# Attribute Inference Attacks in Online Multiplayer Video Games: a Case Study on Dota2

arXiv: https://arxiv.org/abs/2210.09028 · HTML: https://mlsanity.com/paper/2210.09028/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 72 references that are in the map.
- First posted on arXiv 17 Oct 2022.

## Builds on (most cited first, 10 of 10)

- [Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning](https://mlsanity.com/paper/1712.03141/) (2017)
- [Are Your Sensitive Attributes Private? Novel Model Inversion Attribute Inference Attacks on Classification Models](https://mlsanity.com/paper/2201.09370/) (2022)
- [Modeling Realistic Adversarial Attacks against Network Intrusion Detection Systems](https://mlsanity.com/paper/2106.09380/) (2021)
- [Graph Embedding for Recommendation against Attribute Inference Attacks](https://mlsanity.com/paper/2101.12549/) (2021)
- [You are Who You Know and How You Behave: Attribute Inference Attacks via Users' Social Friends and Behaviors](https://mlsanity.com/paper/1606.05893/) (2016)
- [The Role of Machine Learning in Cybersecurity](https://mlsanity.com/paper/2206.09707/) (2022)
- [PRICURE: Privacy-Preserving Collaborative Inference in a Multi-Party Setting](https://mlsanity.com/paper/2102.09751/) (2021)
- [A Comparison of Methods for Player Clustering via Behavioral Telemetry](https://mlsanity.com/paper/1407.3950/) (2014)
- [Modeling Individual Differences in Game Behavior using HMM](https://mlsanity.com/paper/1804.00245/) (2018)
- [Inference of Personal Attributes from Tweets Using Machine Learning](https://mlsanity.com/paper/1709.09927/) (2017)

## Built on by (most cited first, 4 of 4)

- [SoK: On the Offensive Potential of AI](https://mlsanity.com/paper/2412.18442/) (2024)
- ["All of Me": Mining Users' Attributes from their Public Spotify Playlists](https://mlsanity.com/paper/2401.14296/) (2024)
- ["bot lane noob" Towards Deployment of NLP-based Toxicity Detectors in Video Games](https://mlsanity.com/paper/2604.10175/) (2026)
- [From Beats to Breaches:How Offensive AI Infers Sensitive User Information from Playlists](https://mlsanity.com/paper/2605.04724/) (2026)

## Abstract (arXiv)

Did you know that over 70 million of Dota2 players have their in-game data freely accessible? What if such data is used in malicious ways? This paper is the first to investigate such a problem. Motivated by the widespread popularity of video games, we propose the first threat model for Attribute Inference Attacks (AIA) in the Dota2 context. We explain how (and why) attackers can exploit the abundant public data in the Dota2 ecosystem to infer private information about its players. Due to lack of concrete evidence on the efficacy of our AIA, we empirically prove and assess their impact in reality. By conducting an extensive survey on ~500 Dota2 players spanning over 26k matches, we verify whether a correlation exists between a player's Dota2 activity and their real-life. Then, after finding such a link (p < 0.01 and ρ > 0.3), we ethically perform diverse AIA. We leverage the capabilities of machine learning to infer real-life attributes of the respondents of our survey by using their publicly available in-game data. Our results show that, by applyingdomain expertise, some AIA can reach up to 98% precision and over 90% accuracy. This paper hence raises the alarm on a subtle, but concrete threat that can potentially affect the entire competitive gaming landscape. We alerted the developers of Dota2.