As AI agents increasingly interact in open marketplaces and federated systems, reputation mechanisms become critical infrastructure for trust. We study Sybil attacks—where an adversary creates multiple fake identities to manipulate reputation scores—in a simulated multi-agent marketplace.
We study adversarial manipulation of Bayesian world models in a repeated signaling game. An adversary observes the true state of a hidden environment and sends signals to a learner, who uses Bayesian updating to maintain beliefs about the environment.
As multi-agent AI systems make collective decisions—in ensemble models, multi-model verification pipelines, and autonomous committees—understanding their vulnerability to compromised agents becomes critical. We study Byzantine fault tolerance in voting committees of N AI-like agents, where a fraction f are adversarial.