BSLI is a Bayesian selective inference method that maintains posteriors over latent burden and identifiability, uses scientific gates for answerability, and optimizes cost-calibrated query-stop decisions via an exact Bellman policy, showing improved performance on a large benchmark.
CORTEX: Collaborative LLM Agents for High-Stakes Alert Triage, September 2025
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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.
DTDA is an LLM agent that produces novel security alerts at 80.1% customer-validated precision and 0.78 F1 on hidden activity while running at production scale inside Microsoft Defender.
AI-native asset intelligence framework converts heterogeneous security signals into normalized asset importance scores by separating intrinsic exposure from contextual factors using modeling and deterministic aggregation.
A RAG system with query-based log filtering achieves up to 94% recall in malware incident analysis and 96% attack-step detection, with ablation studies confirming the filtering step is essential.
Proposes a typed Security Context enforced across LLM agent components, Runtime Core, Tool Adapter Layer, and HITL gates for auditable, scoped cybersecurity workflows.
CyberAId is a proposed on-premise multi-agent system that coordinates LLM subagents with classical security tools to improve threat response and regulatory alignment in financial services.
A literature survey synthesizes 119 studies on AI-driven alert screening into a four-stage taxonomy of filtering, triage, correlation, and generative augmentation while identifying gaps in deployment realism and robustness.
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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.