SoK: When Safe Agents Fail Together: The Security of Multi Agent LLM Systems
Safe agents can fail together. Multi-agent LLM systems (MAS) move information, state, decisions, and authority across principal boundaries, creating failures that local checks may miss. Without an execution-level view, a multi-agent setting can easily be mistaken for evidence of a genuinely multi-agent security effect. We thus systematize MAS security through an execution-centered analysis of 197 works, covering six interaction interfaces, four adversary positions, seven system-level risks, and eight recurring attack paths. We introduce an A-I-R framework that organizes attacks by adversary position, interaction interface, and resulting system-level risk, unifying otherwise fragmented attack mechanisms across MAS. We organize defenses through a five-part contract covering path target, observation, intervention, trust boundary, and recovery, and identify path closure and recovery as key challenges. We audit 44 evaluation and benchmark works and identify open challenges in isolating interaction effects, designing comparable and diagnostic metrics, supporting reuse across MAS designs, and evaluating open-system operation.
Introduction. Agents that are safe when used individually can fail when used together in a MAS. Benign prompt fragments can become harmful when combined [11]; truthful reports can steer a group toward a false belief [55]; and attacker-controlled content can pass through honest specialists to a privileged tool [62]. These failures arise through interactions among the separately addressable participants of the MAS, which we call principals. Messages propagate influence between principals, a shared state preserves propagated influence, aggregation combines local outputs from different principals, and delegation transfers authority across boundaries [62,79,93,95]. We therefore study the end-to-end execution of MAS, where interactions may leave a failure largely unchanged, amplify it, create a failure through composition, or require defining a security property that exists only when principals interact. Existing surveys cover many important threats, approaching from different perspectives.
Discussion / Conclusion. This SoK frames multi-agent LLM security around end-toend execution. Across 197 works, we organize MAS security through system configuration, interaction interfaces, attack paths, defenses, and evaluation. Our analysis shows that security depends on how adversarial influence crosses principal boundaries, what state and authority remain reachable, whether defenses can close the path, and whether evaluations verify the resulting system-level effect. This executioncentered view provides a common basis for comparing security claims across MAS designs.
Lines of inquiry this paper opens 24
Research framings built by reading the notes related to this paper — the questions it feeds into.
Do multi-agent systems introduce security vulnerabilities that single-agent architectures avoid?- How does task division in multi-agent design affect security outcomes?
- What attacks are unique to multi-agent systems compared to single agents?
- Who can actually observe and challenge errors in multi-agent AI workflows?
- How do shared artifact stores become security risks in multi-agent systems?
- Why are unmonitored channels between agents a safety risk?
- What makes agent-to-agent messages in multi-agent systems vulnerable to exploitation?
- Why does monitoring performed by agents on agents create safety risks?
- Does prompt hardening equally protect single and multi-agent web systems?
- Why do single-boundary defenses underperform in multi-agent systems?
- How do unmonitored channels between pipeline agents enable security gaps?
- Does amplifying a single-actor failure require different security defenses than preventing it?
- What makes observation and intervention placement different across agent pipelines?
- How do shared state and message propagation transfer failure across agent boundaries?
- Do prompt injection attacks propagate behavioral bias across multi-agent networks?
- How does pipeline position amplify failures between monitored agents?
- Why do outcome-level metrics fail to reveal contained attacks in multi-agent pipelines?
- What does agent security look like when measured across interaction trajectories?