AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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DreamGuard is proposed, a proactive guardrail for LLM agents built around a risk-aware world model that maintains a compact recurrent latent state over the trajectory and predicts future latent states from which DreamGuard derives immediate-hazard and prefix-risk evidence.
This work develops a four-part, intent-oriented taxonomy that organizes multi-turn jailbreaks by adversarial intent structure and finds that effectiveness is driven by how deliberately intent is organized across turns rather than by context length or query count.
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