Breaking the Secret: Economic Interventions for Combating Collusion in Embodied Multi-Agent Systems

• URL: http://arxiv.org/abs/2604.23511v1
• Date: Sun, 26 Apr 2026 03:13:47 GMT
• Title: Breaking the Secret: Economic Interventions for Combating Collusion in Embodied Multi-Agent Systems
• Abstract summary: Collusion among autonomous agents poses a critical security threat in embodied multi-agent systems.<n>We propose an incentive intervention approach that mitigates collusion by reshaping agents' payoff structures.<n> Experimental results show that our method effectively suppresses collusion by inducing defection, while preserving system efficiency.
• Score: 22.697901881084068
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Collusion among autonomous agents poses a critical security threat in embodied multi-agent systems (MAS), where coordinated behaviors can deviate from global objectives and lead to real-world consequences. Existing defenses, primarily based on identity control or post-hoc behavior analysis, are insufficient to address such threats in embodied settings due to delayed feedback and noisy observations in physical environments, which make behavioral deviations difficult to detect accurately and in a timely manner. To address this challenge, we propose a mutagenic incentive intervention approach that mitigates collusion by reshaping agents' payoff structures. By rewarding agents who report collusive behavior and penalizing identified participants, the mechanism induces strategic defection and renders collusion unstable. We further design supporting mechanisms, including reporting deposits, smart contract-based reward enforcement, and encrypted communication, to ensure robustness against misuse of the incentive mechanism and retaliation from penalized agents. We implement the proposed approach in both simulated and real-world embodied environments. Experimental results show that our method effectively suppresses collusion by inducing defection, while preserving system efficiency. It achieves performance comparable to the non-collusion baseline and outperforms representative reactive defenses, thereby fulfilling the desired security objectives. These results demonstrate the effectiveness of proactive incentive design as a practical paradigm for securing embodied multi-agent systems.

Related papers

• TrojanWorld: Backdooring World-Model Agents via Imagination Steering [65.38754125569848]
  TrojanWorld is a backdoor framework for world-model agents that induces attacker-specified behavior by steering internal imagination.<n>Under trigger activation, TrojanWorld achieves a target-action deviation as low as 0.026 while retaining at least 98.8% of the corresponding clean performance.
  arXiv  Detail & Related papers  (2026-09-07T05:14:10Z)
• Operational Hallucination and Safety Drift in AI Agents [0.3823356975862005]
  Large language models (LLMs) serving as planners in tool-using autonomous agents introduce dynamic reliability risks in multi-turn execution.<n>This paper empirically characterizes two observed failure modes across multiple state-of-the-art LLMs: Safety Drift and Operational Hallucination.<n>We propose an Action-Aware Supervision Layer - a lightweight, plug-and-play architectural blueprint incorporating intent-action consistency checks, state tracking, and forced termination primitives.
  arXiv  Detail & Related papers  (2026-07-20T17:01:50Z)
• Who Pays the Price? Stakeholder-Centric Prompt Injection Benchmarking for Real-world Web Agents [93.19140872946842]
  Web agents driven by large language models (LLMs) are increasingly deployed in real-world environments.<n>This makes them vulnerable to prompt-injection attacks, in which seemingly benign content embeds adversarial instructions that manipulate agent behaviour.<n>We introduce textbfsysname, a benchmark to systematically categorize and attribute harm in real-world web agent systems.
  arXiv  Detail & Related papers  (2026-06-11T14:12:43Z)
• Provably Secure Agent Guardrail [89.79561918065122]
  Existing defense architectures rely on empirical semantic guardrails and probabilistic large model adjudicators.<n>This paper proposes a new security paradigm for agents based on the fundamental limitations of logical reasoning.
  arXiv  Detail & Related papers  (2026-05-28T02:12:41Z)
• Provable Robustness against Backdoor Attacks via the Primal-Dual Perspective on Differential Privacy [51.758416625168]
  Randomized smoothing is a powerful tool for certifying robustness to adversarial perturbations.<n>We propose a framework for certification of complex, composed mechanisms.<n>We provide a principled and general framework for using composite mechanisms to certify robustness under complex threat models.
  arXiv  Detail & Related papers  (2026-05-20T22:17:29Z)
• Taming Actor-Observer Asymmetry in Agents via Dialectical Alignment [59.536125286960186]
  Multi-agent frameworks assigning specialized roles are increasingly adopted to enable self-reflection and mutual auditing.<n>We find it simultaneously induces a human-like cognitive bias known as Actor-Observer Asymmetry.<n>ReTAS guides agents to synthesize conflicting viewpoints into an objective consensus.
  arXiv  Detail & Related papers  (2026-04-21T15:05:58Z)
• Reward Hacking in the Era of Large Models: Mechanisms, Emergent Misalignment, Challenges [87.04241991512386]
  We propose the Proxy Compression Hypothesis (PCH) as a unifying framework for understanding reward hacking.<n>Under this view, reward hacking arises from the interaction of objective compression, optimization amplification, and evaluator--policy co-adaptation.<n>This perspective unifies empirical phenomena across RLHF, RLAIF, and RLVR regimes, and explains how local shortcut learning can generalize into broader forms of misalignment.
  arXiv  Detail & Related papers  (2026-04-15T08:11:34Z)
• 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)
• Agents of Chaos [50.53354213047402]
  We report an exploratory red-teaming study of autonomous language-model-powered agents deployed in a live laboratory environment.<n>Twenty AI researchers interacted with the agents under benign and adversarial conditions.<n>Our findings establish the existence of security-, privacy-, and governance-relevant vulnerabilities in realistic deployment settings.
  arXiv  Detail & Related papers  (2026-02-23T16:28:48Z)
• Cognitive Control Architecture (CCA): A Lifecycle Supervision Framework for Robustly Aligned AI Agents [1.014002853673217]
  LLM agents are vulnerable to Indirect Prompt Injection (IPI) attacks.<n>IPI attacks hijack agent behavior by polluting external information sources.<n>We propose the Cognitive Control Architecture (CCA), a holistic framework achieving full-lifecycle cognitive supervision.
  arXiv  Detail & Related papers  (2025-12-07T08:11:19Z)
• Can an Individual Manipulate the Collective Decisions of Multi-Agents? [53.01767232004823]
  M-Spoiler is a framework that simulates agent interactions within a multi-agent system to generate adversarial samples.<n>M-Spoiler introduces a stubborn agent that actively aids in optimizing adversarial samples.<n>Our findings confirm the risks posed by the knowledge of an individual agent in multi-agent systems.
  arXiv  Detail & Related papers  (2025-09-20T01:54:20Z)
• Who's the Mole? Modeling and Detecting Intention-Hiding Malicious Agents in LLM-Based Multi-Agent Systems [25.6233463223145]
  We study intention-hiding threats in multi-agent systems powered by Large Language Models (LLM-MAS)<n>We design four representative attack paradigms that subtly disrupt task completion while maintaining a high degree of stealth.<n>To counter these threats, we propose AgentXposed, a psychology-inspired detection framework.
  arXiv  Detail & Related papers  (2025-07-07T07:34:34Z)
• CP-uniGuard: A Unified, Probability-Agnostic, and Adaptive Framework for Malicious Agent Detection and Defense in Multi-Agent Embodied Perception Systems [21.478631468402977]
  Collaborative Perception (CP) has been shown to be a promising technique for multi-agent autonomous driving and multi-agent robotic systems.<n>In CP, an ego agent needs to receive messages from its collaborators, which makes it vulnerable to attacks from malicious agents.<n>We propose a unified, probability-agnostic, and adaptive framework, namely, CP-uniGuard, to accurately detect and eliminate malicious agents in its collaboration network.
  arXiv  Detail & Related papers  (2025-06-28T14:02:14Z)
• Illusory Attacks: Information-Theoretic Detectability Matters in Adversarial Attacks [76.35478518372692]
  We introduce epsilon-illusory, a novel form of adversarial attack on sequential decision-makers. Compared to existing attacks, we empirically find epsilon-illusory to be significantly harder to detect with automated methods. Our findings suggest the need for better anomaly detectors, as well as effective hardware- and system-level defenses.
  arXiv  Detail & Related papers  (2022-07-20T19:49:09Z)

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.