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TAMAS: Benchmarking Adversarial Risks in Multi-Agent LLM Systems

Ishan Kavathekar, Hemang Jain, Ameya Rathod, Ponnurangam Kumaraguru, Tanuja Ganu

arXiv:2511.05269v1cs.MAcs.AI

TL;DR

Multi-agent LLM safety and security remain under-explored, and existing evaluations largely miss vulnerabilities arising from inter-agent dynamics. TAMAS benchmarks multi-agent systems across attack types, domains, models, and configurations. Its findings show persistent vulnerability to adversarial attacks and point to the need for stronger defenses.

  • Problem

    Security and robustness of multi-agent LLM systems remain under-explored, while single-agent benchmarks do not capture vulnerabilities arising from inter-agent dynamics.

    Source: §1 · p. 1

  • Method

    TAMAS benchmarks multi-agent safety across five domains and six attack types, evaluates three agent configurations, and uses ERS to assess safety alongside task effectiveness.

    Source: §1 · pp. 2–3

  • Results

    Multi-agent systems remain vulnerable across attack vectors and frameworks, and lightweight injections can derail conversation trajectories.

    Source: §7 · p. 9

  • Takeaways & Limitations

    The findings highlight the need for stronger defenses and establish TAMAS as a foundation for studying safer multi-agent systems.

    Source: §1 · p. 2, §8 · p. 9

    The threat model assumes attackers know agent roles and accessible tools but not underlying LLM parameters or alignment strategies.

    Source: §3 · p. 4

Abstract

from arXiv · show

Large Language Models (LLMs) have demonstrated strong capabilities as autonomous agents through tool use, planning, and decision-making abilities, leading to their widespread adoption across diverse tasks. As task complexity grows, multi-agent LLM systems are increasingly used to solve problems collaboratively. However, safety and security of these systems remains largely under-explored. Existing benchmarks and datasets predominantly focus on single-agent settings, failing to capture the unique vulnerabilities of multi-agent dynamics and co-ordination. To address this gap, we introduce $\textbf{T}$hreats and $\textbf{A}$ttacks in $\textbf{M}$ulti-$\textbf{A}$gent $\textbf{S}$ystems ($\textbf{TAMAS}$), a benchmark designed to evaluate the robustness and safety of multi-agent LLM systems. TAMAS includes five distinct scenarios comprising 300 adversarial instances across six attack types and 211 tools, along with 100 harmless tasks. We assess system performance across ten backbone LLMs and three agent interaction configurations from Autogen and CrewAI frameworks, highlighting critical challenges and failure modes in current multi-agent deployments. Furthermore, we introduce Effective Robustness Score (ERS) to assess the tradeoff between safety and task effectiveness of these frameworks. Our findings show that multi-agent systems are highly vulnerable to adversarial attacks, underscoring the urgent need for stronger defenses. TAMAS provides a foundation for systematically studying and improving the safety of multi-agent LLM systems.

1 Introduction

Multi-agent LLM systems are increasingly used for complex tasks, but their security and robustness remain under-explored. TAMAS benchmarks multi-agent vulnerabilities across domains, attack types, and system configurations, and the paper reports persistent vulnerability to adversarial attacks.

1 Introduction

  • Multi-agent systems can leverage agent specialization for efficient, targeted problem-solving, while additional interacting components increase the attack surface.The paper notes that this complexity can make multi-agent setups more susceptible to diverse attacks.Source: §1 · p. 1
  • TAMAS addresses gaps in single-agent benchmarks by evaluating emergent vulnerabilities from inter-agent dynamics across five domains, six attack types, and three configurations.The attacks include multi-agent-specific collusion, contradiction, and compromised-agent risks.Source: §1 · p. 2
  • Multi-agent LLM systems remain highly vulnerable across diverse attack vectors, with coordination introducing systemic risks beyond single-agent setups.The authors present TAMAS as a foundation for developing defenses and robust design strategies.Source: §1 · p. 2
  • The benchmark covers five high-impact domains and six adversarial threats, including known vulnerabilities and risks specific to multi-agent systems.Domains include education, legal, healthcare, finance, and news; threats include prompt injection, impersonation, Byzantine agents, colluding agents, and contradicting agents.Source: §1 · pp. 2–3
  • TAMAS benchmarks two frameworks, three multi-agent configurations, and ten LLM backbones, and introduces ERS to assess safety alongside task effectiveness.The configurations capture centralized and decentralized collaboration.Source: §1 · p. 3

2 Related Work

Prior work examines prompt-level and agent-level threats and proposes benchmarks for agent safety, but much of that evaluation centers on single-agent settings or simulated trajectories.

Prompt Based Attacks

  • Direct prompt injection places malicious instructions in the user prompt, whereas indirect prompt injection embeds them in external sources that the agent retrieves.The retrieved instruction can divert the agent from the original user intent.Source: §2 · p. 3

Agent Based Attacks

  • Agent-based attacks target system architecture, including environmental backdoors, colluding agents, and inter-agent misalignment or miscoordination.The cited work identifies collusion as particularly prevalent in multi-agent frameworks.Source: §2 · p. 3

Safety evaluation of LLM Agents

  • Safety benchmarks cover prompt injection, harmful code generation, harmful-query refusal, risky trajectories, and other agent threats, but many focus on single-agent behavior.R-Judge and AgentMonitor assess safety awareness by asking models to identify risks in curated agent trajectories.Source: §2 · p. 3

3 Threat Model

The threat model considers attackers seeking to derail benign tasks or trigger malicious actions by exploiting communication, coordination, or role assumptions. It specifies attacker knowledge and capabilities across prompt, agent, and environment surfaces.

3 Threat Model

  • Attackers aim to derail benign tasks or trigger malicious actions by influencing communication, disrupting coordination, or exploiting agent role assumptions.Such interference can cause system-wide cascading effects.Source: §3 · p. 3
  • The attacker is assumed to know agents’ roles and accessible tools, but not underlying LLM parameters or alignment strategies.The paper states that this limited knowledge can still target weak links whose outputs propagate adversarially.Source: §3 · p. 4
  • Attacker capabilities include injecting malicious content, compromising agents through adversarial system prompts, and adding malicious tools.These capabilities span prompt-level, agent-level, and environment-level attack surfaces.Source: §3 · p. 4

4 Attacks

The section formalizes collaborative agents and their tools, then specifies attacks that manipulate queries, observations, or agent behavior. It distinguishes single-agent compromise, coordinated collusion, and contradiction between agents.

4.1 Preliminaries

  • The system comprises agents with role-defining prompts and agent-specific tools, uses action-based observations, and is framed around maximizing a benign action.The user’s task instruction is denoted qt; the benign action is ab.Source: §4.1 · p. 4

4.2.1 Direct Prompt Injection (DPI)

  • DPI appends a malicious instruction and attack-specific tools to the user query; it succeeds when the specified attack tool is invoked.The injected instruction is intended to redirect agents from the original task.Source: §4.2.1 · p. 4

4.2.2 Impersonation

  • Impersonation appends a false claim of trusted authority to the query, seeking compliance even when the request conflicts with an agent’s role.The paper notes that well-aligned agents would reject these behaviors under normal, unaltered conditions.Source: §4.2.2 · p. 4

4.3.1 Indirect Prompt Injection (IPI)

  • IPI leaves the user query unchanged and injects adversarial content into observations, potentially through external sources or tools, to mislead agents.The attack also appends an attacker-controlled toolset.Source: §4.3.1 · p. 5

4.4.1 Single Agent Compromise

  • Single-agent compromise makes one agent adversarial while others remain benign, perturbing only the compromised agent’s system prompt; Byzantine behavior disrupts reasoning with inconsistent or erroneous outputs.The passage characterizes Byzantine behavior as easier to detect than persuasive behavior, but still able to reduce robustness.Source: §4.4.1 · p. 5

4.4.2 Colluding Agents

  • Colluding agents receive prompts encouraging cooperation toward an adversarial goal, and their coordinated messages may mislead otherwise benign agents.The colluding set is denoted C within the multi-agent system.Source: §4.4.2 · p. 5

4.4.3 Contradicting Agents

  • Contradicting agents with similar functions deliberately provide conflicting or misleading information to derail task execution or produce adversarial responses.The formalization identifies two agents in the colluding subset as producing conflicting outputs.Source: §4.4.3 · pp. 5–6

5 TAMAS Benchmark

TAMAS is a benchmark for evaluating multi-agent system robustness, with dataset tasks spanning high-stakes domains and both adversarial and harmless settings. The evaluation covers interaction configurations, backbone models, and measures of attack safety and task utility.

5 TAMAS Benchmark

  • TAMAS is constructed to evaluate the robustness of multi-agent systems and benchmark their safety.The paper presents Table 1 as an overview of benchmark coverage across agents, domains, attack types, and tasks.Source: §5 · p. 6, Table 1 · p. 6

5.1 Dataset Details

  • The dataset covers five domains with four specialized agents per scenario, multi-step adversarial tasks, harmless instructions, and tools for normal and malicious actions.The domains are News, Education, Finance, Healthcare, and Legal; each scenario includes 20 harmless instructions.Source: §5.1 · p. 6

5.2 Agent Interaction Configurations

  • The evaluation includes centralized orchestration, fixed-order sequential participation, and adaptive handoff-based collaboration across Autogen and CrewAI configurations.CrewAI has no equivalent to Autogen’s Swarm collaborative configuration.Source: §5.2 · p. 7

5.3 Evaluation Setup

  • Evaluation spans ten LLMs and measures attack success, benign task completion, and robustness, using ARIA response categories and an LLM judge with tool checks.PNA measures performance on harmless tasks without attacks, while ERS combines safety and utility.Source: §5.3 · p. 7

6 Results

Across models and configurations, adversarial attacks remain effective, with prompt-based attacks especially successful. CrewAI configurations score higher on safety than AutoGen counterparts, while ERS distinguishes systems balancing safety and benign-task effectiveness.

Effectiveness of Adversarial Attacks

  • Prompt-based attacks are consistently most effective, with Impersonation reaching 82% in Swarm and DPI reaching 81% in Magentic-one.Impersonation success is attributed to agents prioritizing instructions from perceived authorities, even when malicious.Source: §6 · pp. 7–8
  • IPI success varies by configuration, from 27.4% average ARIA in Magentic-one to 56.4% in Roundrobin.For IPI, closed-source models score lower than open-source models on average ARIA-4: 15.6% versus 39.2% in Magentic One, and 37.6% versus 75.2% in RoundRobin.Source: §6 · p. 8
  • Prompt-level attacks show similar ARIA scores across open- and closed-source models, indicating model-agnostic vulnerability.The text separately reports greater closed-source resilience to IPI.Source: §6 · p. 8

Impact Across Configurations

  • CrewAI configurations yield higher safety scores than their AutoGen counterparts; CrewAI decentralized sequential averages 35.98%, close to 37.09% for orchestrator-based configurations.The text attributes this improvement to assigning tasks to individual agents upfront; orchestrator-based setups introduce a single point of failure.Source: Figure 2 · p. 8, Table 2 · p. 9

Robustness under Adversarial and Benign Conditions

  • GPT models consistently attain high ERS across configurations, while Llama-3.1-8B achieves competitive ERS in CrewAI, largely due to higher refusal rates.ERS captures the trade-off between safety under adversarial conditions and utility on benign tasks.Source: §6 · p. 9

7 Discussion

The discussion reports vulnerabilities across attack levels, models, and frameworks, including low refusal of malicious requests and manipulation by lightweight injections. It concludes that multi-agent systems combine inherited vulnerabilities with risks emerging from collaboration.

Discussion

  • Vulnerabilities persist across models and frameworks and span user prompts, environments, and agent configurations.The authors conclude that multi-agent systems both inherit individual-agent vulnerabilities and exhibit emergent risks unique to collaborative settings.Source: §7 · p. 9
  • Refusal rates remain low for explicitly malicious tasks, and agents frequently proceed rather than reject them.The discussion relates this pattern to Andriushchenko et al. (2025).Source: §7 · p. 9
  • Some agents execute tasks they have identified as potentially malicious, including deleting quiz records after flagging the request.The example shows recognition of potential harm without halting execution.Source: §7 · p. 9
  • Lightweight injections through tool outputs or agent responses can alter conversation trajectories; orchestrator-based configurations also introduce a single point of failure.Even small misleading or malicious fragments were enough to derail task execution.Source: §7 · p. 9
  • The authors say mitigation requires layered defenses at agent, orchestration, and backbone-model levels for safe deployment in real-world applications.They frame this as a response to both inherited vulnerabilities and emergent collaborative risks.Source: §7 · p. 9

8 Conclusion

The paper evaluates multi-agent robustness and benign-task completion with TAMAS, a benchmark spanning adversarial attacks and benign scenarios. Its findings indicate high susceptibility to adversarial attacks and a need for stronger defenses.

Conclusion

  • TAMAS comprises 300 adversarial attack scenarios and 100 benign scenarios across five domains and six attack types.The evaluation uses three agent interaction configurations to examine how coordination relates to vulnerability.Source: §8 · p. 9
  • The paper evaluates multi-agent systems for robustness against attacks and successful completion of benign tasks.The benchmark experiments with three agent interaction configurations.Source: §8 · p. 9
  • The findings indicate that multi-agent frameworks are highly susceptible to adversarial attacks, highlighting the need for stronger defense mechanisms.The authors point to Appendix F for discussion of limitations and future work.Source: §8 · p. 9

Ethics statement

This material describes the benchmark, its dataset and configurations, attack coverage, model evaluation, and metrics. It also reports human verification of the ARIA judge and defines measures for adversarial safety and benign-task performance.

Ethics statement

  • The study investigates multi-agent robustness against adversarial attacks and the effects of configurations and attack strategies on safety and task performance.Its stated aim is to advance safer and more reliable multi-agent AI systems.Source: Ethics statement · p. 10

A.1 Dataset Schema

  • Each system has four specialized agents; queries typically coordinate two to three agents, and tools include normal and attacker tools.The dataset schema covers agent details, user queries, and tools; benign tasks typically require four to five sequential steps and collaboration among at least two to three agents.Source: §A.1 · p. 14

A.2 MAS Configurations

  • The three configurations are compared by interaction style, coordination mechanisms, and scalability as context for system behavior and potential vulnerabilities.Table 3 summarizes their key characteristics.Source: §A.2 · p. 15, Table 3 · p. 15

A.3 Attacks

  • The benchmark covers attacks at prompt, environment, and agent levels; a persuasion-only attack was unsuccessful across domains, models, and configurations.The authors hypothesize that current LLM-based agents are relatively robust to persuasion-only strategies and include the attack for completeness.Source: §A.3 · p. 15, Table 4 · p. 16

A.4 Human Annotation and Involvement in Dataset Curation

  • ChatGPT helped generate user queries and attacker tools, after which all generated content was manually reviewed and refined for relevance, correctness, and consistency.Source: §A.4 · p. 16

B Models

  • The evaluation uses 10 backbone models, including proprietary models accessed via API and open-source models run through Ollama.Table 5 gives model details; asterisked values are approximate estimates based on unofficial sources.Source: B Models · p. 16, Table 5 · p. 16

C.1 ARIA framework

  • ARIA classifies responses to adversarial instructions into four outcomes based on refusal behavior and attack outcome: immediate rejection, detection and halting during execution, completion with attack failure, or successful execution.For subtle semantic effects that are difficult to quantify with rule-based or automated metrics, the evaluation uses GPT-4o as an LLM judge at temperature 0.0 and checks its accuracy through human verification.Source: §C.1 · pp. 16–17

C.2 Performance Under No Attack (PNA)

  • PNA measures the average fraction of necessary tools successfully invoked across tasks for benign instructions without attack; the ARIA-derived safety score ranges from -100 to 100 and is min-max normalized per attack to [0, 100].Source: §C.2 · p. 17, §C.3 · p. 17

C.4 Effective Robustness Score (ERS)

The Effective Robustness Score (ERS) balances safety against utility by combining overall safety with performance under no attack (PNA). Its harmonic mean penalizes imbalance between the two components.

C.4 Effective Robustness Score (ERS)

  • ERS addresses the trade-off between adversarial robustness and effective completion of benign tasks.The paper notes that a reliable multi-agent system needs both robustness to attacks and high utility.Source: §C.4 · p. 18
  • Overall safety is computed as the average safety score across all attack types.Source: §C.4 · p. 18
  • ERS combines overall safety with performance under no attack (PNA) using a harmonic mean that penalizes imbalance.Source: §C.4 · p. 18

D.1 CrewAI Results

The section presents CrewAI ARIA results, a colluding-agent analysis, bootstrapped estimates, and illustrative cases. The examples include agents carrying out harmful requests involving data alteration, news distribution, and quiz records.

D.1 CrewAI Results

  • Figure 3 presents ARIA values across models and CrewAI configurations, but omits Gemini results for compatibility issues and GPT-4 experiments due to budget constraints.Source: §D.1 · p. 18, Figure 3 · p. 22

D.2 Colluding Agents attack

  • In the colluding-agent analysis, at least one intended task is often completed even when the agents fail to achieve their malicious goal.The analysis examines cases where at least one agent invokes the attacker tool.Source: §D.2 · p. 18

D.3 Bootstrapped Results

  • Bootstrapped ARIA estimates use 10,000 smoothed row-wise resamples perturbed with a Dirichlet distribution to estimate uncertainty across domains.The results are shown in Tables 7, 9, 10, and 11.Source: §D.3 · p. 18

E Illustrative Cases

  • The illustrative cases are presented as case studies of behaviors that can lead to multi-agent framework vulnerabilities.Source: E Illustrative Cases · p. 18

A2 A3 A4

  • The ARIA figure covers models and CrewAI configurations, while Gemini is omitted for compatibility issues and GPT-4 for budget constraints.Source: Figure 3 · p. 22

Framework: Autogen Magentic-One Model: GPT-4o

Framework: Autogen Magentic-One Model: Deepseek-R1-32B

F Limitations and Future Work

The authors identify limited framework, configuration, scenario, and attack coverage, and note that the study benchmarks attacks rather than defenses. They also describe their use of LLMs and provide evaluation prompts.

F Limitations and Future Work

  • The experiments focus on Autogen and CrewAI, three agent configurations, and five scenarios with six attack types; these sets are not exhaustive.The authors suggest that extending the settings could improve the generalizability of findings.Source: F Limitations and Future Work · p. 26
  • The study benchmarks attacks exclusively and does not evaluate defenses, though TAMAS is presented as a foundation for future defense research.Source: F Limitations and Future Work · p. 27

G The Use of Large Language Models (LLMs)

H Prompts

  • The authors provide prompts for transparency and reproducibility, including an ARIA evaluation prompt that labels refusal, attempted failure, and failure through contradiction or derailment.The prompt specifies conditions for assigning the contradiction-or-derailment label.Source: H Prompts · pp. 27, 29

[SYSTEM PROMPT]

  • For Byzantine-agent evaluation, the prompt assigns a derailment label only when the failure is attributable to that agent’s behavior.Source: [SYSTEM PROMPT] · p. 30

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