Low-Loss Subspace Compression for Clean Gains against Multi-Agent Backdoor Attacks
This work addresses security vulnerabilities in multi-agent systems against backdoor attacks, with incremental improvements in defense mechanisms.
The paper tackles the problem of multi-agent backdoor attacks causing low clean-label accuracy due to the backfiring effect, and proposes three defenses based on agent dynamics and low-loss subspace construction that improve robustness against such attacks.
Recent exploration of the multi-agent backdoor attack demonstrated the backfiring effect, a natural defense against backdoor attacks where backdoored inputs are randomly classified. This yields a side-effect of low accuracy w.r.t. clean labels, which motivates this paper's work on the construction of multi-agent backdoor defenses that maximize accuracy w.r.t. clean labels and minimize that of poison labels. Founded upon agent dynamics and low-loss subspace construction, we contribute three defenses that yield improved multi-agent backdoor robustness.