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    Autonomous AI agents, powered by large language models (LLMs), introduce novel security risks. This survey examines these vulnerabilities and proposes the Reflective Risk-Aware Agent Architecture (R2A2) for enhanced AI safety.

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    Area of Science:

    • Artificial Intelligence
    • AI Security
    • Autonomous Systems

    Background:

    • Large language models (LLMs) enable autonomous AI agents with perception, reasoning, and action capabilities.
    • These agents represent a shift from static systems to interactive, memory-augmented entities.
    • Increased autonomy introduces novel security risks beyond conventional AI systems.

    Purpose of the Study:

    • To survey the structural foundations and capabilities of autonomous AI agents.
    • To analyze the security vulnerabilities and failure modes associated with agent autonomy.
    • To review existing defense strategies and introduce a novel safety framework.

    Main Methods:

    • Examination of agent autonomy structures (memory, tool use, planning, reasoning).
    • Analysis of security vulnerabilities across agent modules (perception, cognition, memory, action).
    • Systematic review of defense strategies and introduction of the Reflective Risk-Aware Agent Architecture (R2A2).

    Main Results:

    • Autonomous agents face risks like memory poisoning, tool misuse, reward hacking, and misalignment.
    • Vulnerabilities stem from architectural fragilities in perception, cognition, memory, and action.
    • Current defenses are often isolated and lack integrated coherence against emergent threats.

    Conclusions:

    • Agent autonomy significantly reshapes the AI security landscape.
    • Existing defenses are insufficient for managing complex, temporally extended threats.
    • The R2A2 framework offers a unified approach for principled, proactive AI safety.