Token-Flow Firewall: Semantic Runtime Auditing for Persistent AI Agents
Persistent AI agents extend large language models (LLMs) beyond single-turn interaction into long-lived software systems. Unlike traditional chat assistants, unsafe content in these agents can propagate through persistent state, reusable skills, and tool-mediated interactions, creating a substantially larger semantic attack surface. We observe that most security-critical interactions in such agents are transmitted through natural-language token flows, including memory updates, tool arguments, retrieved files, and inter-component communications. This observation enables a new security formulation: unsafe behavior can be intercepted as risky semantic flows before reaching privileged runtime...
| Authors | Puji Wang, Yingchen Zhang, Ruqing Zhang et al. |
|---|---|
| Primary category | cs.CR |
| Published | 9 July 2026 |
| Last updated at source | 9 July 2026 |
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