PEO optimizes original prompt embeddings continuously over adaptive rounds to jailbreak aligned LLMs, preserving the exact visible prompt text and outperforming discrete suffix, appended embedding, and search-based white-box attacks on harmful-behavior benchmarks.
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SPOT projects prompts to a tau-safe set via total variation to cut inappropriate content 14-44% relative to baselines while preserving benign prompt behavior in frozen T2I models.
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Adaptive Prompt Embedding Optimization for LLM Jailbreaking
PEO optimizes original prompt embeddings continuously over adaptive rounds to jailbreak aligned LLMs, preserving the exact visible prompt text and outperforming discrete suffix, appended embedding, and search-based white-box attacks on harmful-behavior benchmarks.
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SPOT: Selective Prompt Projection via Total Variation for Inference-Only Safe Text-to-Image Generation
SPOT projects prompts to a tau-safe set via total variation to cut inappropriate content 14-44% relative to baselines while preserving benign prompt behavior in frozen T2I models.