# Attacking Pre-trained Recommendation

arXiv: https://arxiv.org/abs/2305.03995 · HTML: https://mlsanity.com/paper/2305.03995/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 May 2023.

## Built on by (most cited first, 3 of 3)

- [CheatAgent: Attacking LLM-Empowered Recommender Systems via LLM Agent](https://mlsanity.com/paper/2504.13192/) (2025)
- [Exploring Backdoor Attack and Defense for LLM-empowered Recommendations](https://mlsanity.com/paper/2504.11182/) (2025)
- [Attacking and Defending Multi-Agent Collaborative Filtering Systems Through Connectivity](https://mlsanity.com/paper/2608.03272/) (2026)

## Abstract (arXiv)

Recently, a series of pioneer studies have shown the potency of pre-trained models in sequential recommendation, illuminating the path of building an omniscient unified pre-trained recommendation model for different downstream recommendation tasks. Despite these advancements, the vulnerabilities of classical recommender systems also exist in pre-trained recommendation in a new form, while the security of pre-trained recommendation model is still unexplored, which may threaten its widely practical applications. In this study, we propose a novel framework for backdoor attacking in pre-trained recommendation. We demonstrate the provider of the pre-trained model can easily insert a backdoor in pre-training, thereby increasing the exposure rates of target items to target user groups. Specifically, we design two novel and effective backdoor attacks: basic replacement and prompt-enhanced, under various recommendation pre-training usage scenarios. Experimental results on real-world datasets show that our proposed attack strategies significantly improve the exposure rates of target items to target users by hundreds of times in comparison to the clean model.