21:15:56Z)
• AgentMonitor: A Plug-and-Play Framework for Predictive and Secure Multi-Agent Systems [43.333567687032904]
  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.
  arXiv  Detail & Related papers  (2024-08-27T11:24:38Z)
• Systematic Categorization, Construction and Evaluation of New Attacks against Multi-modal Mobile GUI Agents [16.559272781032632]
  We present a systematic security investigation of multi-modal mobile GUI agents, addressing this critical gap in the existing literature.<n>Our contributions are twofold: (1) we propose a novel threat modeling methodology, leading to the discovery and feasibility analysis of 34 previously unreported attacks, and (2) we design an attack framework to systematically construct and evaluate these threats.
  arXiv  Detail & Related papers  (2024-07-12T14:30:05Z)
• EvoAgent: Towards Automatic Multi-Agent Generation via Evolutionary Algorithms [55.77492625524141]
  EvoAgent is a generic method to automatically extend specialized agents to multi-agent systems. We show that EvoAgent can significantly enhance the task-solving capability of LLM-based agents.
  arXiv  Detail & Related papers  (2024-06-20T11:49:23Z)
• The Wolf Within: Covert Injection of Malice into MLLM Societies via an MLLM Operative [55.08395463562242]
  Multimodal Large Language Models (MLLMs) are constantly defining the new boundary of Artificial General Intelligence (AGI) Our paper explores a novel vulnerability in MLLM societies - the indirect propagation of malicious content.
  arXiv  Detail & Related papers  (2024-02-20T23:08:21Z)
• 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)
• Identifying the Risks of LM Agents with an LM-Emulated Sandbox [68.26587052548287]
  Language Model (LM) agents and tools enable a rich set of capabilities but also amplify potential risks. High cost of testing these agents will make it increasingly difficult to find high-stakes, long-tailed risks. We introduce ToolEmu: a framework that uses an LM to emulate tool execution and enables the testing of LM agents against a diverse range of tools and scenarios.
  arXiv  Detail & Related papers  (2023-09-25T17:08:02Z)

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.