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,
2 Pith papers cite this work. Polarity classification is still indexing.
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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.
citing papers explorer
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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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Manifold Steering Reveals the Shared Geometry of Neural Network Representation and Behavior
Manifold steering along activation geometry induces behavioral trajectories matching the natural manifold of outputs, while linear steering produces off-manifold unnatural behaviors.