Detecting Prompt Injection Attacks Against Application Using Classifiers
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arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| author | Shaheer, Safwan Islam, G. M. Refatul Hamid, Mohammad Rafid Khan, Md. Abrar Faiaz Faruk, Md. Omar Nur, Yaseen |
| author_facet | Shaheer, Safwan Islam, G. M. Refatul Hamid, Mohammad Rafid Khan, Md. Abrar Faiaz Faruk, Md. Omar Nur, Yaseen |
| contents | Prompt injection attacks can compromise the security and stability of critical systems, from infrastructure to large web applications. This work curates and augments a prompt injection dataset based on the HackAPrompt Playground Submissions corpus and trains several classifiers, including LSTM, feed forward neural networks, Random Forest, and Naive Bayes, to detect malicious prompts in LLM integrated web applications. The proposed approach improves prompt injection detection and mitigation, helping protect targeted applications and systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_12583 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Detecting Prompt Injection Attacks Against Application Using Classifiers Shaheer, Safwan Islam, G. M. Refatul Hamid, Mohammad Rafid Khan, Md. Abrar Faiaz Faruk, Md. Omar Nur, Yaseen Cryptography and Security Artificial Intelligence D.4.6; I.2.7 Prompt injection attacks can compromise the security and stability of critical systems, from infrastructure to large web applications. This work curates and augments a prompt injection dataset based on the HackAPrompt Playground Submissions corpus and trains several classifiers, including LSTM, feed forward neural networks, Random Forest, and Naive Bayes, to detect malicious prompts in LLM integrated web applications. The proposed approach improves prompt injection detection and mitigation, helping protect targeted applications and systems. |
| title | Detecting Prompt Injection Attacks Against Application Using Classifiers |
| topic | Cryptography and Security Artificial Intelligence D.4.6; I.2.7 |
| url | https://arxiv.org/abs/2512.12583 |