GENERATIV SUN’IY INTELLEKTDA PROMPT INJECTION XAVFLARI VA ULARNING MA’LUMOTLARNI SIRQIB CHIQISHIDAGI ROLINI KAMAYTIRISH
DOI:
https://doi.org/10.5281/zenodo.23375526Abstrak
Katta til modellari va Generativ sun’iy intellekt (GenAI) xizmatlarining korporativ hamda individual jarayonlarga jadal integratsiyalashuvi yangi turdagi kiberxavfsizlik tahdidlarini keltirib chiqarmoqda. Ular orasida eng xavflisi Prompt Injection xurujlari bo‘lib, ular modelning mantiqiy ko‘rsatmalarini buzish va tizim ichidagi maxfiy ma’lumotlarni oshkor qilishga qaratilgan. Ushbu tezisda Prompt Injection xurujlarining mexanizmlari, ularning ma’lumotlar sizib chiqishiga ta’siri hamda ushbu xavflarni kamaytirishga qaratilgan ko‘p qatlamli amaliy va texnik yechimlar tahlil qilinadi.
Yuklab olishlar
Nashr qilingan
Nashr
Bo'lim
Foydalanilgan adabiyotlar
1. Greshake, K., Abdelnabi, S., Mishra, S., Endres, C., Holz, T., & Fritz, M. (2023). Not what you’ve signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection. Proceedings of the 16th ACM Workshop on Artificial Intelligence and Security (AISEC ’23), 79–90.
2. OWASP Foundation. (2023). OWASP Top 10 for Large Language Model Applications (v1.1). Open Web Application Security Project.
3. Inan, H., Upasani, K., et al. (2023). Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations. arXiv preprint arXiv:2312.06674.
4. Yao, Y., Duan, J., et al. (2024). A Survey on Large Language Model (LLM) Security and Privacy: Vulnerabilities, Attacks, and Defenses. IEEE TKDE, 1–20.