# Stealthy Adversarial Attacks on Stochastic Multi-Armed Bandits

arXiv: https://arxiv.org/abs/2402.13487 · HTML: https://mlsanity.com/paper/2402.13487/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 25 references that are in the map.
- First posted on arXiv 21 Feb 2024.

## Builds on (most cited first, 20 of 20)

- [Stochastic bandits robust to adversarial corruptions](https://mlsanity.com/paper/1803.09353/) (2018)
- [Better Algorithms for Stochastic Bandits with Adversarial Corruptions](https://mlsanity.com/paper/1902.08647/) (2019)
- [Vulnerability of Deep Reinforcement Learning to Policy Induction Attacks](https://mlsanity.com/paper/1701.04143/) (2017)
- [Adaptive Reward-Poisoning Attacks against Reinforcement Learning](https://mlsanity.com/paper/2003.12613/) (2020)
- [Policy Poisoning in Batch Reinforcement Learning and Control](https://mlsanity.com/paper/1910.05821/) (2019)
- [Stochastic Linear Bandits Robust to Adversarial Attacks](https://mlsanity.com/paper/2007.03285/) (2020)
- [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)
- [Deceptive Reinforcement Learning Under Adversarial Manipulations on Cost Signals](https://mlsanity.com/paper/1906.10571/) (2019)
- [Vulnerability-Aware Poisoning Mechanism for Online RL with Unknown Dynamics](https://mlsanity.com/paper/2009.00774/) (2020)
- [Adversarial Attacks on Linear Contextual Bandits](https://mlsanity.com/paper/2002.03839/) (2020)
- [Robust Stochastic Linear Contextual Bandits Under Adversarial Attacks](https://mlsanity.com/paper/2106.02978/) (2021)
- [Provably Efficient Black-Box Action Poisoning Attacks Against Reinforcement Learning](https://mlsanity.com/paper/2110.04471/) (2021)
- [Data Poisoning Attacks in Contextual Bandits](https://mlsanity.com/paper/1808.05760/) (2018)
- [The Intrinsic Robustness of Stochastic Bandits to Strategic Manipulation](https://mlsanity.com/paper/1906.01528/) (2019)
- [Robust Stochastic Bandit Algorithms under Probabilistic Unbounded Adversarial Attack](https://mlsanity.com/paper/2002.07214/) (2020)
- [When Are Linear Stochastic Bandits Attackable?](https://mlsanity.com/paper/2110.09008/) (2021)
- [Action-Manipulation Attacks Against Stochastic Bandits: Attacks and Defense](https://mlsanity.com/paper/2002.08000/) (2020)
- [Adversarial Attacks on Online Learning to Rank with Stochastic Click Models](https://mlsanity.com/paper/2305.19218/) (2023)
- [Adversarial Attacks on Combinatorial Multi-Armed Bandits](https://mlsanity.com/paper/2310.05308/) (2023)

## Built on by (most cited first, 2 of 2)

- [Observation-Free Attacks on Online Learning to Rank](https://mlsanity.com/paper/2509.22855/) (2025)
- [When Can You Poison Rewards? A Tight Characterization of Reward Poisoning in Linear MDPs](https://mlsanity.com/paper/2604.10062/) (2026)

## Abstract (arXiv)

Adversarial attacks against stochastic multi-armed bandit (MAB) algorithms have been extensively studied in the literature. In this work, we focus on reward poisoning attacks and find most existing attacks can be easily detected by our proposed detection method based on the test of homogeneity, due to their aggressive nature in reward manipulations. This motivates us to study the notion of stealthy attack against stochastic MABs and investigate the resulting attackability. Our analysis shows that against two popularly employed MAB algorithms, UCB1 and ε-greedy, the success of a stealthy attack depends on the environmental conditions and the realized reward of the arm pulled in the first round. We also analyze the situation for general MAB algorithms equipped with our attack detection method and find that it is possible to have a stealthy attack that almost always succeeds. This brings new insights into the security risks of MAB algorithms.