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© 2026 IJSREM – International Journal of Scientific Research in Engineering and Management. This is an open-access journal. Articles are published under the Creative Commons Attribution (CC BY 4.0) License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM62388

A Multi-Layered Detection and Prevention Framework for Prompt Injection Attacks in AI Systems

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM62388

Volume:

10 |

5 |

May 2026

Pages:

1-4

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM62388

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

Ayushi Verma

Ayushi Verma

Information technology

Noida Institute of Engineering and Technology

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Prateek Mathur

Information technology

Noida Institute of Engineering and Technology

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Abstract

Artificial Intelligence (AI) systems, particularly Large Language Models (LLMs), are widely deployed in real-world applications such as healthcare, finance, and automation. However, these systems are vulnerable to prompt injection attacks, where malicious inputs manipulate model behaviour and bypass safety constraints. This paper presents a comprehensive analysis of prompt injection threats and proposes a multi-layered framework for detection and prevention. The framework integrates input validation, context isolation, and output filtering mechanisms to enhance system security. Experimental analysis shows improved robustness against both direct and indirect attacks. The proposed approach contributes toward building secure, reliable, and trustworthy AI systems.

KEYWORDS

Keywords—AI Security Prompt Injection LLM Cybersecurity Adversarial Attacks NLP Security.

Cite This Article

Ayushi Verma, Prateek Mathur, "A Multi-Layered Detection and Prevention Framework for Prompt Injection Attacks in AI Systems," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 5, pp. 1-4, May 2026. https://doi.org/10.55041/IJSREM62388

Full Article/Conclusion

This research presents a robust framework for detecting and preventing prompt injection attacks in AI systems. By integrating multiple security layers, the system enhances reliability and trustworthiness. The results demonstrate that multi-layered defence significantly improves protection compared to traditional methods.

References

[1] F. Perez and I. Ribeiro, “Prompt Injection Attacks Against LLM Applications,” arXiv, 2023. [2] OWASP, “Top 10 Risks for LLM Applications,” 2024. [3] D. Kang et al., “Adversarial Attacks on LLMs,” IEEE Access, 2024. [4] X. Chen et al., “Security in NLP Systems,” IEEE, 2024. [5] OpenAI, “GPT-4 Technical Report,” 2023. [6] Y. Liu et al., “Jailbreaking LLMs,” arXiv, 2023. [7] S. Shen et al., “Prompt Engineering Security,” ACM, 2024. [8] A. Zou et al., “Adversarial Attacks on Language Models,” arXiv, 2023. [9] N. Carlini et al., “Extracting Training Data from LLMs,” USENIX, 2023. [10] J. Wei et al., “Emergent Abilities of LLMs,” arXiv, 2022.

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Publication Date

5/11/2026

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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5

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Conflict of Interest

The authors declare no conflict of interest related to this research work.

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