Skynet: Workflow-Level Anomaly Detection for Agentic AI via Semantic and Structural Modeling

Abstract:Agentic AI systems execute complex tasks through long-horizon workflows of planning, tool use, and multi-agent coordination. Task failures in these systems often originate from a single step, such as an injected prompt or a flawed plan, and are then amplified through downstream dependencies as the corrupted step propagates across many subsequent agents and tool calls. Existing defenses either target a specific class of attacks or failures, or inspect individual prompts and steps in isolation. Both leave the global dependency structure of a workflow unexamined, and miss the inconsistencies that only emerge when the execution is viewed as a whole. We argue that anomaly detection for agentic AI must reason at the workflow level, where global execution structure exposes signals that local checks cannot see. We present Skynet, a principled workflow-level anomaly detection framework that turns observed multi-agent execution into directed workflow graphs and scores them against learned benign behavior. Skynet jointly models the semantic execution context and the structural organization of inter-agent delegation, tool invocation, and data-flow dependencies, and trains only on benign workflows. Because training never sees attacks or failures, this design naturally extends to zero-day detection: any execution that violates benign workflow regularities surfaces as off-manifold geometry under a single decision rule. We evaluate Skynet on three public agentic safety and failure benchmarks. It sustains high recall together with a sub-1% false positive rate, with per-workflow and per-step latencies low enough for online monitoring of agentic AI runtimes.

10 pages, 4 figures. Accepted by ACM MobiHoc 2026

Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)

K.6.5; C.2.4; I.2.6; I.2.11

arXiv:2609.06835 [cs.CR]

(or arXiv:2609.06835v1 [cs.CR] for this version)

https://doi.org/10.48550/arXiv.2609.06835 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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