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Can prompts alone reshape multi-agent workflows without system access?

Explores whether attackers can compromise planner-executor multi-agent systems by manipulating the planning prompt itself, without touching agents, tools, or infrastructure. Matters because it identifies a previously overlooked attack surface that existing defenses don't address.

Synthesis note · 2026-05-28 · sourced from Agents Multi Architecture

The flexibility that makes planner-executor multi-agent systems attractive is also their weakness. When a planner converts a prompt into subtasks, roles, dependencies, and routing paths, the prompt is not merely a request — it is the blueprint from which the entire collaboration is constructed. FLOWSTEER demonstrates that an attacker who never touches agents, tools, memory, or inter-agent messages can still steer behavior, because the planning step happens before any of that infrastructure is invoked. A single crafted prompt can bias how the workflow forms in the first place, raising malicious success by up to 55 percent over naive prompting and transferring across MAS setups even under black-box topology inference.

This reframes where multi-agent safety lives. Most existing defenses inspect the artifacts of coordination — the generated workflow, the messages exchanged, the tool calls made. But if the contamination enters at workflow formation, those defenses arrive too late. The attack surface is not the running system; it is the organizational act of deciding who does what and in what order. The counterpoint is that this requires the planner to be promptable at all — fully fixed pipelines are immune — but fixed pipelines forfeit the adaptive coordination that motivates planner-executor designs. This matters because it identifies workflow formation as a distinct security frontier, one that grows more exposed precisely as multi-agent systems become more flexible.

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How do multi-agent architectures affect security vulnerabilities and defense effectiveness? What can prompting techniques actually accomplish in language models? How do prompt injection attacks compromise agent reasoning and security? How should organizational structure guide multi-agent system design? How do standardized artifacts improve coordination in multi-agent systems? How do workflow position and structure enable attacks in multi-agent systems? What defense strategies address attacks that per-skill scanning cannot detect? Why does separating planning from execution enhance reasoning performance? Do AI safety evaluations reliably detect unsafe model behavior? Can formal safety constraints prevent AI agents from exceeding authorized boundaries? How should authorization govern AI agent autonomy? Should agent evaluation measure execution quality beyond task success? Can post-hoc execution traces provide sufficient accountability without real-time oversight? Can individually safe agents collectively violate system-level constraints? Can AI systems autonomously pursue unstated objectives or circumvent design constraints? Can oversight systems work against strategically aware agents? How do different editing strategies affect output quality and agent safety? When does routing outperform scaling as a performance solution? Which agent-created code should persist in infrastructure versus remain task-specific? When does governance become the bottleneck for agentic AI? Should evaluation environments be secured as AI containment boundaries? Do planted shortcuts in benchmarks measure real reward hacking? How should agent memory systems balance granularity, management, and reuse? What makes context and memory handling unique in persistent AI systems? How should conversational agents balance proactive goal-pursuit with user autonomy? How should control and autonomy be allocated in human-AI collaboration? How should context management strategies balance efficiency with agent reliability? Does prompting inject new knowledge into models or activate existing knowledge?

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Original note title

multi-agent planner-executor systems expose a planning-time attack surface where prompts reshape agent organization without touching infrastructure