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 systematizes the interaction between autonomous AI agents and blockchain platforms using a bidirectional trust framework, identifying significant gaps in current standards and proposing a ta…

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…

The paper introduces ACIArena, a unified and comprehensive evaluation framework designed to systematically test the robustness of Multi-Agent Systems against complex Agent Cascading Injection attacks.

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 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…

This survey provides a comprehensive, practical guide to ensuring the trustworthiness of complex, autonomous agentic AI systems by focusing on safety, robustness, privacy, and system security.

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…

The paper introduces AgentWard, a lifecycle-oriented, defense-in-depth architecture designed to systematically secure autonomous AI agents by protecting them across all stages of their operation.

This survey analyzes the unique security threats posed by complex, multi-agent AI systems and proposes Confidential Computing (CC) using Trusted Execution Environments (TEEs) as a hardware-rooted defe…

The paper proposes a taxonomy of 20 hardware-level governance mechanisms for AI compute, finding that the most critical mechanisms needed for international treaty verification are currently the least…

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

The paper proposes Arbiter-K, a Governance-First execution architecture that treats LLMs as probabilistic units encapsulated by a deterministic kernel, significantly improving the security and reliabi…

The paper proposes replacing individual agent autonomy with a structured 'social contract' and institutional Separation of Power (SoP) to mitigate systemic failures and deceptive behavior in multi-age…

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 ADR, a novel, production-proven detection system that provides high-fidelity security monitoring for AI agents operating via the Model Context Protocol, significantly outperformin…

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 analyzes the failure modes of current AI containment methods when the agent itself is the adversary, deriving five necessary architectural requirements for durable safety.

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 proposes a compositional governance framework to provide richer, dynamic authorization semantics necessary for governing autonomous agentic AI systems, moving beyond traditional static IAM m…