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P RO A CT represents an orthogonal defense strategy that serves as an additional guardrail to enhance LLM safety against the most effective attacks.
GPT-Red, an automated red-teaming agent that is trained to discover novel prompt injection attacks against frontier LLMs, is introduced, a scalable self-play algorithm where the model is tasked with attacking a diverse population of simultaneously-trained defender agents.
This work instantiates Feedback-Augmented Self-Distillation (FA-SD), a self-distillation algorithm for agentic search that leverages successful demonstrations as privileged information and identifies that models can rely on recurring reasoning-and-search output templates, producing trajectories that appear diverse but...
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