ZERO-APT is a closed-loop framework that integrates an LLM attacker, configurable LLM defender, and judge agent to achieve 79% attack success rate, 0.860 causal consistency, and full decision auditability in penetration testing under intelligent defense.
L2M-AID: Autonomous cyber-physical defense by fusingsemanticreasoningoflargelanguagemodelswithmulti-agent reinforcement learning
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The paper proposes a bidirectional continuum between LLMs and control systems, covering LLM-assisted controller design, control-based LLM steering, and state-space modeling of LLMs.
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ZERO-APT: A Closed-Loop Adversarial Framework for LLM-Driven Automated Penetration Testing under Intelligent Defense
ZERO-APT is a closed-loop framework that integrates an LLM attacker, configurable LLM defender, and judge agent to achieve 79% attack success rate, 0.860 causal consistency, and full decision auditability in penetration testing under intelligent defense.
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When control meets large language models: From words to dynamics
The paper proposes a bidirectional continuum between LLMs and control systems, covering LLM-assisted controller design, control-based LLM steering, and state-space modeling of LLMs.