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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.
C$^{3}$T (Counterfactual Causal Conversation Transformer), a thread-structured temporal model that jointly predicts node sentiment and shifts, learns sparse ancestor attribution, and supports counterfactual queries by forcing conversational intervention embeddings on or off to estimate potential outcomes, is proposed.
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.
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