[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-31678":3,"doc-seo-31678":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},31678,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1779183583414876462",6,"Technology","Prompt Injection in Large Language Model Exploitation: A Security Perspective","Rapid advances in AI require strong security for open-source large language models to preserve reliability and trustworthiness. The paper proposes an evaluation framework built around prompt-injection testing, using generators, probes, detectors, and assessment methods to surface vulnerabilities and feasible attack paths. It supports security testing, penetration testing, compliance checks, and continuous monitoring, enabling organizations, researchers, and developers to measure susceptibility, identify weak points, and strengthen cybersecurity for safer AI systems.","cbCaikzVRcKExTfH","https://ap.wps.com/l/cbCaikzVRcKExTfH","pdf",729729,1,8,"English","en","# Introduction\n## Security challenges for open-source LLMs\n## Selected models and methodology\n## Dataset and evaluation approach\n## Tools, probes, and threat simulation\n# Related Works","[{\"question\":\"What security risk does the paper focus on for large language models?\",\"answer\":\"The paper centers on prompt injection, where attackers manipulate a model’s responses to trigger unsafe or unintended behavior. It also discusses related risks such as malicious content generation and weaknesses that can enable cyberattacks.\"},{\"question\":\"How does the proposed framework evaluate LLM security?\",\"answer\":\"It uses a structured process including generators, probes, detectors, and evaluation methods. The approach relies on prompt-injection scenarios and dynamic probing combined with empirical measurement such as F1-score and recall.\"},{\"question\":\"What tools and techniques are used to test model weaknesses?\",\"answer\":\"The paper mentions advanced probing tools like Garak and supplements them with custom probes and pseudocode-based scanners. These are designed to detect LLM-specific threats that general security tools may miss.\"}]",1779915635,20,{"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,"prompt-injection-in-large-language-model-exploitation-a-security-perspective","",{"@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/technology/",3,{"item":50,"name":13,"@type":41,"position":51},"https://docshare.wps.com/document/prompt-injection-in-large-language-model-exploitation-a-security-perspective/31678/",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","2026-05-27",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 security risk does the paper focus on for large language models?","Question",{"text":73,"@type":74},"The paper centers on prompt injection, where attackers manipulate a model’s responses to trigger unsafe or unintended behavior. It also discusses related risks such as malicious content generation and weaknesses that can enable cyberattacks.","Answer",{"name":76,"@type":71,"acceptedAnswer":77},"How does the proposed framework evaluate LLM security?",{"text":78,"@type":74},"It uses a structured process including generators, probes, detectors, and evaluation methods. The approach relies on prompt-injection scenarios and dynamic probing combined with empirical measurement such as F1-score and recall.",{"name":80,"@type":71,"acceptedAnswer":81},"What tools and techniques are used to test model weaknesses?",{"text":82,"@type":74},"The paper mentions advanced probing tools like Garak and supplements them with custom probes and pseudocode-based scanners. These are designed to detect LLM-specific threats that general security tools may miss.","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}]