Mitigating the OWASP Top 10 For Large Language Models Applications using Intelligent Agents
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866912848834199552 |
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| author | Fasha, Mohammad Rub, Faisal Abul Matar, Nasim Sowan, Bilal Khaldy, Mohammad Al |
| author_facet | Fasha, Mohammad Rub, Faisal Abul Matar, Nasim Sowan, Bilal Khaldy, Mohammad Al |
| contents | Large Language Models (LLMs) have emerged as a transformative and disruptive technology, enabling a wide range of applications in natural language processing, machine translation, and beyond. However, this widespread integration of LLMs also raised several security concerns highlighted by the Open Web Application Security Project (OWASP), which has identified the top 10 security vulnerabilities inherent in LLM applications. Addressing these vulnerabilities is crucial, given the increasing reliance on LLMs and the potential threats to data integrity, confidentiality, and service availability. This paper presents a framework designed to mitigate the security risks outlined in the OWASP Top 10. Our proposed model leverages LLM-enabled intelligent agents, offering a new approach to proactively identify, assess, and counteract security threats in real-time. The proposed framework serves as an initial blueprint for future research and development, aiming to enhance the security measures of LLMs and protect against emerging threats in this rapidly evolving landscape. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_18105 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Mitigating the OWASP Top 10 For Large Language Models Applications using Intelligent Agents Fasha, Mohammad Rub, Faisal Abul Matar, Nasim Sowan, Bilal Khaldy, Mohammad Al Cryptography and Security Artificial Intelligence Large Language Models (LLMs) have emerged as a transformative and disruptive technology, enabling a wide range of applications in natural language processing, machine translation, and beyond. However, this widespread integration of LLMs also raised several security concerns highlighted by the Open Web Application Security Project (OWASP), which has identified the top 10 security vulnerabilities inherent in LLM applications. Addressing these vulnerabilities is crucial, given the increasing reliance on LLMs and the potential threats to data integrity, confidentiality, and service availability. This paper presents a framework designed to mitigate the security risks outlined in the OWASP Top 10. Our proposed model leverages LLM-enabled intelligent agents, offering a new approach to proactively identify, assess, and counteract security threats in real-time. The proposed framework serves as an initial blueprint for future research and development, aiming to enhance the security measures of LLMs and protect against emerging threats in this rapidly evolving landscape. |
| title | Mitigating the OWASP Top 10 For Large Language Models Applications using Intelligent Agents |
| topic | Cryptography and Security Artificial Intelligence |
| url | https://arxiv.org/abs/2601.18105 |