  As collaborative learning and the outsourcing of data collection become more
common, malicious actors (or agents) which attempt to manipulate the learning
process face an additional obstacle as they compete with each other. In
backdoor attacks, where an adversary attempts to poison a model by introducing
malicious samples into the training data, adversaries have to consider that the
presence of additional backdoor attackers may hamper the success of their own
backdoor. In this paper, we investigate the scenario of a multi-agent backdoor
attack, where multiple non-colluding attackers craft and insert triggered
samples in a shared dataset which is used by a model (a defender) to learn a
task. We discover a clear backfiring phenomenon: increasing the number of
attackers shrinks each attacker's attack success rate (ASR). We then exploit
this phenomenon to minimize the collective ASR of attackers and maximize
defender's robustness accuracy by (i) artificially augmenting the number of
attackers, and (ii) indexing to remove the attacker's sub-dataset from the
model for inference, hence proposing 2 defenses.
