Unknown Vulnerability Mining for Power Monitoring Systems Aided by Large Language Modeling

Unknown Vulnerability Mining for Power Monitoring Systems Aided by Large Language Modeling | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Unknown Vulnerability Mining for Power Monitoring Systems Aided by Large Language Modeling Manpo Li, Xuerui Yang, Xiaochen Yang, Shugui Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6780793/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract An exciting new direction for improving operational efficiency and decision-making is the use of large language models (LLMs) to contemporary power systems. Nevertheless, there may be unanticipated security risks associated with this move. Using LLMs to power networks may pose certain risks, which this paper examines. It stresses the need of doing research and developing remedies immediately. It is a challenging but vital job to secure large language models in a power monitoring context. Through the implementation of thorough security measures, the promotion of a security-conscious culture, and the continuous monitoring of new threats while technologies, we may maximize the benefits of LLMs while minimizing their hazards. It is our duty as information security experts to pioneer this new field and make sure that our security protocols adapt to the increasing sophistication of our AI systems. Security flaws in LLM that allow rapid injection attacks are among the most critical ones. These types of attacks take advantage of LLMs' fundamental features by deliberately feeding them data that will cause them to operate in an unexpected way or leak private information. Industries that deal with sensitive data are especially worried about the consequences of these vulnerabilities. Creating a comprehensive strategy for LLM security is essential for reducing these threats. The first step is to establish reliable procedures for training and selecting models. Many large language models (LLMs) have seen extensive usage with the introduction of commercially accessible systems like ChatGPT. This has piqued interest in semantic search, which is able to do searches that consider the meaning of words. Here, we built an LLM model using TinyLlama Chat 1.1. The LLM takes the processed packet data and the extracted context as input and outputs a user-friendly summary of the packet file. Through the use of machine learning models, the program provides a concise, well-organized, and straightforward overview of the network's operations. Power systems large language models security threats TinyLlama Chat 1.1 HAI Dataset Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6780793","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":472629225,"identity":"a04718c8-aa0c-4a0e-82ae-bb60a96ea112","order_by":0,"name":"Manpo Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIiWNgGAWjYBACgwMgskBCzv54M4hpwcDAQ0CLJViLgYQxw5ljCQwMCRKEtdhDtDAkNtzwMSBOi9nxs8cefDCwSGycwfPxMe8PCTl+ngOMHz7m4NFyJi/dcAbQYc3SvZuNeRIkjCV7G5glZ27Do+VAjpk0j4GEbJvM2W3SQC2JG84zsDHz4tFicP4NWAtjj0TOMyK13IDYojhDIocNouVsAyEtb8zBfjHgOWZsOCcN6Jeeg814/WJwPsfswYeKOjkD9uaHD97Y2ABDLPngh494tAABG7oAYwNe9di0jIJRMApGwShABQD6+k3TbhQkaAAAAABJRU5ErkJggg==","orcid":"","institution":"Northeast Branch of State Grid Corporation of China","correspondingAuthor":true,"prefix":"","firstName":"Manpo","middleName":"","lastName":"Li","suffix":""},{"id":472629226,"identity":"91dad5f0-cff1-4519-864e-aa397d650260","order_by":1,"name":"Xuerui Yang","email":"","orcid":"","institution":"Northeast Branch of State Grid Corporation of China","correspondingAuthor":false,"prefix":"","firstName":"Xuerui","middleName":"","lastName":"Yang","suffix":""},{"id":472629227,"identity":"9a36eef1-8739-44c4-8ea8-580977a9053d","order_by":2,"name":"Xiaochen Yang","email":"","orcid":"","institution":"Northeast Branch of State Grid Corporation of China","correspondingAuthor":false,"prefix":"","firstName":"Xiaochen","middleName":"","lastName":"Yang","suffix":""},{"id":472629228,"identity":"73cb53e6-d02b-46e6-a31e-6c05608fa69a","order_by":3,"name":"Shugui Zhang","email":"","orcid":"","institution":"Sichuan Energy Internet Research InstituteTsinghua University","correspondingAuthor":false,"prefix":"","firstName":"Shugui","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2025-05-30 04:08:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6780793/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6780793/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87644632,"identity":"065a7444-3eb8-46b5-948d-6d4944867084","added_by":"auto","created_at":"2025-07-26 17:01:31","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":408241,"visible":true,"origin":"","legend":"","description":"","filename":"CC034UnknownVulnerabilityMiningforPowerMonitoringSystemsAidedbyLargeLanguageModeling.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6780793/v1_covered_07d1471c-671f-4247-ac31-51ce4ebe6e35.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Unknown Vulnerability Mining for Power Monitoring Systems Aided by Large Language Modeling","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Power systems, large language models, security threats, TinyLlama Chat 1.1, HAI Dataset","lastPublishedDoi":"10.21203/rs.3.rs-6780793/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6780793/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAn exciting new direction for improving operational efficiency and decision-making is the use of large language models (LLMs) to contemporary power systems. 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