# Adversarial Attacks on Cooperative Multi-agent Bandits

arXiv: https://arxiv.org/abs/2311.01698 · HTML: https://mlsanity.com/paper/2311.01698/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 24 references that are in the map.
- First posted on arXiv 3 Nov 2023.

## Builds on (most cited first, 15 of 15)

- [Adversarial Attacks on Stochastic Bandits](https://mlsanity.com/paper/1810.12188/) (2018)
- [Data Poisoning Attacks on Stochastic Bandits](https://mlsanity.com/paper/1905.06494/) (2019)
- [SIC-MMAB: Synchronisation Involves Communication in Multiplayer Multi-Armed Bandits](https://mlsanity.com/paper/1809.08151/) (2018)
- [Adversarial Attacks on Linear Contextual Bandits](https://mlsanity.com/paper/2002.03839/) (2020)
- [Fair Exploration via Axiomatic Bargaining](https://mlsanity.com/paper/2106.02553/) (2021)
- [Robust Multi-Agent Multi-Armed Bandits](https://mlsanity.com/paper/2007.03812/) (2020)
- [Heterogeneous Multi-player Multi-armed Bandits: Closing the Gap and Generalization](https://mlsanity.com/paper/2110.14622/) (2021)
- [One More Step Towards Reality: Cooperative Bandits with Imperfect Communication](https://mlsanity.com/paper/2111.12482/) (2021)
- [Selfish Robustness and Equilibria in Multi-Player Bandits](https://mlsanity.com/paper/2002.01197/) (2020)
- [Private and Byzantine-Proof Cooperative Decision-Making](https://mlsanity.com/paper/2205.14174/) (2022)
- [Distributed Bandits with Heterogeneous Agents](https://mlsanity.com/paper/2201.09353/) (2022)
- [Multiple-Play Stochastic Bandits with Shareable Finite-Capacity Arms](https://mlsanity.com/paper/2206.08776/) (2022)
- [Adversarial Attacks on Adversarial Bandits](https://mlsanity.com/paper/2301.12595/) (2023)
- [Adversarial Attacks on Online Learning to Rank with Click Feedback](https://mlsanity.com/paper/2305.17071/) (2023)
- [Adversarial Attacks on Online Learning to Rank with Stochastic Click Models](https://mlsanity.com/paper/2305.19218/) (2023)

## Abstract (arXiv)

Cooperative multi-agent multi-armed bandits (CMA2B) consider the collaborative efforts of multiple agents in a shared multi-armed bandit game. We study latent vulnerabilities exposed by this collaboration and consider adversarial attacks on a few agents with the goal of influencing the decisions of the rest. More specifically, we study adversarial attacks on CMA2B in both homogeneous settings, where agents operate with the same arm set, and heterogeneous settings, where agents have distinct arm sets. In the homogeneous setting, we propose attack strategies that, by targeting just one agent, convince all agents to select a particular target arm T-o(T) times while incurring o(T) attack costs in T rounds. In the heterogeneous setting, we prove that a target arm attack requires linear attack costs and propose attack strategies that can force a maximum number of agents to suffer linear regrets while incurring sublinear costs and only manipulating the observations of a few target agents. Numerical experiments validate the effectiveness of our proposed attack strategies.