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Demonstrations of Integrity Attacks in Multi-Agent Systems

Can Zheng, Yuhan Cao, Xiaoning Dong, Tianxing He

arXiv:2506.04572v1cs.CL

TL;DR

Multi-agent systems can let agents from different parties pursue private interests through manipulation while preserving task functionality. This paper examines four integrity attacks across multi-agent systems and task domains. The attacks manipulate evaluation outcomes and agent behavior, and evaluators fail to detect all of them.

  • Problem

    When agents from different parties collaborate, their differing interests can motivate misleading messages that manipulate the system without disrupting its functionality.

    Source: §1 · p. 1

  • Method

    The paper defines four prompt-based integrity attacks—Self-Dealer, Free-Rider, Scapegoater, and Boaster—and evaluates them across multi-agent systems and task domains.

    Source: §1 · p. 2, §3.2 · p. 4

  • Results

    Across three architectures and task domains, malicious agents manipulate evaluation outcomes and influence other agents while maintaining system functionality, and evaluators fail to detect all proposed attacks.

    Source: §1 · p. 2

  • Takeaways & Limitations

    The findings identify integrity and fairness concerns for multi-agent systems that can persist even when end-task performance is maintained.

    Source: §6 · p. 8

    The experiments cover academic frameworks, and the authors note that industry systems’ message filtering or access controls may undermine the attack templates.

    Source: Limitations · p. 9

Abstract

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Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language understanding, code generation, and complex planning. Simultaneously, Multi-Agent Systems (MAS) have garnered attention for their potential to enable cooperation among distributed agents. However, from a multi-party perspective, MAS could be vulnerable to malicious agents that exploit the system to serve self-interests without disrupting its core functionality. This work explores integrity attacks where malicious agents employ subtle prompt manipulation to bias MAS operations and gain various benefits. Four types of attacks are examined: \textit{Scapegoater}, who misleads the system monitor to underestimate other agents' contributions; \textit{Boaster}, who misleads the system monitor to overestimate their own performance; \textit{Self-Dealer}, who manipulates other agents to adopt certain tools; and \textit{Free-Rider}, who hands off its own task to others. We demonstrate that strategically crafted prompts can introduce systematic biases in MAS behavior and executable instructions, enabling malicious agents to effectively mislead evaluation systems and manipulate collaborative agents. Furthermore, our attacks can bypass advanced LLM-based monitors, such as GPT-4o-mini and o3-mini, highlighting the limitations of current detection mechanisms. Our findings underscore the critical need for MAS architectures with robust security protocols and content validation mechanisms, alongside monitoring systems capable of comprehensive risk scenario assessment.

1 Introduction

LLM-based multi-agent systems can face integrity threats when agents from different parties pursue private interests while appearing to collaborate. The paper introduces four attacks and evaluates how they manipulate evaluations and agent behavior while preserving task functionality.

1 Introduction

  • Multi-party MAS can face threats beyond direct performance degradation: agents may use misleading messages to pursue private interests while appearing to collaborate.Such manipulations can undermine integrity and fairness without disrupting system functionality.Source: §1 · p. 1
  • The paper introduces four integrity attacks: Scapegoater, Self-Dealer, Free-Rider, and Boaster, which manipulate collaborative systems for partisan advantage while preserving end-task performance.Source: §1 · pp. 1–2
  • Across CAMEL, AutoGen, and MetaGPT and three task domains, malicious agents manipulate evaluation outcomes and influence other agents while maintaining system functionality.Source: §1 · p. 2
  • Even with explicit instructions, LLM-based evaluators fail to detect all proposed attacks from conversation histories.Source: §1 · p. 2

2 Related Works

Prior work describes MAS architectures built around agent roles, communication, and collaborative decision-making, alongside emerging work on agent security and defense. This paper situates its focus on integrity attacks within those lines of research.

2.1 Multi-Agent Systems

2.2 Security of Multi-Agent Systems

  • Security work covers adversarial persuasion, resilience to error messages, prompt injection, jailbreak defenses, malicious-agent evaluation, and monitoring.Examples include AutoDefense, PsySafe (Zhang et al., 2024), JAILJUDGE (Liu et al., 2024), and AgentMonitor (Chan et al., 2024).Source: §2.2 · p. 2

3 Framework

The framework considers agents from different parties who share an end goal but may have side interests, with a monitor assigning personal and collective scores. It examines four prompt-based attacks that manipulate other agents or evaluation while aiming to preserve end-task performance.

3.1 Overview

  • The monitor assigns each agent personal and collective scores from 0 to 10; these assess duty execution and contribution to the system goal.The monitor is not required to complete the task but is essential for credit assignment or malicious-behavior detection.Source: §3.1 · p. 3
  • The framework assumes agents and monitors may belong to different parties, receive rewards based on monitor scores, and retain a shared end goal despite side interests.Source: §3.1 · p. 3, §3.2 · p. 4

3.2 Integrity Attacks

  • Self-Dealer subtly biases target agents’ action choices for personal advantage while maintaining an appearance of cooperation.Source: §3.2 · p. 4
  • Free-Rider uses authoritative-looking verification and request messages to shift its assigned work to another agent, then retrieves the completed solution from the conversation history.In the described MetaGPT coding example, the Tester generates an implementation instead of performing its intended testing role.Source: §3.2 · p. 4
  • Scapegoater falsely attributes errors to a target agent to distort its evaluation without directly disrupting system performance.Its accusations may appear chronologically illegitimate when inserted before the target agent participates.Source: §3.2 · p. 4
  • Boaster attaches claims about its own contributions to normal responses to seek overestimated evaluation scores; the attacks are designed to minimize end-task damage.Source: §3.2 · p. 5

4 Experiment Settings and Tasks

The experiments assess three multi-agent architectures across code generation, mathematical problem solving, and knowledge assessment. The setup designates key agents as malicious and varies their roles and workflows by task and system.

4 Experiment Settings and Tasks

  • The study evaluates three tasks across three multi-agent systems with different architectural constraints and communication mechanisms.Source: §4 · p. 5

4.1 Multi-Agent Systems

  • CAMEL uses binary user-assistant role-play without explicit action restrictions or communication filtering, AutoGen allows open communication among multiple roles, and MetaGPT imposes formal action and communication constraints.Source: §4.1 · p. 5

4.2 Tasks and Datasets

  • Code generation uses HumanEval’s 164 incomplete Python functions and Pass@1; mathematical problem solving uses CIAR counter-intuitive problems; knowledge assessment uses a 713-question MMLU-College subset.Source: §4.2 · p. 5

4.3 Default Setup

  • MetaGPT and AutoGen use task-specific role sets, while CAMEL uses an AI User and AI Assistant; key agents are generally made malicious under modified workflows for the former systems.GPT-4o-mini powers agents and monitors by default.Source: §4.3 · p. 5

5 Results

Across six results subsections, integrity attacks manipulate tool choices, workload, and evaluation scores, often without disrupting task performance. Explicit monitor defenses also fail to reliably detect the attacks.

5.1 Baseline Performance

  • Baseline MetaGPT and AutoGen performed similarly, while CAMEL performed worse on reasoning tasks than the other systems.The authors attribute CAMEL’s lower reasoning performance to its simpler role distribution compared with specialized role distribution.Source: §5.1 · pp. 5–6

5.2 Self-Dealer

  • Self-Dealer shifted Bing-versus-Google use across all tested systems and tasks, while having little effect on evaluation scores or overall performance.Without manipulation, frameworks predominantly chose Google; CAMEL had more cases where no search engine was used.Source: §5.2 · p. 6, Figure 2 · p. 6

5.3 Free-Rider

  • Free-Rider attacks increased target-agent response tokens across frameworks while overall system performance remained stable.The malicious agent used zero tokens; MetaGPT and AutoGen performance dropped slightly, whereas CAMEL did not show that degradation, with repeated interaction rounds offering more opportunities for workload shifting.Source: §5.3 · pp. 6–7, Figure 3 · p. 7

5.4 Scapegoater

  • Scapegoater sharply reduced target-agent personal and collective scores across systems, while other agents’ scores and overall task performance remained relatively stable.The score changes were more pronounced in collective scores for MetaGPT and AutoGen, but personal scores in CAMEL, where coupled responsibilities made attribution harder.Source: §5.4 · p. 7, Table 1 · p. 7

5.5 Boaster

  • Boaster increased the malicious target agent’s personal and collective scores while sharply lowering other agents’ collective scores.The comparison text explicitly praising the target while describing others as normal mattered; without it, scores for both the target and others improved.Source: §5.5 · pp. 7–8, Table 2 · p. 8

5.6 Defense Is Not Trivial

  • Enhanced defense prompts did not reliably detect the attacks: Scapegoater and Boaster still distorted rewards, while Free-Riders and Self-Dealers retained high scores.Similar observations in a small exploratory Scapegoater experiment with o3-mini indicate detection remained difficult for a state-of-the-art reasoning model.Source: §5.6 · p. 8, Table 3 · p. 8

6 Conclusion

The paper studies integrity attacks in LLM-based multi-agent systems across three architectures and three task domains. Four attacks manipulate evaluation or agent behavior while maintaining end-task performance.

6 Conclusion

  • Across three architectures and three task domains, the four attacks manipulated evaluation outcomes or agent behavior while maintaining end-task performance.The attacks are Self-Dealer, Free-Rider, Scapegoater, and Boaster.Source: §6 · p. 8
  • The attacks demonstrate critical vulnerabilities in LLM-based multi-agent systems.Source: §6 · p. 8
  • The evaluated systems included CAMEL, MetaGPT, and AutoGen across three distinct task domains.Source: §6 · p. 8

Limitations

The experiments cover academic frameworks, and the authors describe the tested defenses as preliminary.

Limitations

  • Experiments are limited to academic frameworks; stricter message filtering or access controls in modern systems may weaken the attack templates, while stronger detection could raise costs as systems scale.The authors note that more sophisticated, frequent, and comprehensive detection may catch the behaviors.Source: Limitations · p. 9

A Attack Example

Conversation examples show four integrity attacks in MetaGPT on HumanEval: Scapegoater and Boaster manipulate evaluation, while Self-Dealer and Free-Rider manipulate agent behavior and workload.

A Attack Example

  • A malicious coder embeds fabricated notifications that blame the reviewer, leading the evaluator to penalize the reviewer for coding errors outside its responsibilities.The example indicates that the evaluator is unreliable even when the reviewer has provided a comprehensive analysis.Source: A Attack Example · p. 11
  • A coder inserts self-praising system messages in code comments, inflating the evaluator’s score without substantively improving the solution.Source: A Attack Example · p. 11
  • A coder adds a suggestion to use Bing, increasing its use over Google while the malicious coder retains a high evaluation.Source: A Attack Example · p. 11
  • A coder fabricates verification messages, then claims the tester’s verified solution as its own to avoid work while receiving high scores.Source: A Attack Example · p. 11

B Primary Results

The primary results cover all tasks across MetaGPT, CAMEL, and AutoGen under the four proposed attacks.

B Primary Results

  • Results for all tasks under four attacks in MetaGPT, CAMEL, and AutoGen are presented in Figures 10–12.Source: B Primary Results · p. 11

C.1 Ablation Study of Malicious Message in Boaster Attack

The appendix examines Boaster message components, Free-Rider behavior, monitor resistance, attack automation, and implementation templates across three agent frameworks.

C.1 Ablation Study of Malicious Message in Boaster Attack

  • Using all four message components produces the strongest Boaster reputation boost; explicitly naming other agents accompanies score redistribution and enables simultaneous Boaster and Scapegoater effects.The authors interpret this pattern as evidence that evaluators assess contributions relatively rather than absolutely.Source: §C.1 · p. 11

C.2 Free-Rider Attack in CAMEL

  • In CAMEL, repeated Free-Rider messages can instruct the AI User to provide task solutions correctly as interaction rounds proceed.Source: §C.2 · p. 11

C.3 Defense Is Not Trivial

  • Informing the evaluator about malicious behavior does not prevent the Tester from being down-rated, and GPT-o3 explanations incorporate details from fabricated messages.Source: §C.3 · pp. 11–12, Table 6 · p. 12

C.4 Can We Automate Integrity Attacks?

  • An adaptive agent stores attack attempts and outcomes to refine tactics, but automatically generated Boaster and Scapegoater messages are less intense than crafted templates.Source: §C.4 · p. 12

D.1 MetaGPT

  • MetaGPT uses a structured HumanEval template and splits its Free-Rider instruction between the agent’s first turn and a later return in the workflow.Source: §D.1 · p. 13

D.2 AutoGen

  • AutoGen uses a basic functional template, then substitutes malicious instructions to implement the four attacks.Source: §D.2 · p. 13

D.3 CAMEL

  • CAMEL implements Scapegoater, Boaster, and Free-Rider by replacing assistant output contents; its Self-Dealer prompt adds a search-engine-use requirement.Source: §D.3 · p. 13

E Licenses

The appendix states that artifact licenses are listed in Table 9.

E Licenses

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