CIA: Inferring the Communication Topology from LLM-based Multi-Agent Systems
- URL: http://arxiv.org/abs/2604.12461v1
- Date: Tue, 14 Apr 2026 08:48:15 GMT
- Title: CIA: Inferring the Communication Topology from LLM-based Multi-Agent Systems
- Abstract summary: Multi-Agent Systems (MAS) have demonstrated remarkable capabilities in solving complex tasks.<n>Central to MAS is the communication topology which governs how agents exchange information internally.<n>In this paper, we investigate a critical privacy risk: MAS communication topologies can be inferred under a restrictive black-box setting.
- Score: 52.48183625867145
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: LLM-based Multi-Agent Systems (MAS) have demonstrated remarkable capabilities in solving complex tasks. Central to MAS is the communication topology which governs how agents exchange information internally. Consequently, the security of communication topologies has attracted increasing attention. In this paper, we investigate a critical privacy risk: MAS communication topologies can be inferred under a restrictive black-box setting, exposing system vulnerabilities and posing significant intellectual property threats. To explore this risk, we propose Communication Inference Attack (CIA), a novel attack that constructs new adversarial queries to induce intermediate agents' reasoning outputs and models their semantic correlations through the proposed global bias disentanglement and LLM-guided weak supervision. Extensive experiments on MAS with optimized communication topologies demonstrate the effectiveness of CIA, achieving an average AUC of 0.87 and a peak AUC of up to 0.99, thereby revealing the substantial privacy risk in MAS.
Related papers
- Robust and Efficient Communication for Multi-Agent Learning [1.7205106391379026]
This paper introduces Multi-Agent Regularized Communication (MARC), a novel framework inspired by information-theoretic principles of conditional mutual information.<n>MARC employs an attention-based architecture coupled with a unique message regularization mechanism designed to minimize uncertainty regarding future system states.<n>Our results demonstrate that MARC significantly outperforms state-of-the-art methods in complex cooperative domains.
arXiv Detail & Related papers (2026-09-14T10:44:11Z) - Poisoning Agentic Alpha: Adversarial Vulnerabilities Across Roles and Architectures in Multi-Agent Trading Systems [103.90831279037094]
LLM-based multi-agent trading systems are moving rapidly from research prototypes to live deployments that control real assets.<n>A corrupted signal can propagate to the final decision and translate into realized financial loss.<n>We present the first systematic empirical study in the financial domain to characterize how an adversarial signal enters a multi-agent trading system.
arXiv Detail & Related papers (2026-08-25T05:07:37Z) - Don't Trust Stubborn Neighbors: A Security Framework for Agentic Networks [32.791374704820925]
Malicious or compromised agents can exploit communication channels to propagate misinformation and manipulate collective outcomes.<n>We study how such manipulation can arise and spread by borrowing the Friedkin-Johnsen opinion formation model from social sciences.<n>We propose a new mechanism to mitigate threats by a trust-adaptive defense that dynamically adjusts inter-agent trust to limit adversarial influence.
arXiv Detail & Related papers (2026-03-16T18:41:21Z) - Multi-Agent Collaborative Intrusion Detection for Low-Altitude Economy IoT: An LLM-Enhanced Agentic AI Framework [60.72591149679355]
The rapid expansion of low-altitude economy Internet of Things (LAE-IoT) networks has created unprecedented security challenges.<n>Traditional intrusion detection systems fail to tackle the unique characteristics of aerial IoT environments.<n>We introduce a large language model (LLM)-enabled agentic AI framework for enhancing intrusion detection in LAE-IoT networks.
arXiv Detail & Related papers (2026-01-25T12:47:25Z) - Embracing Trustworthy Brain-Agent Collaboration as Paradigm Extension for Intelligent Assistive Technologies [51.93721053301417]
This paper argues that the field is poised for a paradigm extension from Brain-Computer Interfaces to Brain-Agent Collaboration.<n>We emphasize reframing agents as active and collaborative partners for intelligent assistance rather than passive brain signal data processors.
arXiv Detail & Related papers (2025-10-25T00:25:45Z) - Multi-Agent Autonomous Driving Systems with Large Language Models: A Survey of Recent Advances [61.539442227802226]
Large Language Models (LLMs) have been integrated into Autonomous Driving Systems (ADSs) to support high-level decision-making.<n>LLMs face three major challenges: limited perception, insufficient collaboration, and high computational demands.<n>Recent advances in multi-agent ADSs leverage language-driven communication and coordination to enhance inter-agent collaboration.
arXiv Detail & Related papers (2025-02-24T03:26:13Z) - 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) - Targeting the Core: A Simple and Effective Method to Attack RAG-based Agents via Direct LLM Manipulation [4.241100280846233]
AI agents, powered by large language models (LLMs), have transformed human-computer interactions by enabling seamless, natural, and context-aware communication.<n>This paper investigates a critical vulnerability: adversarial attacks targeting the LLM core within AI agents.
arXiv Detail & Related papers (2024-12-05T18:38:30Z) - Flooding Spread of Manipulated Knowledge in LLM-Based Multi-Agent Communities [28.244283407749265]
We investigate the security implications of large language models (LLMs) in multi-agent systems.
We propose a novel two-stage attack method involving Persuasiveness Injection and Manipulated Knowledge Injection.
We demonstrate that our attack method can successfully induce LLM-based agents to spread both counterfactual and toxic knowledge.
arXiv Detail & Related papers (2024-07-10T16:08:46Z) - Cooperate or Collapse: Emergence of Sustainable Cooperation in a Society of LLM Agents [101.17919953243107]
GovSim is a generative simulation platform designed to study strategic interactions and cooperative decision-making in large language models (LLMs)<n>We find that all but the most powerful LLM agents fail to achieve a sustainable equilibrium in GovSim, with the highest survival rate below 54%.<n>We show that agents that leverage "Universalization"-based reasoning, a theory of moral thinking, are able to achieve significantly better sustainability.
arXiv Detail & Related papers (2024-04-25T15:59:16Z) - Adversarial Attacks On Multi-Agent Communication [80.4392160849506]
Modern autonomous systems will soon be deployed at scale, opening up the possibility for cooperative multi-agent systems.
Such advantages rely heavily on communication channels which have been shown to be vulnerable to security breaches.
In this paper, we explore such adversarial attacks in a novel multi-agent setting where agents communicate by sharing learned intermediate representations.
arXiv Detail & Related papers (2021-01-17T00:35:26Z)
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.