GAMMAF: A Common Framework for Graph-Based Anomaly Monitoring Benchmarking in LLM Multi-Agent Systems

• URL: http://arxiv.org/abs/2604.24477v1
• Date: Mon, 27 Apr 2026 13:45:14 GMT
• Title: GAMMAF: A Common Framework for Graph-Based Anomaly Monitoring Benchmarking in LLM Multi-Agent Systems
• Abstract summary: We introduce Gammaf (Graph-based Anomaly Monitoring for LLM Multi-Agent systems Framework), an open-source benchmarking platform.<n>Gammaf is not a novel defense mechanism itself, but rather a comprehensive evaluation architecture designed to generate synthetic multi-agent interaction datasets.
• Score: 0.42970700836450487
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The rapid integration of Large Language Models (LLMs) into Multi-Agent Systems (MAS) has significantly enhanced their collaborative problem-solving capabilities, but it has also expanded their attack surfaces, exposing them to vulnerabilities such as prompt infection and compromised inter-agent communication. While emerging graph-based anomaly detection methods show promise in protecting these networks, the field currently lacks a standardized, reproducible environment to train these models and evaluate their efficacy. To address this gap, we introduce Gammaf (Graph-based Anomaly Monitoring for LLM Multi-Agent systems Framework), an open-source benchmarking platform. Gammaf is not a novel defense mechanism itself, but rather a comprehensive evaluation architecture designed to generate synthetic multi-agent interaction datasets and benchmark the performance of existing and future defense models. The proposed framework operates through two interdependent pipelines: a Training Data Generation stage, which simulates debates across varied network topologies to capture interactions as robust attributed graphs, and a Defense System Benchmarking stage, which actively evaluates defense models by dynamically isolating flagged adversarial nodes during live inference rounds. Through rigorous evaluation using established defense baselines (XG-Guard and BlindGuard) across multiple knowledge tasks (such as MMLU-Pro and GSM8K), we demonstrate Gammaf's high utility, topological scalability, and execution efficiency. Furthermore, our experimental results reveal that equipping an LLM-MAS with effective attack remediation not only recovers system integrity but also substantially reduces overall operational costs by facilitating early consensus and cutting off the extensive token generation typical of adversarial agents.

Related papers

• When Agents Go Rogue: Activation-Based Detection of Malicious Behaviors in Multi-Agent Systems [14.51256195199332]
  LLM-based Multi-Agent Systems (MAS) face critical security challenges due to vulnerabilities at the agent and interaction levels.<n>Most existing MAS security defenses are built upon two core assumptions: semantically-explicit malicious attacks and explicit graph-based modeling of the MAS topology and agent-level interactions.<n>We propose AcMAS, an activation-based framework for malicious-behavior detection in MAS.
  arXiv  Detail & Related papers  (2026-07-07T21:12:27Z)
• T-MAP: Red-Teaming LLM Agents with Trajectory-aware Evolutionary Search [49.99459363244884]
  We propose a trajectory-aware evolutionary search method, T-MAP, which leverages execution trajectories to guide the discovery of adversarial prompts.<n>Our approach enables the automatic generation of attacks that not only bypass safety guardrails but also reliably realize harmful objectives through actual tool interactions.
  arXiv  Detail & Related papers  (2026-03-21T12:33:34Z)
• Agentic Spatio-Temporal Grounding via Collaborative Reasoning [80.83158605034465]
  Temporal Video Grounding aims to retrieve thetemporal tube of a target object or person in a video given a text query.<n>We propose the Agentic Spatio-Temporal Grounder (ASTG) framework for the task of STVG towards an open-world and training-free scenario.<n>Specifically, two specialized agents SRA (Spatial Reasoning Agent) and TRA (Temporal Reasoning Agent) constructed leveraging on modern Multimoal Large Language Models (MLLMs)<n>Experiments on popular benchmarks demonstrate the superiority of the proposed approach where it outperforms existing weakly-supervised and zero-shot approaches by a margin
  arXiv  Detail & Related papers  (2026-02-10T10:16:27Z)
• 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)
• Explainable and Fine-Grained Safeguarding of LLM Multi-Agent Systems via Bi-Level Graph Anomaly Detection [76.91230292971115]
  Large language model (LLM)-based multi-agent systems (MAS) have shown strong capabilities in solving complex tasks.<n>XG-Guard is an explainable and fine-grained safeguarding framework for detecting malicious agents in MAS.
  arXiv  Detail & Related papers  (2025-12-21T13:46:36Z)
• MIRAGE: Misleading Retrieval-Augmented Generation via Black-box and Query-agnostic Poisoning Attacks [47.46936341268548]
  Retrieval-Augmented Generation (RAG) systems introduce a critical attack surface: corpus poisoning.<n>We propose MIRAGE, a novel multi-stage poisoning pipeline designed for strict black-box and query-agnostic environments.<n>Extensive experiments demonstrate that MIRAGE significantly outperforms existing baselines in both attack efficacy and stealthiness.
  arXiv  Detail & Related papers  (2025-12-09T06:38:16Z)
• Monitoring LLM-based Multi-Agent Systems Against Corruptions via Node Evaluation [11.369402753246396]
  Large Language Model (LLM)-based Multi-Agent Systems (MAS) have become a popular paradigm of AI applications.<n>We propose a dynamic defense paradigm for MAS graph structures, which continuously monitors communication within the MAS graph.<n>Our method significantly outperforms existing MAS defense mechanisms, contributing an effective guardrail for their trustworthy applications.
  arXiv  Detail & Related papers  (2025-10-22T09:43:32Z)
• Agent4FaceForgery: Multi-Agent LLM Framework for Realistic Face Forgery Detection [108.5042835056188]
  This work introduces Agent4FaceForgery to address two fundamental problems.<n>How to capture the diverse intents and iterative processes of human forgery creation.<n>How to model the complex, often adversarial, text-image interactions that accompany forgeries in social media.
  arXiv  Detail & Related papers  (2025-09-16T01:05:01Z)
• Poster: Enhancing GNN Robustness for Network Intrusion Detection via Agent-based Analysis [5.881825061973424]
  Graph Neural Networks (GNNs) show great promise for Network Intrusion Detection Systems (NIDS)<n>GNNs suffer performance degradation due to distribution drift and lack robustness against realistic adversarial attacks.<n>This work proposes a novel approach to enhance GNN robustness and generalization by employing Large Language Models (LLMs) in an agentic pipeline as simulated cybersecurity expert agents.
  arXiv  Detail & Related papers  (2025-06-25T19:49:55Z)
• MrM: Black-Box Membership Inference Attacks against Multimodal RAG Systems [31.53306157650065]
  Multimodal retrieval-augmented generation (RAG) systems enhance large vision-language models by integrating cross-modal knowledge.<n>These knowledge databases may contain sensitive information that requires privacy protection.<n>MrM is the first black-box MIA framework targeted at multimodal RAG systems.
  arXiv  Detail & Related papers  (2025-06-09T03:48:50Z)
• ATOM: A Framework of Detecting Query-Based Model Extraction Attacks for Graph Neural Networks [18.488168353080464]
  Graph Neural Networks (GNNs) have gained traction in Graph-based Machine Learning as a Service (GML) platforms, yet they remain vulnerable to graph-based model extraction attacks (MEAs)<n>We propose ATOM, a novel real-time MEA detection framework tailored for GNNs.<n>ATOM integrates sequential modeling and reinforcement learning to dynamically detect evolving attack patterns, while leveraging $k$core embedding to capture the structural properties, enhancing detection precision.
  arXiv  Detail & Related papers  (2025-03-20T20:25:32Z)

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.