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The University of Texas at Arlington
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Poisoned data can alter summarization behavior without triggering alarms, but our defense framework detects and restores model integrity with impressive precision.
Jointly verifying user intent and response harm can reduce attack success rates to just 4.1%, setting a new standard for LLM safety.
