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Domain-specific RAG improved evidence grounding and response quality for cancer registry questions while enabling citation-supported assistance across complexity levels, and demonstrates the potential of human-centered, citation-grounded AI to support cancer registrars while preserving human oversight for final coding...
This thesis proposes a unified two-layer approach to enhancing the trustworthiness and security of LLM-enabled systems and introduces the Adaptive LLM Threat Response (ALTR) framework, which integrates behavioral anomaly detection, context-aware prompt classification, and temporal threat memory to identify and mitigate...
The fast deployment of large language models (LLMs) in critical-domain applications such as financial systems has introduced new types of security vulnerabilities (e.g., adversarial prompt injections, semantic obfuscation, and multi-stage social engineering attacks) that conventional countermeasures cannot typically ha...
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