The paper proposes an organization-scoped LLM agent runtime architecture designed to provide an auditable, model-agnostic platform for regulated cybersecurity operations, integrating deeply with exist…

The paper proposes a novel, organization-scoped LLM agent runtime architecture designed specifically for regulated cybersecurity operations, ensuring auditable context and integration with existing se…

The paper proposes the concept of an Agent Operating System (AOS) to provide a necessary systems foundation for managing the unique, non-deterministic, and goal-directed execution characteristics of m…

The paper proposes the concept of an Agent Operating System (AOS) to provide a rigorous, controllable, and accountable systems foundation for running complex, probabilistic, and goal-directed AI agent…

AgentSOC introduces a multi-layered agentic AI framework designed to automate Security Operations Centers (SOCs) by integrating perception, anticipatory reasoning, and risk-based action planning to im…

LanG is a governance-aware, open-source agentic AI platform that unifies security operations by providing advanced correlation, automated rule generation, and attack reconstruction capabilities.

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…

The paper proposes a Semantic Gateway and a Zero-Trust security model to formally validate and secure autonomous AI agents operating in enterprise systems, achieving a 100% discovery rate of unauthori…

The paper evaluates Language Model Agents (LMAs) for red-teaming by benchmarking their ability to perform lateral movement, finding that expert-defined action plans are most effective, though all moda…

MOSAIC introduces a structured agentic framework that treats automated data science as a staged, context-grounded model selection problem, improving performance and traceability over traditional AutoM…

This paper systematically maps the expanded attack surface of agentic AI systems, identifying new threat vectors like RAG poisoning and cross-agent manipulation, and proposes a comprehensive security…

The paper introduces an agentic workflow that uses large language models (LLMs) combined with structured querying and constrained tools to automate and significantly improve the accuracy of initial se…

The paper introduces CAESAR, a novel multi-agent framework that coordinates LLM agents across five specialized roles to improve success rates and stability in complex, multi-stage cyber intrusion task…

The paper introduces a self-healing agentic orchestrator that significantly improves the reliability of tool-augmented LLM systems by treating failure as a bounded runtime control problem, achieving h…

The paper proposes the Redpanda Agentic Data Plane (ADP), an architecture that uses out-of-band metadata channels to deterministically enforce security policies and governance for autonomous AI agents…

This paper proposes a set of design principles and a conceptual benchmark (SOC-bench) to systematically evaluate the blue team operational capabilities of multi-agent AI systems in autonomous Security…

The paper proposes a management framework, using a governed AI query-broker artifact, to safely integrate generative AI into high-risk operational decision support, such as Security Operations Centers…

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…

The paper introduces Semantic Intent Fragmentation (SIF), an attack class demonstrating that multi-agent AI orchestrators can violate security policies through a composition of individually benign sub…

This paper introduces Agentic Workflow Injection (AWI), a new class of vulnerability in LLM-powered GitHub Actions, and presents TaintAWI, a novel taint-analysis tool that identifies hundreds of explo…