The paper proposes a dynamic queueing framework that estimates an organization's cyber resources and attack surface dynamics by analyzing the timestamps of vulnerabilities and fixes, achieving high ac…

This survey provides a comprehensive, systematic roadmap for achieving cyber-physical system (CPS) resilience by integrating five interconnected themes: system-wide properties, handling data scarcity…

The paper proposes an evidence-driven protocol combining Deterministic Build Systems and Trusted Execution Environments to provide cryptographically verifiable guarantees of software artifact integrit…

The paper proposes Sovereign 2.0, a control-plane-centric model redefining cloud sovereignty as enforceable governance authority and operational control, rather than mere data location.

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…

SCARA is a novel, end-to-end framework that autonomously connects binary-level vulnerability candidates to conditionally validated remedies for opaque industrial software, achieving high precision and…

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…

The paper introduces SafeRedirect, a system-level defense that prevents frontier LLMs from generating harmful content during legitimate tasks that structurally require it, significantly reducing unsaf…

The paper introduces APIOT, the first LLM framework capable of autonomously performing the full discovery, exploitation, patching, and verification cycle against bare-metal industrial OT devices.

The paper proposes using Trusted-Execution Environments (TEEs) to create a scalable, privacy-preserving system where authors can submit cryptographic proofs of correct research replication, thereby ad…

The paper develops a formal theory to analyze how throughput changes in AI-enhanced cybersecurity pipelines when stage capacities are perturbed by multipliers.

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…

This paper analyzes location-data provenance risks across multiple European sectors, proposing a risk taxonomy and architectural design for a next-generation digital trust infrastructure that treats l…

The paper introduces SynthChain, a comprehensive, multi-source synthetic testbed and dataset that demonstrates that detecting advanced software supply chain attacks requires fusing evidence from multi…

This paper analyzes RPKI specifications, demonstrating that vague or conflicting requirements in dozens of RFCs cause systemic vulnerabilities in real-world implementations, leading to 61 undocumented…

The paper reframes LLM reliability from an impossible universal problem to a manageable, local patch-based problem, showing that sufficient interventions can be found by focusing on recurring failure…

TeeDAO introduces a novel three-layer framework that autonomously organizes and manages multiple heterogeneous Trusted Execution Environments (TEEs) to provide robust, distributed-trust systems with h…

The paper proposes an attestation-aware promotion gate to mitigate supply-chain risks in LLM pipelines by cryptographically verifying and enforcing claims about training and release artifacts before d…

The paper introduces STRIKE, a multi-dimensional structured taxonomy designed to provide a comprehensive and unified framework for classifying the rapidly evolving complexity of modern cybercrimes.

ACRFence introduces a framework-agnostic mitigation to prevent semantic rollback attacks in LLM agents by recording irreversible tool effects and enforcing strict replay-or-fork semantics upon checkpo…