MIT AI Risk Repository · Risk Sub-Category · 63.05.01
Error propagation
Category: Network Effects
Description
"Error Propagation. One well-known issue with communication networks is that information can be corrupted as it propagates through the network.24 As AI systems become capable of generating and processing more and more kinds of information, AI agents could end up ‘polluting the epistemic commons’ (Huang & Siddarth, 2023; Kay et al., 2024) of both other agents (Ju et al., 2024) and humans (see Case Study 7 and Section 3.1) Another increasingly important framework is the use of individual AI agents as part of teams and scaffolded chains of delegation, which transmit not only information but instr
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
- Unintentional
- Timing
- Post-deployment
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
- Foundationality May Cause Correlated Failures
- Multi-Agent Safety Is Not Assured by Single-Agent Safety
- Collusion between LLM-Agents
- Financial instability due to model homogeneity
- Multi-agent collaboration capability
- Impact on Financial Stability
- Multi-agent collusion propensity: