ChannelGuard: From Borrowed Safety to Application-Controlled Defenses in Multi-Agent LLMs
AI assistants increasingly work as teams, where one model makes a plan, others search the web, use tools, or read stored information, and another writes the final answer. This creates new security risks because harmful instructions can enter through information passed between models, not just through the user's message. Yet safety tests usually report only whether an attack succeeded, without showing what actually stopped it. We show that systems with similar safety scores can depend on very different protections. Across five agent models, attacks were stopped by a cloud provider's filter, by the model itself, or by inconsistent model behavior. This matters because protection that comes f...
| Authors | Elias Hossain, Md Mehedi Hasan Nipu, Fatema Tuj Johora Faria et al. |
|---|---|
| Primary category | cs.CR |
| Published | 20 July 2026 |
| Last updated at source | 11 October 2026 |
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