The paper introduces MolTrust, a production-deployed trust infrastructure built on W3C standards (VCs and DIDs) that provides a verifiable, multi-layered authorization framework for autonomous AI agen…

The paper introduces an efficient, lightweight LLM framework for smart contract auditing that decouples the audit process into multiple components, achieving high accuracy while significantly reducing…

The paper introduces SIGIL, a novel framework that cryptographically seals the entire lifecycle of LLM skills, ensuring verifiable integrity from publication through runtime execution to prevent suppl…

The paper introduces KBF, a low-cost black-box auditing protocol that fingerprints LLM APIs by analyzing stable numerical recall near the knowledge boundary, successfully detecting numerous model subs…

The paper introduces KBF, a novel black-box auditing protocol that fingerprints LLM APIs by analyzing stable numerical recall near the knowledge boundary, effectively detecting model substitutions and…

The paper introduces a comprehensive taxonomy and auditing framework to assess the collective coverage of existing LLM attack benchmarks, revealing significant and systematic gaps in current testing m…

enclawed is a configurable, hard-fork hardening framework for AI assistant gateways that enforces strict security controls, verifiable trust, and auditable connectivity for regulated environments.

The paper introduces a 'Privacy Guard' framework that simultaneously reduces operational costs and eliminates data leakage risks when using LLMs by optimizing prompts and routing queries to secure mod…

The paper proposes a canonical, end-to-end validation framework to ensure secure integration of Alternative Data Availability (AltDA) systems with Ethereum Layer 2s, demonstrating that L2 integration…

MemLineage introduces a novel, cryptographically-backed defense mechanism that enforces a chain-of-custody for LLM agent memory, preventing untrusted or poisoned state from justifying sensitive action…

This survey provides a comprehensive, structured taxonomy of split learning techniques for fine-tuning Large Language Models (LLMs), covering model optimization, system efficiency, and privacy preserv…

The Device Context Protocol (DCP) introduces a compact, safety-first communication standard designed to allow LLMs to reliably control resource-constrained physical microcontrollers, significantly imp…

The paper establishes a standardized security assessment framework and develops a multi-layered defensive system, demonstrating that systematic testing and external defenses are crucial for safe LLM d…

The paper proposes IntraShuffler, a novel privacy-preserving middleware defense that enables gradient shuffling in Heterogeneous Differential Privacy Federated Learning (HDP-FL) systems, significantly…

The paper proposes IntraShuffler, a novel privacy-preserving middleware defense that enables gradient shuffling in Heterogeneous Differential Privacy Federated Learning (HDP-FL) while maintaining the…

The paper introduces Distributed Sentinel, a zero-trust architecture that prevents Context-Fragmented Violations (CFVs) in multi-agent systems by propagating security state across departmental boundar…

OpenSOC-AI is a lightweight framework that uses parameter-efficient fine-tuning of a small LLM to automate threat classification and severity assessment from raw security logs, significantly improving…

The paper introduces HIDBench, a new benchmark for evaluating LLMs' ability to perform host-based intrusion detection using complex, noisy system logs, finding that model performance degrades signific…

The paper proposes Ablating Safety, a controlled protocol for removing safety alignment from language models, demonstrating that targeted de-alignment can significantly boost security performance whil…

AgenTEE is a system that enables the secure, confidential execution of complex LLM agent pipelines directly on edge devices by using isolated confidential virtual machines.