arXiv AI By Marcos Lima Romero, Ricardo Suyama

When LLM Agents See a Conflict but Still Proceed: Cross-Domain Dependencies in Network Slicing

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arXiv AI
Sep 11

Can AI Agents Deliver Verifiable Network-Wide Outcomes Across Authority Boundaries?

The paper introduces EvidenceNet, a runtime assurance layer designed to verify that coordinated AI agent operations achieve an operator’s intended network-wide outcomes across multiple administrative domains. EvidenceNet collects post-change observations from the required authority scopes, checks their freshness and validity, and uses a verifier agent to assess observation content. Experiments on live routing networks demonstrate that this approach can detect successful outcomes that configuration-action logs alone miss, and it rejects completions when observations are sourced incorrectly, substituted, or stale.

By Tianzhu Zhang, Chih-Kai Huang, Meikang Qiu
arXiv Machine Learning
Oct 8

Restricting the Model, Missing the System: Measurement and Accountability in Offensive AI Governance

arXiv:2605.09504v3 Announce Type: replace-cross Abstract: We show that the instruments used to measure AI offensive capability fail in two ways: (i) they overstate harm, and (ii) they credit the mode...

By Michael Alexander Riegler, Finn Schwall, Annika Willoch Olstad, Birk Sebastian Frostelid Torpmann-Hagen, Sushant Gautam, Klas H. Pettersen, Inga Str\"umke
arXiv AI
Aug 28

Five Primitives for Governing Autonomous AI Agents at Runtime

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.

By Jiten Oswal, John Cadeddu
arXiv AI
Oct 5

Beyond Predefined Sinks: Security-Aware Dependency Analysis for LLM Agents

The paper introduces AgentSecGraph, a static analysis framework that builds a Security-Aware Agent Dependency Graph (Security-ADG) for each security-sensitive operation in large language model (LLM) agents. It enriches operation identity with agent relevance, source and dependency evidence, trust-boundary context, guard evidence, and external-effect semantics. The authors also present AgentSecBench, a corpus of 67 real-world LLM-agent repositories, and demonstrate that their analyzer identifies thousands of operation candidates, recovers substantial dependency and guard evidence, and distinguishes guarded behaviors from vulnerabilities with high accuracy.

By Hang Cui