The ACTING platform addresses the need for interoperable cyber-range training by providing a structured language (EDL-FG) for scenario description and automated evaluation mechanisms for complex, mult…

This paper provides the first comprehensive security analysis of the Agent Skills framework, identifying severe structural vulnerabilities that require fundamental architectural changes rather than si…

The paper analyzes the escalating security and safety threats posed by generative AI systems as they transition from merely generating content to executing real-world actions via tools and agents, fin…

The paper argues that agent security must be treated as a systems problem, requiring the enforcement of security invariants at the system level rather than solely relying on improving the underlying A…

The paper introduces a novel six-agent AI architecture for cybersecurity risk assessment, demonstrating high accuracy and speed compared to human experts, though its performance is ultimately limited…

The paper introduces the CAI Dataset, a massive, multi-terabyte corpus of real-world, hands-on cybersecurity LLM trajectories, designed to address the performance bottleneck caused by expert operator…

The paper introduces CyberGym-E2E, a large-scale, end-to-end benchmark designed to comprehensively evaluate AI agents' capabilities across the entire lifecycle of real-world software vulnerability dis…

The paper reports on penetration tests conducted on proprietary, large-scale AI agent systems, finding that security vulnerabilities persist despite stricter development standards.

The paper introduces CritBench, a novel framework to evaluate LLM cybersecurity capabilities specifically within IEC 61850 Digital Substation Operational Technology (OT) environments, finding that whi…

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…

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 introduces MATRA, a systematic threat modeling framework, to assess how known LLM threats translate into concrete, deployment-specific risks within autonomous agentic AI systems.

The paper forecasts that agentic AI will compress the cyber attack lifecycle by lowering the cost of multiple attack stages, necessitating immediate operational security upgrades for enterprises and t…

The paper proposes Dynamic Cyber Ranges, an advanced cyber range environment using LLM-driven Defender agents to counter the saturation of traditional security benchmarks, demonstrating that these dyn…

The paper introduces STRIATUM-CTF, a modular agentic framework that uses a standardized context protocol to enable LLMs to perform multi-step, stateful reasoning for general-purpose CTF solving, achie…

The paper surveys the use of LLMs for agentic NetOps and AIOps, arguing that operational reliability depends not on the model itself, but on robust surrounding machinery and workflow-centered evaluati…

The paper introduces CyberEvolver, a self-evolving agent framework that iteratively revises its own operational scaffold based on failed execution attempts, significantly improving cybersecurity agent…

This paper introduces and evaluates a scalable, reproducible 'CTF as a Service' (CaaS) platform designed to simplify the infrastructure management required for cybersecurity training.

This paper provides a systematic, layered review of security risks and defense strategies for autonomous agent frameworks, using OpenClaw as a case study to address the current lack of integrated rese…