Skynet: Workflow-Level Anomaly Detection for Agentic AI via Semantic and Structural Modeling
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...
| Authors | Chaoyu Zhang, Hexuan Yu, Heng Jin et al. |
|---|---|
| Primary category | cs.CR |
| Published | 6 September 2026 |
| Last updated at source | 12 September 2026 |
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