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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM62388
Volume:
10 |
5 |
May 2026
Pages:
1-4
Publication Frequency:
Monthly (12 issues per year)
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.

Information technology
Noida Institute of Engineering and Technology
Information technology
Noida Institute of Engineering and Technology
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
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.
[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.
Publication Date
5/11/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
Funding Information
No funding information available for this research.
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