{
  "@context": {
    "dcterms": "http://purl.org/dc/terms/",
    "dcmitype": "http://purl.org/dc/dcmitype/"
  },
  "dcterms:identifier": [
    "tag:aim-pro.eu,2026:oer/31c13855f104",
    "https://zenodo.org/records/13852602",
    "doi:10.35940/ijrte.C8142.13030924",
    "zenodo-record:13852602"
  ],
  "dcterms:title": [
    "Security Considerations for Large Language Model Use: Implementation Research in Securing LLM-Integrated Applications"
  ],
  "dcterms:type": [
    {
      "@id": "dcmitype:Text"
    },
    "Journal article"
  ],
  "dcterms:creator": [
    "Nikhil Pesati"
  ],
  "dcterms:description": [
    "Abstract: Large Language Models (LLMs) are rapidly being adopted in various applications due to their natural language capabilities that enable user interaction using human language. As system designers, developers, and users embrace generative artificial intelligence and large language models in various applications, they need to understand the significant security risks associated with them. The paper describes a typical LLM-integrated application architecture and identifies multiple security risks to address while building these applications. In addition, the paper provides guidance on potential mitigations to consider in this rapidly evolving space to help protect systems and users from potential attack vectors. This paper presents the common real-world application patterns of LLMs trending today. It also provides a background on generative artificial intelligence and related fields."
  ],
  "dcterms:subject": [
    "Large Language Models",
    "Security",
    "Copilot",
    "OWASP"
  ],
  "dcterms:language": [
    "en"
  ],
  "dcterms:license": [
    {
      "@id": "https://creativecommons.org/licenses/by/4.0/"
    }
  ],
  "dcterms:rights": [
    "CC-BY-4.0 — assessed as OPEN by the harvester's licence gate. Conditions: attribution required. open, permissive"
  ],
  "dcterms:accessRights": [
    "open"
  ],
  "dcterms:format": [
    "application/pdf",
    "text/markdown"
  ],
  "dcterms:extent": [
    "608576 bytes (original)",
    "48092 bytes (extracted text)"
  ],
  "dcterms:issued": [
    "2024-09-30"
  ],
  "dcterms:modified": [
    "2024-09-28"
  ],
  "dcterms:created": [
    "2024-09-28"
  ],
  "dcterms:bibliographicCitation": [
    "International Journal of Recent Technology and Engineering (IJRTE), vol. 13(3), pp. 19-27"
  ],
  "dcterms:provenance": [
    "Retrieved from Zenodo on 2026-10-09 in response to the search string “(\"artificial intelligence\" OR \"machine learning\" OR \"generative AI\" OR \"deep learning\" OR \"reinforcement learning\" OR \"large language model\" OR \"AI\") AND (\"AI concepts\" OR \"types of AI\" OR \"AI fundamentals\" OR \"recognizing AI\" OR \"recognising AI\" OR \"general versus narrow AI\" OR \"narrow AI\" OR \"general AI\" OR \"machine intelligence\" OR \"AI strengths and weaknesses\" OR \"traditional software\" OR \"rule-based systems\" OR \"introduction to AI\" OR \"introduction to artificial intelligence\" OR \"artificial intelligence introduction\" OR \"AI primer\" OR \"foundations of artificial intelligence\" OR \"overview of AI\" OR \"understanding AI\" OR \"history of AI\" OR \"AI essentials\" OR \"AI terminology\" OR \"metaphors for AI\" OR \"AI fundamental concepts\" OR \"AI key concepts\" OR \"philosophy of AI\" OR \"critical AI literacy\")”. Zenodo served the resource and is not asserted to be its publisher or author.",
    "Text extracted from pdf to Markdown by pdf-inspector; the original is retained unchanged beside it."
  ]
}