[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-31566":3,"doc-seo-31566":26},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"file_id":15,"file_url":16,"file_type":17,"file_size":18,"view_count":4,"is_deleted":4,"is_public":19,"is_downloadable":19,"audit_status":19,"page_count":20,"language":21,"language_code":22,"table_of_contents":23,"faqs":24,"seo_title":13,"seo_description":14,"update_tm":25,"read_time":11},31566,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","ThreatFinderAI Automated Threat Modeling for LLM System Integration","Rapid AI integration creates security concerns, especially for Large Language Models (LLMs) where threats such as spear phishing, code injection, and remote code execution can emerge. Conventional threat modeling methods struggle with AI-specific systems because they target traditional software and offer limited practical automation. ThreatFinderAI is an asset-centric framework with seven AI-aligned steps, transforming threat and control knowledge into a queryable graph for automated asset identification and threat elicitation, plus business impact quantification via analysis and expert estimates.","cbCaictPpvBPuE7D","https://ap.wps.com/l/cbCaictPpvBPuE7D","pdf",281212,1,3,"English","en","# Introduction\n# ThreatFinderAI\n## Architectural Overview\n## Threat Modeling Steps\n# Demonstration Scenario\n# Conclusion and Future Work","[{\"question\":\"What security threats does ThreatFinderAI focus on for LLM-based systems?\",\"answer\":\"It addresses LLM-related threats including spear phishing, code injections, and remote code execution, and highlights that similar issues can also appear across broader AI contexts such as machine learning, federated learning, and computer vision.\"},{\"question\":\"How does ThreatFinderAI automate threat modeling for AI systems?\",\"answer\":\"ThreatFinderAI provides a seven-step, AI-design-aligned workflow that converts AI threat and control knowledge into a queryable knowledge graph, enabling automated asset identification and threat elicitation through structured diagram inputs and stencil-based elicitation.\"},{\"question\":\"How are business impacts and threat impact quantification handled?\",\"answer\":\"It proposes business impact analysis using Monte Carlo simulations combined with expert estimates to quantify AI threat impacts and support strategic risk communication.\"}]",1779742864,{"code":4,"msg":27,"data":28},"ok",{"site_id":29,"language":22,"slug":30,"title":13,"keywords":31,"description":14,"schema_data":32,"social_meta":82,"head_meta":84,"extra_data":86,"updated_unix":25},105,"threatfinderai-automated-threat-modeling-for-llm-system-integration","",{"@graph":33,"@context":81},[34,50,64],{"@type":35,"itemListElement":36},"BreadcrumbList",[37,41,45,47],{"item":38,"name":39,"@type":40,"position":19},"https://docshare.wps.com","Home","ListItem",{"item":42,"name":43,"@type":40,"position":44},"https://docshare.wps.com/document/","Document",2,{"item":46,"name":12,"@type":40,"position":20},"https://docshare.wps.com/document/research-report/",{"item":48,"name":13,"@type":40,"position":49},"https://docshare.wps.com/document/threatfinderai-automated-threat-modeling-for-llm-system-integration/31566/",4,{"url":48,"name":13,"@type":51,"author":52,"headline":13,"publisher":54,"fileFormat":57,"description":14,"dateModified":58,"datePublished":58,"encodingFormat":57,"isAccessibleForFree":59,"interactionStatistic":60},"DigitalDocument",{"name":9,"@type":53},"Person",{"url":38,"name":55,"@type":56},"DocShare","Organization","application/pdf","2026-05-25",true,{"@type":61,"interactionType":62,"userInteractionCount":4},"InteractionCounter",{"@type":63},"ViewAction",{"@type":65,"mainEntity":66},"FAQPage",[67,73,77],{"name":68,"@type":69,"acceptedAnswer":70},"What security threats does ThreatFinderAI focus on for LLM-based systems?","Question",{"text":71,"@type":72},"It addresses LLM-related threats including spear phishing, code injections, and remote code execution, and highlights that similar issues can also appear across broader AI contexts such as machine learning, federated learning, and computer vision.","Answer",{"name":74,"@type":69,"acceptedAnswer":75},"How does ThreatFinderAI automate threat modeling for AI systems?",{"text":76,"@type":72},"ThreatFinderAI provides a seven-step, AI-design-aligned workflow that converts AI threat and control knowledge into a queryable knowledge graph, enabling automated asset identification and threat elicitation through structured diagram inputs and stencil-based elicitation.",{"name":78,"@type":69,"acceptedAnswer":79},"How are business impacts and threat impact quantification handled?",{"text":80,"@type":72},"It proposes business impact analysis using Monte Carlo simulations combined with expert estimates to quantify AI threat impacts and support strategic risk communication.","https://schema.org",{"og:url":48,"og:type":83,"og:title":13,"og:site_name":55,"og:description":14},"article",{"robots":85,"canonical":48},"index,follow",{"doc_id":7,"site_id":29}]