The paper introduces FP-Agent, a classifier that demonstrates that while browser fingerprints are poor discriminators for AI browsing agents, behavioral fingerprints (like typing and scrolling pattern…

AgentWall is a runtime safety layer that intercepts and evaluates all proposed actions from local AI agents against a declarative policy, ensuring safety before execution.

The paper defines AI Identity as the correspondence between an agent's declared state and its observed behavior, concluding that current infrastructure and standards are fundamentally inadequate for g…

This paper systematically analyzes security risks in cloud-hosted, tool-enabled AI agents, concluding that most risks stem from over-privileged tools and capability-intent mismatches rather than novel…

This paper demonstrates that using a communication algorithm (CommFormer) with heterogeneous agents significantly improves the speed and performance of multi-agent reinforcement learning for autonomou…

The paper proposes and evaluates symbolic guardrails as a practical method to provide strong, verifiable safety and security guarantees for domain-specific AI agents without compromising their utility…

The paper introduces Hyperparam, a set of lightweight JavaScript libraries designed to enable direct, model-aware querying of unstructured data (like agent traces) within client-side AI applications.

This paper proposes a new approach for cryptographically verifiable agent authorization, formalizing it as a relation that binds an agent principal, a request, an execution context, and policy satisfa…

KYA introduces a framework-agnostic trust and governance layer for autonomous systems that ensures actions are authorized, policy-conforming, and verifiable through a combination of novel primitives.

The paper proposes Multi-Agent Computer Use (MACU) systems, which significantly improve performance on complex, long-horizon tasks by enabling parallel execution and dynamic task decomposition compare…

The paper introduces the Agent Name Service (ANS), a DNS-inspired trust layer implemented in Kubernetes, to provide secure discovery, identity, and governance for autonomous AI agents.

The paper introduces AgentDoG 1.5, a lightweight and scalable alignment framework that significantly improves AI agent safety and security for complex open-world agent deployments.

The paper introduces AgentDoG 1.5, a lightweight and scalable alignment framework that significantly improves AI agent safety and security for complex, open-world agentic scenarios.

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.

PocketAgents introduces a manifest-driven framework for autonomous defense agents, enabling measurable and attributable LLM-driven security responses by strictly controlling agent actions and telemetr…

The paper introduces GAAP, an execution environment that deterministically guarantees the confidentiality of private user data by enforcing user-defined permission specifications on AI agents, even ag…

The paper introduces AIBuildAI-2, a knowledge-enhanced agent that significantly improves the automatic building of AI models by integrating an external, evolving knowledge system, achieving state-of-t…

This paper conducts the first real-world safety evaluation of the personal AI agent OpenClaw, demonstrating that its broad system access creates inherent vulnerabilities that significantly increase th…

This tutorial explores advances and challenges in deploying large language model-based agentic systems across industries, with a focus on reasoning and planning, multi-agent coordination, and evaluati…