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Contextual drift in LLMs can be contained with over 99% effectiveness, drastically reducing the risk of adversarial manipulation in multi-turn interactions.
Achieving 95.9% detection accuracy in IoT intrusion detection while effectively handling zero-day threats and minimizing false positives is a game changer for cybersecurity.
LLMs can exhibit surprising ethical failures and progressive degradation under sustained adversarial pressure, even when passing standard single-round safety benchmarks.
Forget retraining: this SDN-IoT defense system uses LLMs to safely evolve reinforcement learning policies through interpretable policy updates, slashing catastrophic overloads by 80%.
