MIT AI Risk Repository · Risk Sub-Category · 63.02.03
Coercion and Extortion
Category: Conflict
Description
"Advanced AI systems might also lead to various forms of coercion and extortion in less extreme settings (Ellsberg, 1968; Harrenstein et al., 2007). These threats might target humans directly (such as the revelation of private information extracted by advanced AI surveillance tools), or other AI systems that are deployed on behalf of humans (such as by hacking a system to limit its resources or operational capacity; see also Section 3.7). Increasing AI cyber-offensive capabilities – including those that target other AI systems via adversarial attacks and jailbreaking (Gleave et al., 2020; Yami
From Multi-Agent Risks from Advanced AI (Hammond2025), as extracted by the MIT AI Risk Repository (CC BY 4.0).
Classification
- Subdomain
- 7.6 Multi-agent risks
- Causal entity
- AI
- Intent
- Other
- Timing
- Other
Subdomain definition: Risks from multi-agent interactions, due to incentives (which can lead to conflict or collusion) and/or the structure of multi-agent systems, which can create cascading failures, selection pressures, new security vulnerabilities, and a lack of shared information and trust.
How other frameworks describe this risk
- Groups of LLM-Agents May Show Emergent Functionality
- Collusion between LLM-Agents
- Multi-Agent Safety Is Not Assured by Single-Agent Safety
- Foundationality May Cause Correlated Failures
- Financial instability due to model homogeneity
- Impact on Financial Stability
- Multi-agent collaboration capability
- Multi-agent collusion propensity: