# Adversarial Attacks on Online Learning to Rank with Click Feedback

arXiv: https://arxiv.org/abs/2305.17071 · HTML: https://mlsanity.com/paper/2305.17071/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 26 references that are in the map.
- First posted on arXiv 26 May 2023.

## Builds on (most cited first, 17 of 17)

- [Cascading Bandits: Learning to Rank in the Cascade Model](https://mlsanity.com/paper/1502.02763/) (2015)
- [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)
- [Online Learning to Rank with Features](https://mlsanity.com/paper/1810.02567/) (2018)
- [Contextual Combinatorial Bandits with Probabilistically Triggered Arms](https://mlsanity.com/paper/2303.17110/) (2023)
- [DCM Bandits: Learning to Rank with Multiple Clicks](https://mlsanity.com/paper/1602.03146/) (2016)
- [Learning to Rank in the Position Based Model with Bandit Feedback](https://mlsanity.com/paper/2004.13106/) (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)
- [Online Learning to Rank with Top-k Feedback](https://mlsanity.com/paper/1608.06408/) (2016)
- [Adversarial Attacks on Adversarial Bandits](https://mlsanity.com/paper/2301.12595/) (2023)

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

- [Adversarial Attacks on Online Learning to Rank with Stochastic Click Models](https://mlsanity.com/paper/2305.19218/) (2023)
- [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)
- [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)
- [Online Learning to Rank under Corruption: A Robust Cascading Bandits Approach](https://mlsanity.com/paper/2511.03074/) (2025)

## Abstract (arXiv)

Online learning to rank (OLTR) is a sequential decision-making problem where a learning agent selects an ordered list of items and receives feedback through user clicks. Although potential attacks against OLTR algorithms may cause serious losses in real-world applications, little is known about adversarial attacks on OLTR. This paper studies attack strategies against multiple variants of OLTR. Our first result provides an attack strategy against the UCB algorithm on classical stochastic bandits with binary feedback, which solves the key issues caused by bounded and discrete feedback that previous works can not handle. Building on this result, we design attack algorithms against UCB-based OLTR algorithms in position-based and cascade models. Finally, we propose a general attack strategy against any algorithm under the general click model. Each attack algorithm manipulates the learning agent into choosing the target attack item T-o(T) times, incurring a cumulative cost of o(T). Experiments on synthetic and real data further validate the effectiveness of our proposed attack algorithms.