# Adversarial Attacks on Online Learning to Rank with Stochastic Click Models

arXiv: https://arxiv.org/abs/2305.19218 · HTML: https://mlsanity.com/paper/2305.19218/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 47 references that are in the map.
- First posted on arXiv 30 May 2023.

## Builds on (most cited first, 25 of 25)

- [Stochastic bandits robust to adversarial corruptions](https://mlsanity.com/paper/1803.09353/) (2018)
- [Cascading Bandits: Learning to Rank in the Cascade Model](https://mlsanity.com/paper/1502.02763/) (2015)
- [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)
- [Cascading Bandits for Large-Scale Recommendation Problems](https://mlsanity.com/paper/1603.05359/) (2016)
- [Combinatorial Cascading Bandits](https://mlsanity.com/paper/1507.04208/) (2015)
- [Adversarial Attacks on Linear Contextual Bandits](https://mlsanity.com/paper/2002.03839/) (2020)
- [TopRank: A practical algorithm for online stochastic ranking](https://mlsanity.com/paper/1806.02248/) (2018)
- [Multiple-Play Bandits in the Position-Based Model](https://mlsanity.com/paper/1606.02448/) (2016)
- [Online Learning to Rank in Stochastic Click Models](https://mlsanity.com/paper/1703.02527/) (2017)
- [Differentiable Unbiased Online Learning to Rank](https://mlsanity.com/paper/1809.08415/) (2018)
- [Online Learning to Rank with Features](https://mlsanity.com/paper/1810.02567/) (2018)
- [DCM Bandits: Learning to Rank with Multiple Clicks](https://mlsanity.com/paper/1602.03146/) (2016)
- [Learning Product Rankings Robust to Fake Users](https://mlsanity.com/paper/2009.05138/) (2020)
- [When Are Linear Stochastic Bandits Attackable?](https://mlsanity.com/paper/2110.09008/) (2021)
- [Minimax Regret for Cascading Bandits](https://mlsanity.com/paper/2203.12577/) (2022)
- [Efficient Exploration of Gradient Space for Online Learning to Rank](https://mlsanity.com/paper/1805.07317/) (2018)
- [Variance Reduction in Gradient Exploration for Online Learning to Rank](https://mlsanity.com/paper/1906.03766/) (2019)
- [The Capacity of Robust Private Information Retrieval with Colluding Databases](https://mlsanity.com/paper/1605.00635/) (2016)
- [PairRank: Online Pairwise Learning to Rank by Divide-and-Conquer](https://mlsanity.com/paper/2103.00368/) (2021)
- [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 Gaussian Process Bandits](https://mlsanity.com/paper/2110.08449/) (2021)
- [Efficient Action Poisoning Attacks on Linear Contextual Bandits](https://mlsanity.com/paper/2112.05367/) (2021)

## Built on by (most cited first, 6 of 6)

- [Online Experimental Design With Estimation-Regret Trade-off Under Network Interference](https://mlsanity.com/paper/2412.03727/) (2024)
- [Practical Adversarial Attacks on Stochastic Bandits via Fake Data Injection](https://mlsanity.com/paper/2505.21938/) (2025)
- [Stealthy Adversarial Attacks on Stochastic Multi-Armed Bandits](https://mlsanity.com/paper/2402.13487/) (2024)
- [Design-Based Bandits Under Network Interference: Trade-Off Between Regret and Statistical Inference](https://mlsanity.com/paper/2510.07646/) (2025)
- [Adversarial Attacks on Cooperative Multi-agent Bandits](https://mlsanity.com/paper/2311.01698/) (2023)
- [Observation-Free Attacks on Online Learning to Rank](https://mlsanity.com/paper/2509.22855/) (2025)

## Abstract (arXiv)

We propose the first study of adversarial attacks on online learning to rank. The goal of the adversary is to misguide the online learning to rank algorithm to place the target item on top of the ranking list linear times to time horizon T with a sublinear attack cost. We propose generalized list poisoning attacks that perturb the ranking list presented to the user. This strategy can efficiently attack any no-regret ranker in general stochastic click models. Furthermore, we propose a click poisoning-based strategy named attack-then-quit that can efficiently attack two representative OLTR algorithms for stochastic click models. We theoretically analyze the success and cost upper bound of the two proposed methods. Experimental results based on synthetic and real-world data further validate the effectiveness and cost-efficiency of the proposed attack strategies.