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.
Cybersecurity ai: The dangerous gap between automation and autonomy
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The 2025 AI Agent Index catalogs technical and safety details for 30 deployed AI agents and finds low developer transparency on safety, evaluations, and societal impacts.
AI-emulated APTs compromise enterprise hosts and weaponize defender tools in 8/10 trials while military ranges resist, indicating TTP attribution fails when agents can be scaffolded to mimic threat actors.
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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The 2025 AI Agent Index: Documenting Technical and Safety Features of Deployed Agentic AI Systems
The 2025 AI Agent Index catalogs technical and safety details for 30 deployed AI agents and finds low developer transparency on safety, evaluations, and societal impacts.
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Synthetic APTs: the Collapse of TTP-Based Attribution
AI-emulated APTs compromise enterprise hosts and weaponize defender tools in 8/10 trials while military ranges resist, indicating TTP attribution fails when agents can be scaffolded to mimic threat actors.