# Observation-Free Attacks on Online Learning to Rank

arXiv: https://arxiv.org/abs/2509.22855 · HTML: https://mlsanity.com/paper/2509.22855/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 22 references that are in the map.
- First posted on arXiv 26 Sep 2025.

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

- [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)
- [Algorithmic Collective Action in Machine Learning](https://mlsanity.com/paper/2302.04262/) (2023)
- [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)
- [Thompson Sampling Algorithms for Cascading Bandits](https://mlsanity.com/paper/1810.01187/) (2018)
- [Saving Stochastic Bandits from Poisoning Attacks via Limited Data Verification](https://mlsanity.com/paper/2102.07711/) (2021)
- [Algorithmic Collective Action in Recommender Systems: Promoting Songs by Reordering Playlists](https://mlsanity.com/paper/2404.04269/) (2024)
- [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)
- [Stealthy Adversarial Attacks on Stochastic Multi-Armed Bandits](https://mlsanity.com/paper/2402.13487/) (2024)

## Abstract (arXiv)

Online learning to rank (OLTR) plays a critical role in information retrieval and machine learning systems, with a wide range of applications in search engines and content recommenders. However, despite their extensive adoption, the susceptibility of OLTR algorithms to coordinated adversarial attacks remains poorly understood. In this work, we present a novel framework for attacking some of the widely used OLTR algorithms. Our framework is designed to promote a set of target items so that they appear in the list of top-K recommendations for T - o(T) rounds, while simultaneously inducing linear regret in the learning algorithm. We propose two novel attack strategies: CascadeOFA for CascadeUCB1 and PBMOFA for PBM-UCB . We provide theoretical guarantees showing that both strategies require only O(log T) manipulations to succeed. Additionally, we supplement our theoretical analysis with empirical results on real-world data.