- Sirdaryo viloyati Guliston shahar
- Dushanba-Shanba: 9.00 - 18.00
Azizbek Xaitbayev Pirnazarovich
10.5281/zenodo.23375526
Annotatsiya
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.
Kalit so'zlar:
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.