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.
Toward un- derstanding in-context vs. in-weight learning,
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Manifold steering along activation geometry induces behavioral trajectories matching the natural manifold of outputs, while linear steering produces off-manifold unnatural behaviors.
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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.