OWASP AI Security Project: Top 10 LLM Vulnerabilities Guide

Organizations are increasingly investing in large language models (LLMs) to enhance customer experiences, automate workflows, and innovate, with 72% of companies planning to increase spending in the coming year. However, this rapid adoption introduces new security risks specific to LLMs, such as prompt manipulation, data poisoning, and unmonitored resource consumption. The OWASP Top 10 for LLM Applications 2025, developed by the Open Worldwide Application Security Project, provides a framework for understanding these threats. Kong AI Gateway offers a comprehensive solution for mitigating these vulnerabilities by integrating specialized AI security controls into existing infrastructures, ensuring consistent security policies across various LLM providers. The platform supports flexible deployment strategies, offers runtime observability, and integrates with third-party monitoring tools to enhance visibility and reliability. Key vulnerabilities addressed by Kong include prompt injection, sensitive information disclosure, supply chain vulnerabilities, data poisoning, improper output handling, system prompt leakage, vector and embedding weaknesses, misinformation, unbounded consumption, and excessive autonomy. The gateway's security measures include access control, prompt management, semantic understanding, caching capabilities, and resource management, making it suitable for diverse industries with specific compliance needs.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.