Adopting the OWASP Top 10 for LLM Applications: A Practical Guide for Organizations
What Is the OWASP Top 10 for LLM Applications?
The OWASP Top 10 for Large Language Model (LLM) Applications is a community-driven framework that highlights the most critical security risks unique to LLMs and generative AI systems. First released in 2023 and most recently updated in 2025, the framework draws from real-world attacks, community feedback, and security research to provide a practical lens on how adversaries exploit LLM.
The 2025 release includes ten categories:
Prompt Injection – Manipulating prompts to alter model behavior.
Sensitive Information Disclosure – Leaking private or proprietary data.
Supply Chain Risks – Dependencies on external providers or training data.
Data and Model Poisoning – Manipulated datasets introducing malicious bias.
Improper Output Handling – Using model outputs unsafely in downstream systems.
Excessive Agency – Giving LLMs too much autonomy, risking harmful actions.
System Prompt Leakage – Exposing hidden or sensitive system instructions.
Vector and Embedding Weaknesses – Manipulated retrieval or embeddings in RAG pipelines.
Misinformation – Generating plausible but false or misleading content.
Unbounded Consumption – Resource misuse, denial of service, or cost overrun.
Each entry details vulnerabilities, attack scenarios, and recommended mitigations, providing a baseline for organizations adopting LLM-powered applications.
Who is this framework for?
This framework is for any organization that is developing their own models and applications that are dependent up on models, especially if they are going to be client facing or will influence decisions.
How to Adopt the Framework in Your Organization
Adopting the OWASP Top 10 for LLMs isn’t about compliance, it’s about building resilience and trustworthiness into AI systems. Organizations should embed the framework into their existing application security (AppSec), risk management, and AI governance programs.
Key integration steps include:
Map risks to existing controls. Align each of the Top 10 risks with your enterprise security controls (e.g., NIST AI RMF, ISO/IEC 42001, or your internal risk taxonomy).
Update threat modeling. Incorporate LLM-specific attack vectors into system design reviews and architecture diagrams.
Strengthen supply chain oversight. Apply vendor due diligence, model documentation review, and “AI bill of materials” practices.
Embed into SecOps. Expand your vulnerability scanning, red-teaming, and adversarial testing to include prompt injection, model poisoning, and data exfiltration scenarios.
Add human-in-the-loop controls. Ensure high-risk outputs and actions are reviewed by humans before execution.
Educate teams. Train developers, security professionals, and business stakeholders on how LLM vulnerabilities differ from traditional software flaws.
Conclusion
The OWASP Top 10 for LLM Applications provides a practical foundation for managing the unique risks of generative AI. By embedding its principles into existing security programs, organizations can reduce vulnerabilities, maintain compliance, and use LLMs responsibly. If you need help implementing an effective framework for adopting Gen AI, reach today to schedule a free consultation.