The paper proposes a two-timescale governance framework using a multi-agent LLM to safely update and guide RL agents for SDN-IoT defense, significantly improving performance and stability under advers…

The paper proposes a tool-mediated LLM architecture for autonomous cyber defense, formally proving its stability and demonstrating that it significantly reduces an attacker's expected payoff in real-w…

The paper develops a unified, cross-layer security framework for autonomous LLM agents operating in agentic commerce, identifying key attack vectors and proposing a layered defense architecture.

The paper proposes a declarative, autonomous, self-protecting framework for securing complex 5G/6G networks by leveraging a standardized security ontology and automated graph reasoning to neutralize l…

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 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 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…

The paper proposes the Layered Attack Surface Model (LASM), a structural taxonomy that maps security threats and defenses across the complex, multi-layered architecture of AI agents, revealing signifi…

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.

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…

MA-IDS proposes a Multi-Agent RAG framework that uses LLMs and a self-building Experience Library to achieve explainable and self-improving intrusion detection for resource-constrained IoT networks.

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…

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 MAGE, a novel defensive framework that uses a dedicated 'shadow memory' to proactively detect and mitigate long-horizon threats against LLM agents during complex, multi-step inter…

The paper proposes a novel attack paradigm demonstrating how compromising a single robot in an LLM-controlled multi-robot system can rapidly propagate malicious intent to cause coordinated unsafe acti…

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…

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…

This paper empirically demonstrates that the architectural design of multi-agent systems significantly impacts their security, finding that coordination mechanisms can introduce vulnerabilities greate…

This paper introduces Mobius Injection, a novel, lightweight attack that weaponizes autonomous LLM agents into zombie nodes to launch highly scalable AbO-DDoS attacks by exploiting a vulnerability cal…