Adversarial Attack Detection Framework for LLMs

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Explore the crucial need to address adversarial attacks in Large Language Models (LLMs) impacting workflows through deterministic alignment exploitation. Learn about challenges in defending against rapidly evolving threats, the limitations of existing defense mechanisms, and a proposed detection framework utilizing cardinality-based logic and anomaly scoring. Performance metrics validate efficacy in real-world scenarios, highlighting advancements like dynamic honeytokens and proactive...