The paper proposes a federated, high-throughput stream-processing framework for cross-sector threat detection and automated containment, achieving end-to-end operational convergence within 12-20 secon…

This paper introduces a foundational framework and taxonomy for managing catastrophic AI loss of control (LOC) incidents, providing a proportional guide for response based on the severity and recovera…

The study demonstrates that LLMs exhibit significant, language-driven disparities in medical triage recommendations, recommending emergency care more frequently for English and Arabic prompts, even wh…

The paper introduces SIR-Bench, a comprehensive benchmark of 794 test cases, to rigorously evaluate autonomous security incident response agents by measuring their ability to perform deep forensic inv…

The paper introduces an end-to-end framework that not only detects network intrusions using deep learning but also generates actionable, citation-grounded mitigation reports using a Retrieval-Augmente…

SOCpilot is a system that verifies the compliance of LLM-drafted incident response plans against mandatory policies and required procedural steps, significantly improving the reliability of AI-assiste…

This paper introduces GeoDisaster, an operational geospatial disaster reasoning benchmark with 2,921 instances and five task families, and proposes an orchestrated multi-agent framework with RCEA for…

This paper proposes an end-to-end AI-accelerated framework for upskilling workers, validated by industry partnerships and exam success.

This paper identifies and discusses twelve technical challenges that need to be addressed to safely and effectively scale drone technology for commercial use.

The paper introduces 'log-substrate prompt injection,' demonstrating that attacker-controlled log fields can be used to manipulate LLM-powered security analysis, with persona hijacking and context man…

The paper introduces MIRA, a bilingual benchmark that reveals that LLMs tend to dilute or omit critical medical information when responding to prompts from users with low health literacy, a pattern te…

The paper introduces TurnGate, a response-aware defense mechanism that detects the earliest turn in a multi-turn dialogue where the accumulated interaction enables a harmful action, significantly impr…

The paper proposes a Sovereign AI architecture for clinical triage that ensures maximum security by performing all inference on-device and receiving data only through physically unidirectional channel…

The paper introduces an AI red teaming agent that drastically reduces the time and effort required for security testing by allowing operators to define complex attack goals using natural language, com…

This study investigated whether Security Operations Center (SOC) analysts can justify their decisions when triaging alarms, finding that while they are often correct in identifying true threats, they…

The paper proposes an autonomous red teaming framework combining LLMs and RL to generate sophisticated, multi-stage cyber attack campaigns, demonstrating its necessity for evaluating robust AI-enabled…

This paper introduces a machine learning model, RuBR, and a methodology to reliably distinguish genuine astronomical transients from spurious detections for the upcoming Roman Space Telescope's data p…

The paper proposes an end-to-end LLM framework that automates SOC operations by integrating ensemble-based threat detection, syntax-constrained query generation, and evidence-grounded incident resolut…