Agentic Loops and Multi-Agent Graphs Expand AI Prompt Injection Risk

Auth0 published an analysis arguing that the most serious AI agent security risks stem from architectural choices—specifically agentic loops and multi-agent graphs—rather than the model alone. In loop-based systems attacker-controlled external content can be fed back into an agent's reasoning to persist and compound malicious instructions, while multi-agent graphs create trust-boundary failures where a compromised agent passes tainted instructions downstream; the report cites 2024 'Prompt Infection' research showing prompt injection can self-replicate across connected agents and recommends controls like step/time budgets, approval gates, scoped permissions, and treating tool output as untrusted.