LLMs generated 615 vulnerable code snippets aligned with CAPEC and CWE frameworks across three languages, with 0.98 cosine similarity between model outputs.
Al Azher and H
2 Pith papers cite this work. Polarity classification is still indexing.
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CASE is a novel agentic AI system that proactively interviews scam victims using LLMs to collect detailed intelligence, which is then structured for use in scam prevention, resulting in a 21% increase in enforcements on Google Pay India.
citing papers explorer
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From Theory to Practice: Code Generation Using LLMs for CAPEC and CWE Frameworks
LLMs generated 615 vulnerable code snippets aligned with CAPEC and CWE frameworks across three languages, with 0.98 cosine similarity between model outputs.
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CASE: An Agentic AI Framework for Enhancing Scam Intelligence in Digital Payments
CASE is a novel agentic AI system that proactively interviews scam victims using LLMs to collect detailed intelligence, which is then structured for use in scam prevention, resulting in a 21% increase in enforcements on Google Pay India.