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Securing autonomous AI agents demands a lifecycle-oriented approach, and AgentWard provides a blueprint for defense-in-depth across initialization, input processing, memory, decision-making, and execution.
Autonomous LLM agents are riddled with vulnerabilities, as point defenses fail to address cross-temporal and multi-stage systemic risks like memory poisoning and intent drift.
