Malicious agents in collaborative learning and outsourced data collection threaten the training of clean models. Backdoor attacks, where an attacker poisons a model during training to successfully achieve targeted misclassification, are a major concern to train-time robustness. In this paper, we investigate a multi-agent backdoor attack scenario, where multiple attackers attempt to backdoor a victim model simultaneously. A consistent backfiring phenomenon is observed across a wide range of games, where agents suffer from a low collective attack success rate. We examine different modes of backdoor attack configurations, non-cooperation / cooperation, joint distribution shifts, and game setups to return an equilibrium attack success rate at the lower bound. The results motivate the re-evaluation of backdoor defense research for practical environments.
@article{arxiv.2201.12211,
title = {Backdoors Stuck At The Frontdoor: Multi-Agent Backdoor Attacks That Backfire},
author = {Siddhartha Datta and Nigel Shadbolt},
journal= {arXiv preprint arXiv:2201.12211},
year = {2022}
}