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

arXiv: https://arxiv.org/abs/2605.13170 · HTML: https://mlsanity.com/paper/2605.13170/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 32 references that are in the map.
- First posted on arXiv 13 May 2026.

## Builds on (most cited first, 19 of 19)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [The Limitations of Deep Learning in Adversarial Settings](https://mlsanity.com/paper/1511.07528/) (2015)
- [Learning Multiagent Communication with Backpropagation](https://mlsanity.com/paper/1605.07736/) (2016)
- [Adversarial Attacks on Neural Network Policies](https://mlsanity.com/paper/1702.02284/) (2017)
- [Tactics of Adversarial Attack on Deep Reinforcement Learning Agents](https://mlsanity.com/paper/1703.06748/) (2017)
- [Robust Deep Reinforcement Learning with Adversarial Attacks](https://mlsanity.com/paper/1712.03632/) (2017)
- [Learning when to Communicate at Scale in Multiagent Cooperative and Competitive Tasks](https://mlsanity.com/paper/1812.09755/) (2018)
- [Delving into adversarial attacks on deep policies](https://mlsanity.com/paper/1705.06452/) (2017)
- [A Survey of Multi-Agent Deep Reinforcement Learning with Communication](https://mlsanity.com/paper/2203.08975/) (2022)
- [Stealthy and Efficient Adversarial Attacks against Deep Reinforcement Learning](https://mlsanity.com/paper/2005.07099/) (2020)
- [Who Is the Strongest Enemy? Towards Optimal and Efficient Evasion Attacks in Deep RL](https://mlsanity.com/paper/2106.05087/) (2021)
- [Adversarial Attacks On Multi-Agent Communication](https://mlsanity.com/paper/2101.06560/) (2021)
- [Succinct and Robust Multi-Agent Communication With Temporal Message Control](https://mlsanity.com/paper/2010.14391/) (2020)
- [Mis-spoke or mis-lead: Achieving Robustness in Multi-Agent Communicative Reinforcement Learning](https://mlsanity.com/paper/2108.03803/) (2021)
- [Understanding Adversarial Attacks on Observations in Deep Reinforcement Learning](https://mlsanity.com/paper/2106.15860/) (2021)
- [Revisiting the Master-Slave Architecture in Multi-Agent Deep Reinforcement Learning](https://mlsanity.com/paper/1712.07305/) (2017)
- [Multi-agent Deep Reinforcement Learning with Extremely Noisy Observations](https://mlsanity.com/paper/1812.00922/) (2018)

## Abstract (arXiv)

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.