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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2505.18172 |
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| _version_ | 1866909709757317120 |
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| author | Bahadur, Sunil Kumar Jang Dhar, Gopala Nigam, Lavi |
| author_facet | Bahadur, Sunil Kumar Jang Dhar, Gopala Nigam, Lavi |
| contents | The increasing sophistication and integration of Generative AI (GenAI) models into diverse applications introduce new security challenges that traditional methods struggle to address. This research explores the critical need for proactive security measures to mitigate the risks associated with malicious exploitation of GenAI systems. We present a framework encompassing key approaches, tools, and strategies designed to outmaneuver even advanced adversarial attacks, emphasizing the importance of securing GenAI innovation against potential liabilities. We also empirically prove the effectiveness of the said framework by testing it against the SPML Chatbot Prompt Injection Dataset. This work highlights the shift from reactive to proactive security practices essential for the safe and responsible deployment of GenAI technologies |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_18172 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GenAI Security: Outsmarting the Bots with a Proactive Testing Framework Bahadur, Sunil Kumar Jang Dhar, Gopala Nigam, Lavi Cryptography and Security Machine Learning The increasing sophistication and integration of Generative AI (GenAI) models into diverse applications introduce new security challenges that traditional methods struggle to address. This research explores the critical need for proactive security measures to mitigate the risks associated with malicious exploitation of GenAI systems. We present a framework encompassing key approaches, tools, and strategies designed to outmaneuver even advanced adversarial attacks, emphasizing the importance of securing GenAI innovation against potential liabilities. We also empirically prove the effectiveness of the said framework by testing it against the SPML Chatbot Prompt Injection Dataset. This work highlights the shift from reactive to proactive security practices essential for the safe and responsible deployment of GenAI technologies |
| title | GenAI Security: Outsmarting the Bots with a Proactive Testing Framework |
| topic | Cryptography and Security Machine Learning |
| url | https://arxiv.org/abs/2505.18172 |