Record summary
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Risk profile
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"Undetectable Threats. Cooperation and trust in many multi-agent systems relies crucially on the ability to detect (and then avoid or sanction) adversarial actions taken by others (Ostrom, 1990; Schneier, 2012). Recent developments, however, have shown that AI agents are capable of both steganographic communication (Motwani et al., 2024; Schroeder de Witt et al., 2023b) and ‘illusory’ attacks (Franzmeyer et al., 2023), which are black-box undetectable and can even be hidden using white-box undetectable encrypted backdoors (Draguns et al., 2024). Similarly, in environments where agents learn from interac- tions with others, it is possible for agents to secretly poison the training data of others (Halawi et al., 2024; Wei et al., 2023). If left unchecked, these new attack methods could rapidly destabilise cooperation and coordination in multi-agent systems."
Suggested mitigations
Defenses that may help with related attacks.
Source
Research source for this risk, when available.
Included resource
Multi-Agent Risks from Advanced AI
Original source
MIT AI Risk Repository
Open the public repository used for AI risk records and taxonomy fields.
