Note
Turkish Translation of the OWASP LLM Top 10 2026 Is Live
We are publishing an unofficial Turkish translation of the OWASP GenAI LLM Top 10 2026: the ten critical LLM security risks, what changed from 2025, and the open-source repository.
- owasp
- llm-security
- prompt-injection
- genai
The OWASP GenAI Security Project published the 2026 edition of OWASP Top 10 for LLM Applications in August 2026 — the list of the ten most critical security risks facing large language model applications. This edition brings a first: the ranking was not built on expert judgement alone. The community vote carried three quarters of the weight, while the remaining quarter came from real-world incident records drawn from public vulnerability databases and an AI-harm database.
There is no official Turkish translation of the document. As a studio working on AI integration and secure AI architecture, we translated it in full and published it as open source:
→ github.com/neveratech/owasp-llm-top10-tr
The translation is complete: foreword, the 2026 change summary, all ten entries (LLM01–LLM10) and the appendices.
The 2026 list at a glance
| # | Official title | Turkish rendering |
|---|---|---|
| LLM01 | Prompt Injection | Komut Enjeksiyonu |
| LLM02 | Sensitive Information Disclosure | Hassas Bilgi İfşası |
| LLM03 | Excessive Agency | Aşırı Yetki |
| LLM04 | Supply Chain | Tedarik Zinciri |
| LLM05 | Data and Model Poisoning | Veri ve Model Zehirlenmesi |
| LLM06 | Unbounded Consumption | Sınırsız Tüketim |
| LLM07 | Misinformation | Yanlış Bilgi |
| LLM08 | Hidden Context Exposure | Gizli Bağlam İfşası |
| LLM09 | Vector and Embedding Weaknesses | Vektör ve Gömme Zafiyetleri |
| LLM10 | Improper Output Handling | Hatalı Çıktı İşleme |
What changed in 2026
The list's central argument fits in one sentence: stop trying to build a model that cannot be fooled; build the system around it so that when the model is fooled — and it will be — nothing important breaks. Seen through that lens, the main moves in 2026 are:
- Excessive Agency climbed from sixth to third. Community vote and incident records agree that the real damage happens in agentic deployments that can call tools and carry memory. This is the list's most consequential move.
- Prompt Injection stayed at the top — with an interesting paradox: ranked on raw incident records alone it would not even make the top ten. This is where the three-quarters weighting of the community vote earns its keep. OWASP reads it as a defence effect: teams fight this risk so hard that the count of clean public exploits understates the real exposure.
- Misinformation was pulled up. Experts placed it near the bottom; incident records put it near the top — the widest gap between belief and evidence.
- System Prompt Leakage became Hidden Context Exposure — a broader frame for the same failure: information that should stay out of reach leaking out.
- Improper Output Handling fell from fifth to tenth, while its scope grew to include insecure code produced at scale by assistants.
For the full change map and the detailed translation of each entry, see the translation repository.
Why this matters to us
Every team taking an LLM-based system to production — chatbot, RAG architecture, agent-driven automation or enterprise AI integration — meets at least a few of these ten risks in the first sprint. This list is one of the frameworks we work from when we build secure AI architecture, and we want the Turkish-speaking developer community to have it in their own language.
If you want to talk through the security architecture of an enterprise AI project, get in touch.
Licence and attribution
Original work: OWASP Top 10 for Large Language Model Applications, Version 2026 — OWASP GenAI Security Project, OWASP Foundation (genai.owasp.org), published under CC BY-SA 4.0. Our translation is an unofficial community translation, neither reviewed nor endorsed by OWASP, and is distributed under the same licence. In case of any discrepancy, the English original governs.