AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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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.
This paper presents a proactive defense framework for securing LLMs against evolving multi-turn adversarial attacks that combines disruption, misdirection, and adaptation across successive interaction turns and employs a cooperative multi-agent architecture.
Safety Harness Evolution (SHE) is proposed, a framework that learns evolving safe boundaries from rollout trajectories and introduces an attribution-guided evolution loop that converts trajectory failures into structured diagnoses, learns artifact-specific boundary refinements, and selects evolved harnesses through safety-utility validation.
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