[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-31804":3,"doc-seo-31804":27},{"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":26},31804,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","PRISM Prompt Red-teaming and Injection Simulation for Models A Scalable Framework for Evaluating LLM Robustness Against Malicious Prompts","Adoption of Large Language Models introduces security weaknesses, especially prompt injection attacks that hijack model intent through malicious inputs and cause harmful, unintended, or privacy-compromising outputs. Traditional rule-based detection cannot track the creativity and diversity of evolving attacks. PRISM provides a scalable, systematic framework to evaluate LLM robustness using distributed processing via Hadoop MapReduce and flexible inference with Ollama, across many prompt variations and temperature settings. Similarity-based measures are computed over a corpus of known attacks to quantify performance, reveal successful vectors, and support automated red-teaming and security auditing.","cbCaiv2PvnwdYzKn","https://ap.wps.com/l/cbCaiv2PvnwdYzKn","pdf",1704006,1,6,"English","en","# Introduction\n## Prompt Injection Attacks and Challenges\n## PRISM Framework Overview\n# Related Context: Big Data Distributed Processing","[{\"question\":\"What is a prompt injection attack and why is it dangerous for LLM deployments?\",\"answer\":\"A prompt injection attack uses malicious user input to override the LLM’s original intent, leading to unintended actions such as generating inappropriate content, leaking sensitive information, or executing harmful code.\"},{\"question\":\"Why do traditional prompt injection defenses struggle against adaptive adversarial prompts?\",\"answer\":\"Static filtering and rule-based approaches often fail to keep up with the creativity and adaptiveness of adversarial prompts, making defense coverage incomplete.\"},{\"question\":\"How does PRISM evaluate LLM robustness against malicious prompts at scale?\",\"answer\":\"PRISM uses Hadoop MapReduce for distributed processing and Ollama for flexible LLM inference to automatically generate and assess outputs across thousands of prompt variations and temperature settings.\"}]",1780174848,15,{"code":4,"msg":28,"data":29},"ok",{"site_id":30,"language":22,"slug":31,"title":13,"keywords":32,"description":14,"schema_data":33,"social_meta":84,"head_meta":86,"extra_data":88,"updated_unix":25},105,"prism-prompt-red-teaming-and-injection-simulation-for-models-a-scalable-framework-for-evaluating-llm-robustness-against-malicious-prompts","",{"@graph":34,"@context":83},[35,52,66],{"@type":36,"itemListElement":37},"BreadcrumbList",[38,42,46,49],{"item":39,"name":40,"@type":41,"position":19},"https://docshare.wps.com","Home","ListItem",{"item":43,"name":44,"@type":41,"position":45},"https://docshare.wps.com/document/","Document",2,{"item":47,"name":12,"@type":41,"position":48},"https://docshare.wps.com/document/research-report/",3,{"item":50,"name":13,"@type":41,"position":51},"https://docshare.wps.com/document/prism-prompt-red-teaming-and-injection-simulation-for-models-a-scalable-framework-for-evaluating-llm-robustness-against-malicious-prompts/31804/",4,{"url":50,"name":13,"@type":53,"author":54,"headline":13,"publisher":56,"fileFormat":59,"description":14,"dateModified":60,"datePublished":60,"encodingFormat":59,"isAccessibleForFree":61,"interactionStatistic":62},"DigitalDocument",{"name":9,"@type":55},"Person",{"url":39,"name":57,"@type":58},"DocShare","Organization","application/pdf","2026-05-30",true,{"@type":63,"interactionType":64,"userInteractionCount":4},"InteractionCounter",{"@type":65},"ViewAction",{"@type":67,"mainEntity":68},"FAQPage",[69,75,79],{"name":70,"@type":71,"acceptedAnswer":72},"What is a prompt injection attack and why is it dangerous for LLM deployments?","Question",{"text":73,"@type":74},"A prompt injection attack uses malicious user input to override the LLM’s original intent, leading to unintended actions such as generating inappropriate content, leaking sensitive information, or executing harmful code.","Answer",{"name":76,"@type":71,"acceptedAnswer":77},"Why do traditional prompt injection defenses struggle against adaptive adversarial prompts?",{"text":78,"@type":74},"Static filtering and rule-based approaches often fail to keep up with the creativity and adaptiveness of adversarial prompts, making defense coverage incomplete.",{"name":80,"@type":71,"acceptedAnswer":81},"How does PRISM evaluate LLM robustness against malicious prompts at scale?",{"text":82,"@type":74},"PRISM uses Hadoop MapReduce for distributed processing and Ollama for flexible LLM inference to automatically generate and assess outputs across thousands of prompt variations and temperature settings.","https://schema.org",{"og:url":50,"og:type":85,"og:title":13,"og:site_name":57,"og:description":14},"article",{"robots":87,"canonical":50},"index,follow",{"doc_id":7,"site_id":30}]