Agentic Loops and Agent Graphs: Security Risks You Need to Know
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Agentic loops and multi-agent graphs significantly expand the security attack surface of AI systems. Loops enable prompt injection to compound across iterations, allow irreversible actions to accumulate before human review, and let false context persist throughout a session. Multi-agent graphs introduce trust boundary problems where a single compromised agent can propagate malicious instructions downstream. Practical mitigations include setting hard step budgets, adding approval gates before irreversible actions, scoping tool permissions to specific tasks, treating all tool outputs as untrusted, and enforcing explicit trust boundaries between agents rather than inheriting permissions through delegation chains.
