Likely: Future AI will be decentralized for exactly these reasons. We don't want a single bad actor to control it. Security agencies are now warning that Russia is building a large botnet in the case it needs to go to war, and wants to disable enemy infrastructure. The US has similar needs.
Well designed game theory makes it possible for adversaries to cooperate. So it is no guarantee that Alice is always susceptible to Bob's attacks. Cryptography provides methods that can't be attacked if properly implemented. Defense and offense also can have differing costs: It can be way (computationally) cheaper to create defenses for Alice, than it is to craft adversarial offenses for Bob.
Though the risk is real: Spam preceded spam-filters. There was a short period (in internet years) where spam was more effective than our methods to counter it. So intelligent self-modifying worms/viruses will probably precede intelligent self-learning anti-viruses.
We also see both inverse reinforcement learning (learning about the policy of another agent through observing its behavior), adversarial RL (forcing another trading bot to make unprofitable decisions), and computational arms-races (who has the lowest latency?) between High Frequency Trading firms.
See also the Adversarial Bandit:
> Another variant of the multi-armed bandit problem is called the adversarial bandit, first introduced by Auer and Cesa-Bianchi (1998). In this variant, at each iteration an agent chooses an arm and an adversary simultaneously chooses the payoff structure for each arm. This is one of the strongest generalizations of the bandit problem as it removes all assumptions of the distribution and a solution to the adversarial bandit problem is a generalized solution to the more specific bandit problems.
Good robust RL algorithms are able to learn in the presence of adversarial noise. Correct information is information that allows you to compress reality better. When an agent is able to compress reality better (has access to a better generalizing world model), it will be rewarded. Correct information is information that helps an agent better optimize its policy function.
You actually hit on an interesting angle of research, and you probably will be vindicated in the near future, when adversarial images (those that fool state-of-the-art image classifiers to fail), move to adversarial agents (those that fool other agents into making bad decisions). However, this research was not about multi-agent systems, though the opponents (those that shoot fireballs and try to kill the agent) can already be seen as adversaries to the agent's goal of staying alive longer.