arxiv: 2605.13170 · v1 · submitted 2026-05-13 · 💻 cs.LG · cs.MA

Recognition: no theorem link

Finding the Weakest Link: Adversarial Attack against Multi-Agent Communications

Maxwell Standen , Junae Kim , Claudia Szabo

Authors on Pith no claims yet

Pith reviewed 2026-05-14 19:17 UTC · model grok-4.3

classification 💻 cs.LG cs.MA

keywords adversarial attacksmulti-agent reinforcement learningcommunication perturbationJacobian gradientsmessage selectionvictim selectionadversarial loss functions

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The pith

Gradient-based selection of vulnerable messages and agents disrupts multi-agent reinforcement learning communications as effectively as random attacks in most cases.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper aims to show that single-victim attacks on multi-agent reinforcement learning can be sharpened by using Jacobian gradients to choose which messages to alter, which agent to target, and at which moments to strike. Two new loss functions are added to balance how much the attack succeeds with how much it actually harms overall system performance. Tests across navigation, PredatorPrey, and TrafficJunction environments with two communication methods show the message selection approach matching or beating random selection in nearly every scenario, while the full set of choices improves results in half of the thirty cases examined. A reader would care because multi-agent systems depend on message exchange for coordination, so finding these weak points reveals where security efforts should focus.

Core claim

The authors claim that a novel message selection method using Jacobian gradients achieves similar or greater impact than random message selection across almost all tested scenarios, and that combining it with victim selection, tempo adjustments, and two new adversarial loss functions improves attack effectiveness in half of the thirty scenarios tested.

What carries the argument

Jacobian gradient computation to rank messages, agents, and timesteps by susceptibility, paired with two adversarial loss functions that trade attack success against system-wide impact.

If this is right

The Jacobian-based message selection matches or exceeds random selection impact in almost all scenarios.
Victim selection, tempo, and the two loss functions raise effectiveness in half of the thirty scenarios.
The approach works against two different multi-agent communication methods.
Results hold in navigation, PredatorPrey, and TrafficJunction environments.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Communication channels appear to be the primary vulnerability when agents must coordinate actions.
Defenses could focus on detecting or hardening messages that carry high gradient sensitivity.
Approximations of the Jacobian might allow similar attacks even when full white-box access is unavailable.
The pattern may extend to other multi-agent systems that rely on message passing beyond reinforcement learning.

Load-bearing premise

The attacker has white-box access to the victim model to compute Jacobians and gradients for target selection.

What would settle it

Reproducing the thirty scenarios with the same environments and communication methods but replacing Jacobian-based selection with purely random choice and finding no advantage for the proposed method in most cases.

read the original abstract

Multi-agent systems rely on communication for information sharing and action coordination, which exposes a vulnerability to attacks. We investigate single-victim communication perturbation attacks against Multi-Agent Reinforcement Learning-trained systems and propose methods that use gradient information from the Jacobian to identify which messages, agent, and timesteps are most susceptible to attack and have the greatest impact on the system. We enhance these methods with two proposed adversarial loss functions that trade-off attack success for attack impact which also create more effective perturbations. We empirically demonstrate the effectiveness of our methods against two different multi-agent communication methods in navigation, PredatorPrey, and TrafficJunction environments. Our results show that our novel message selection method achieves a similar or greater impact than random message selection across almost all tested scenarios. Our victim selection, message selection, tempo, and loss functions improve attack effectiveness in half of the thirty scenarios we tested.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

Jacobian-based selection of messages and agents gives a modest edge over random attacks in multi-agent RL but requires white-box access and shows gains in only half the scenarios.

read the letter

The main thing here is a gradient-based method to choose which messages, agents, and timesteps to perturb in multi-agent reinforcement learning systems. They compute the Jacobian of the victim policy with respect to incoming messages to rank impact, then add two new loss functions that trade attack success rate against overall system disruption. This is new compared to earlier work that mostly relied on random perturbations or simpler heuristics for communication attacks in MARL. The experiments cover navigation, Predator-Prey, and Traffic Junction environments with two different communication methods, and the results show the selection approach matches or beats random in almost all cases while the full combination improves attack effectiveness in half of the thirty scenarios tested. That spread across environments is a reasonable strength for an empirical paper. The central limitation is that every selection step needs white-box access to compute Jacobians and gradients from the victim model. This assumption may not hold for black-box deployed systems where an attacker has only query access, so the practical reach is narrower than the abstract suggests. The abstract also leaves out details on statistical tests, exact baseline implementations, and hyperparameter choices, which makes the reported gains harder to evaluate precisely. This work is aimed at researchers studying adversarial robustness in multi-agent systems or security for autonomous coordination. Someone building or attacking MARL communication protocols would find concrete selection ideas worth testing, even if they need to extend it beyond white-box settings. It deserves peer review because the technique is distinct and the experiments are broad enough to warrant referee input on the assumptions and reporting.

Referee Report

2 major / 2 minor

Summary. The manuscript proposes gradient- and Jacobian-based methods to select which messages, victim agents, and timesteps to perturb in multi-agent reinforcement learning systems that rely on communication. It introduces two new adversarial loss functions that trade off attack success against impact and evaluates the full suite of techniques (message selection, victim selection, tempo, and losses) against two communication protocols in navigation, PredatorPrey, and TrafficJunction environments. The central empirical claims are that the novel message-selection heuristic matches or exceeds random message selection in almost all tested scenarios and that the combined components improve attack effectiveness in half of the thirty scenarios examined.

Significance. If the reported improvements prove robust, the work would usefully illustrate concrete attack surfaces in multi-agent communication and supply practical selection heuristics plus loss functions for white-box adversaries. The multi-environment evaluation is a positive feature. However, the white-box Jacobian/gradient requirement is a fundamental scope limitation that restricts direct transfer to black-box deployed systems, and the absence of statistical tests or variance reporting leaves the quantitative claims only moderately supported.

major comments (2)

[Abstract] Abstract: the claim that the combined methods 'improve attack effectiveness in half of the thirty scenarios' is load-bearing for the paper's contribution yet is stated without a quantitative definition of improvement (e.g., absolute or relative change in success rate or reward) or any statistical significance test; without these, it is impossible to judge whether the reported gains exceed experimental noise.
[Abstract] Abstract and method descriptions: all proposed selection procedures (message selection, victim selection, tempo) explicitly compute Jacobians or gradients of the victim policy with respect to incoming messages. This white-box assumption is never framed as a limitation or contrasted with query-only black-box alternatives, even though the strongest empirical claims rest on these gradient-based rankings.

minor comments (2)

The manuscript should report the precise random baseline implementation, all hyperparameter values, and the number of independent runs together with standard deviations or confidence intervals so that the 'similar or greater impact' and 'half of thirty scenarios' statements can be reproduced and assessed.
Clarify the exact communication protocols tested and the dimensionality of the message spaces; this information is needed to interpret the Jacobian computations and the relative difficulty of the attack task.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments. We address each major point below and will revise the manuscript to incorporate clarifications on quantitative claims and scope limitations.

read point-by-point responses

Referee: [Abstract] Abstract: the claim that the combined methods 'improve attack effectiveness in half of the thirty scenarios' is load-bearing for the paper's contribution yet is stated without a quantitative definition of improvement (e.g., absolute or relative change in success rate or reward) or any statistical significance test; without these, it is impossible to judge whether the reported gains exceed experimental noise.

Authors: We agree that the abstract claim requires a precise definition of improvement and statistical support. In revision, we will define 'improvement' explicitly (e.g., absolute increase in attack success rate of at least 5 percentage points or relative gain exceeding one standard deviation across runs) and add variance reporting plus significance tests (e.g., paired t-tests with p < 0.05) to the results section and abstract. revision: yes
Referee: [Abstract] Abstract and method descriptions: all proposed selection procedures (message selection, victim selection, tempo) explicitly compute Jacobians or gradients of the victim policy with respect to incoming messages. This white-box assumption is never framed as a limitation or contrasted with query-only black-box alternatives, even though the strongest empirical claims rest on these gradient-based rankings.

Authors: We acknowledge that the Jacobian- and gradient-based selection methods assume white-box access, which is a fundamental scope limitation. The revised manuscript will explicitly state this assumption as a limitation in the abstract, introduction, and a new limitations paragraph, while contrasting it with black-box alternatives and noting that the work targets white-box adversaries to expose concrete attack surfaces. revision: yes

Circularity Check

0 steps flagged

No circularity: purely empirical attack evaluation with no derivations or self-referential fits

full rationale

The paper proposes gradient-based selection heuristics for messages, victims, and timesteps, then evaluates them through direct experiments in navigation, PredatorPrey, and TrafficJunction environments. No equations, fitted parameters, or derivations appear that reduce a claimed result to its own inputs by construction. All performance claims rest on explicit comparisons against random baselines and ablations across 30 scenarios, with no self-citation chains or ansatzes invoked to justify core results. The white-box Jacobian assumption is stated as a limitation rather than hidden inside any derivation.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The paper is empirical and relies on standard multi-agent RL assumptions without introducing new free parameters or invented entities.

axioms (1)

domain assumption Multi-agent environments are modeled as standard multi-agent Markov decision processes with explicit communication channels.
Invoked to define the attack surface in the navigation, PredatorPrey, and TrafficJunction environments.

pith-pipeline@v0.9.0 · 5446 in / 1141 out tokens · 67688 ms · 2026-05-14T19:17:22.075416+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

36 extracted references · 36 canonical work pages

[1]

Multi-Agent Deep Reinforcement Learning Applications in Cybersecurity: Challenges and Perspectives,

Z. Tolba, N. E. H. Dehimi, S. Galland, S. Boukelloul, and D. Guassmi, “Multi-Agent Deep Reinforcement Learning Applications in Cybersecurity: Challenges and Perspectives, ” inInternational Conference on Electrical, Computer, Telecommuni- cation and Energy Technologies, Dec. 2024

work page 2024
[2]

Multi-agent reinforcement learning for cybersecurity: Approaches and challenges,

S. Finistrella, S. Mariani, and F. Zambonelli, “Multi-agent reinforcement learning for cybersecurity: Approaches and challenges, ” inWorkshop "From Objects to Agents", pp. 103–118, July 2024

work page 2024
[3]

F. A. Oliehoek and C. Amato,A Concise Introduction to Decentralized POMDPs. Springer International Publishing, 2016

work page 2016
[4]

Learning to Communi- cate with Deep Multi-Agent Reinforcement Learning,

J. Foerster, Y. Assael, N. de Freitas, and S. Whiteson, “Learning to Communi- cate with Deep Multi-Agent Reinforcement Learning, ” inAdvances in Neural Information Processing Systems, pp. 2145–2153, Dec. 2016

work page 2016
[5]

Succinct and robust multi-agent communication with temporal message control,

S. Zhang, J. Lin, and Q. Zhang, “Succinct and robust multi-agent communication with temporal message control, ” inAdvances in Neural Information Processing Systems, pp. 17271–17282, Dec. 2020

work page 2020
[6]

Communication learning via back- propagation in discrete channels with unknown noise,

B. Freed, G. Sartoretti, J. Hu, and H. Choset, “Communication learning via back- propagation in discrete channels with unknown noise, ” inAAAI Conference on Artificial Intelligence, pp. 7160–7168, Apr. 2020

work page 2020
[7]

Adversarial Attacks On Multi-Agent Communication,

J. Tu, T. Wang, J. Wang, S. Manivasagam, M. Ren, and R. Urtasun, “Adversarial Attacks On Multi-Agent Communication, ” inIEEE/CVF International Conference on Computer Vision, pp. 7748–7757, Oct. 2021

work page 2021
[8]

Cer- tifiably Robust Policy Learning against Adversarial Multi-agent Communication,

Y. Sun, R. Zheng, P. Hassanzadeh, Y. Liang, S. Feizi, S. Ganesh, and F. Huang, “Cer- tifiably Robust Policy Learning against Adversarial Multi-agent Communication, ” inInternational Conference on Learning Representations, Apr. 2023

work page 2023
[9]

Mis-spoke or mis-lead: Achieving Robustness in Multi-Agent Communicative Reinforcement Learning,

W. Xue, W. Qiu, B. An, Z. Rabinovich, S. Obraztsova, and C. K. Yeo, “Mis-spoke or mis-lead: Achieving Robustness in Multi-Agent Communicative Reinforcement Learning, ” inInternational Conference on Autonomous Agents and Multiagent Systems, pp. 1418–1426, May 2022

work page 2022
[10]

Grey-box Adversarial Attack on Communication in Multi- agent Reinforcement Learning,

X. Ma and W.-J. Li, “Grey-box Adversarial Attack on Communication in Multi- agent Reinforcement Learning, ” inInternational Conference on Autonomous Agents and Multiagent Systems, pp. 2448–2450, May 2023

work page 2023
[11]

Tactics of Adversarial Attack on Deep Reinforcement Learning Agents,

Y.-C. Lin, Z.-W. Hong, Y.-H. Liao, M.-L. Shih, M.-Y. Liu, and M. Sun, “Tactics of Adversarial Attack on Deep Reinforcement Learning Agents, ” inInternational Joint Conference on Artificial Intelligence, pp. 3756–3762, Aug. 2017

work page 2017
[12]

Stealthy and Efficient Adversarial Attacks against Deep Reinforcement Learning,

J. Sun, T. Zhang, X. Xie, L. Ma, Y. Zheng, K. Chen, and Y. Liu, “Stealthy and Efficient Adversarial Attacks against Deep Reinforcement Learning, ” inAAAI Conference on Artificial Intelligence, pp. 5883–5891, Apr. 2020

work page 2020
[13]

Delving into adversarial attacks on deep policies,

J. Kos and D. Song, “Delving into adversarial attacks on deep policies, ” inInter- national Conference on Learning Representations, Apr. 2017

work page 2017
[14]

Strategically-timed State-Observation Attacks on Deep Reinforcement Learning Agents,

Y. Qiaoben, X. Zhou, C. Ying, and J. Zhu, “Strategically-timed State-Observation Attacks on Deep Reinforcement Learning Agents, ” inICML Workshop on Adver- sarial Machine Learning, July 2021

work page 2021
[15]

Vulnerability Assessment of Deep Reinforcement Learning Models for Power System Topology Optimization,

Y. Zheng, Z. Yan, K. Chen, J. Sun, Y. Xu, and Y. Liu, “Vulnerability Assessment of Deep Reinforcement Learning Models for Power System Topology Optimization, ” IEEE Transactions on Smart Grid, vol. 12, pp. 3613–3623, Mar. 2021

work page 2021
[16]

Critical State Detection for Adversarial Attacks in Deep Reinforcement Learning,

R. Praveen Kumar, I. Niranjan Kumar, S. Sivasankaran, A. Mohan Vamsi, and V. Vijayaraghavan, “Critical State Detection for Adversarial Attacks in Deep Reinforcement Learning, ” inIEEE International Conference on Machine Learning and Applications, pp. 1761–1766, Dec. 2021

work page 2021
[17]

Explaining and Harnessing Adver- sarial Examples,

I. J. Goodfellow, J. Shlens, and C. Szegedy, “Explaining and Harnessing Adver- sarial Examples, ” inInternational Conference on Learning Representations, Mar. 2015

work page 2015
[18]

Towards Deep Learning Models Resistant to Adversarial Attacks,

A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu, “Towards Deep Learning Models Resistant to Adversarial Attacks, ” inInternational Conference on Learning Representations, Apr. 2018

work page 2018
[19]

On the Ro- bustness of Cooperative Multi-Agent Reinforcement Learning,

J. Lin, K. Dzeparoska, S. Q. Zhang, A. Leon-Garcia, and N. Papernot, “On the Ro- bustness of Cooperative Multi-Agent Reinforcement Learning, ” inIEEE Security and Privacy Workshops, pp. 62–68, May 2020

work page 2020
[20]

Who Is the Strongest Enemy? Towards Optimal and Efficient Evasion Attacks in Deep RL,

Y. Sun, R. Zheng, Y. Liang, and F. Huang, “Who Is the Strongest Enemy? Towards Optimal and Efficient Evasion Attacks in Deep RL, ” inInternational Conference on Learning Representations, Apr. 2022

work page 2022
[21]

Exploring the Vulnerability of Deep Reinforce- ment Learning-based Emergency Control for Low Carbon Power Systems,

X. Wan, L. Zeng, and M. Sun, “Exploring the Vulnerability of Deep Reinforce- ment Learning-based Emergency Control for Low Carbon Power Systems, ” in International Joint Conference on Artificial Intelligence, pp. 3954–3961, July 2022

work page 2022
[22]

Understanding adversarial attacks on observations in deep reinforcement learning,

Y. Qiaoben, C. Ying, X. Zhou, H. Su, J. Zhu, and B. Zhang, “Understanding adversarial attacks on observations in deep reinforcement learning, ”Science China Information Sciences, vol. 67, pp. 1869–1919, Apr. 2024

work page 1919
[23]

Towards Optimal Attacks on Reinforcement Learning Policies,

A. Russo and A. Proutiere, “Towards Optimal Attacks on Reinforcement Learning Policies, ” inAmerican Control Conference, pp. 4561–4567, May 2021

work page 2021
[24]

Learning adversarial attack policies through multi-objective reinforcement learning,

J. García, R. Majadas, and F. Fernández, “Learning adversarial attack policies through multi-objective reinforcement learning, ”Engineering Applications of Artificial Intelligence, vol. 96, Nov. 2020

work page 2020
[25]

Robust Deep Reinforcement Learning with adversarial attacks,

A. Pattanaik, Z. Tang, S. Liu, G. Bommannan, and G. Chowdhary, “Robust Deep Reinforcement Learning with adversarial attacks, ” inInternational Conference on Autonomous Agents and Multiagent Systems, pp. 2040–2042, July 2018. 9 Maxwell Standen, Junae Kim, and Claudia Szabo

work page 2040
[26]

Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations,

H. Zhang, H. Chen, C. Xiao, B. Li, M. Liu, D. Boning, and C.-J. Hsieh, “Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations, ” inAdvances in Neural Information Processing Systems, pp. 21024–21037, Dec. 2020

work page 2020
[27]

The Limitations of Deep Learning in Adversarial Settings,

N. Papernot, P. McDaniel, S. Jha, M. Fredrikson, Z. B. Celik, and A. Swami, “The Limitations of Deep Learning in Adversarial Settings, ” inIEEE European Symposium on Security and Privacy, pp. 372–387, Mar. 2016

work page 2016
[28]

Multi-agent Deep Reinforcement Learning with Extremely Noisy Observations,

O. Kilinc and G. Montana, “Multi-agent Deep Reinforcement Learning with Extremely Noisy Observations, ” Dec. 2018

work page 2018
[29]

hammer: Multi-level coordination of reinforcement learning agents via learned messaging,

N. Gupta, G. Srinivasaraghavan, S. Mohalik, N. Kumar, and M. Taylor, “hammer: Multi-level coordination of reinforcement learning agents via learned messaging, ” Neural Computing and Applications, Oct. 2023

work page 2023
[30]

Revisiting the Master-Slave Architecture in Multi-Agent Deep Reinforcement Learning,

X. Kong, B. Xin, F. Liu, and Y. Wang, “Revisiting the Master-Slave Architecture in Multi-Agent Deep Reinforcement Learning, ” Dec. 2017

work page 2017
[31]

A Survey of Multi-Agent Deep Reinforcement Learning with Communication,

C. Zhu, M. Dastani, and S. Wang, “A Survey of Multi-Agent Deep Reinforcement Learning with Communication, ”Autonomous Agents and Multi-Agent Systems, vol. 38, Jan. 2024

work page 2024
[32]

Playing Atari with Deep Reinforcement Learning,

V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra, and M. Riedmiller, “Playing Atari with Deep Reinforcement Learning, ” inDeep Learn- ing Workshop, Dec. 2013

work page 2013
[33]

Learning Multiagent Communication with Backpropagation,

S. Sukhbaatar, A. Szlam, and R. Fergus, “Learning Multiagent Communication with Backpropagation, ” inAdvances in Neural Information Processing Systems, pp. 2252–2260, Dec. 2016

work page 2016
[34]

Adversarial Machine Learning Attacks and Defences in Multi-Agent Reinforcement Learning,

M. Standen, J. Kim, and C. Szabo, “Adversarial Machine Learning Attacks and Defences in Multi-Agent Reinforcement Learning, ”ACM Comput. Surv., vol. 57, pp. 124:1–124:35, Jan. 2025

work page 2025
[35]

Adversarial Attacks on Neural Network Policies,

S. Huang, N. Papernot, I. Goodfellow, Y. Duan, and P. Abbeel, “Adversarial Attacks on Neural Network Policies, ” inInternational Conference on Learning Representations, Apr. 2017

work page 2017
[36]

Learning when to communicate at scale in multiagent cooperative and competitive tasks,

A. Singh, T. Jain, and S. Sukhbaatar, “Learning when to communicate at scale in multiagent cooperative and competitive tasks, ” inInternational Conference on Learning Representations, Apr. 2018. 10

work page 2018