arXiv:2608. 16402v1 Announce Type: new Abstract: Large language model-based agentic frameworks primarily optimize capability: whether an agent can reason, retrieve information, call tools, delegate work, and complete a goal.
The paper proposes five runtime primitives—discovery, identity, governance, attestation, and supply chain—to manage autonomous AI agents in enterprise settings. It argues that traditional control models fail because agents are transient, model-driven, and self‑discoverable, making runtime governance essential. The authors detail an implementation that mediates agent actions against policy, authorizes them via a per‑tenant vocabulary, and records them in a verifiable ledger, noting the associated operational costs and partial deployment status.
arXiv:2607. 13718v1 Announce Type: cross Abstract: As AI agents gain prevalance, users are increasingly exposed to the risks such systems entail.
arXiv:2606. 04104v1 Announce Type: cross Abstract: Agent systems execute through runtimes with very different control points: local coding tools, framework SDKs, managed agent platforms, API gateways, and observer-only integrations.
arXiv:2607. 21325v2 Announce Type: replace-cross Abstract: Autonomous AI agents increasingly execute actions, invoke tools, and operate on protected resources with limited human oversight.
arXiv:2607. 21325v1 Announce Type: cross Abstract: Autonomous AI agents increasingly execute actions, invoke tools, and operate on protected resources with limited human oversight.
arXiv:2606. 19464v1 Announce Type: new Abstract: Autonomous agentic AI systems driven by Large Language Models (LLMs) introduce a new class of security, privacy, and compliance challenges: an agent that can invoke tools, manipulate data, install software, and coordinate with peer agents across organizational boundaries must be constrained not just by authentication and access control, but by the full structure of enterprise governance.
arXiv:2606. 11632v1 Announce Type: cross Abstract: Agentic infrastructure introduces a critical control-plane authorization problem: non-deterministic reasoning systems can propose high-stakes mutations to production resources, yet existing security mechanisms -- such as identity and access management (IAM), policy engines, consensus protocols, and audit logs -- either enforce static, context-unaware permissions or merely record actions post-execution.
The paper introduces Verifiable Agent Insurance (VAI), a framework that lets AI agent providers commit to policy compliance through smart contracts and escrow funds on a blockchain. When a user detects a policy violation, a verifier reviews execution traces logged by the agent harness; if the violation is confirmed, the escrow is paid to the user. Experiments on three agent benchmarks show VAI detects 42.2% of violations, rising to 83.0% when agents emit explicit structured records.
The paper introduces Aegis, a runtime governance system for agentic AI that treats model outputs as action proposals and mediates them through a trusted decision layer before tool execution. Aegis evaluates proposals against active policy, resolves provenance server‑side, fails closed under uncertainty, and routes selected cases through a Senate‑style settlement process. In a sandbox evaluation across 6,300 rows, Aegis prevented all governed mock‑tool applications and risky side‑effect completions, preserving provenance and quorum evidence for all settled cases.
arXiv:2608. 06353v1 Announce Type: cross Abstract: We give a formal mechanism design model for the continuous participatory governance of a deployed AI agent.
The paper examines the security challenges of delegating authority to autonomous LLM agents that act on users’ behalf. It introduces a threat model with four adversaries and eight security requirements, demonstrates that current frameworks (LangGraph, CrewAI, AutoGen, MCP) fail to meet these standards, and presents an authorization broker that blocks all identified threats with minimal overhead. The broker is shown to resist numerous attacks and limits compromised sub‑agents to their delegated tasks, and its principles are implemented in VotalAI’s LLM Shield.