# Near Optimal Adversarial Attacks on Stochastic Bandits and Defenses with Smoothed Responses

arXiv: https://arxiv.org/abs/2008.09312 · HTML: https://mlsanity.com/paper/2008.09312/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Aug 2020.

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

- [Practical Adversarial Attacks on Stochastic Bandits via Fake Data Injection](https://mlsanity.com/paper/2505.21938/) (2025)
- [Stochastic Bandits Robust to Adversarial Attacks](https://mlsanity.com/paper/2408.08859/) (2024)

## Abstract (arXiv)

I study adversarial attacks against stochastic bandit algorithms. At each round, the learner chooses an arm, and a stochastic reward is generated. The adversary strategically adds corruption to the reward, and the learner is only able to observe the corrupted reward at each round. Two sets of results are presented in this paper. The first set studies the optimal attack strategies for the adversary. The adversary has a target arm he wishes to promote, and his goal is to manipulate the learner into choosing this target arm T - o(T) times. I design attack strategies against UCB and Thompson Sampling that only spend widehatO(√log T) cost. Matching lower bounds are presented, and the vulnerability of UCB, Thompson sampling, and varepsilon-greedy are exactly characterized. The second set studies how the learner can defend against the adversary. Inspired by literature on smoothed analysis and behavioral economics, I present two simple algorithms that achieve a competitive ratio arbitrarily close to 1.