OWASP publishes updated Top 10 for Large Language Model Applications outlining prompt injection and data leakage risks

OWASP released the Top 10 for Large Language Model Applications, documenting the most critical security risks for LLM-based systems, such as prompt injection, data leakage, insecure output handling, and supply-chain vulnerabilities in models and plugins.[1] The project provides concrete examples of how LLM-powered apps can be manipulated to exfiltrate sensitive data, call tools in unsafe ways, or abuse external plugins, and offers mitigation guidance for developers building SaaS, healthcare, and fintech applications.[1]

According to OWASP, the updated Top 10 for Large Language Model Applications highlights prompt injection, insecure output handling, sensitive information disclosure, and supply-chain vulnerabilities as critical risks for LLM-based systems, including agents and plugin ecosystems.[3][6] The project documents concrete attack patterns where crafted prompts or untrusted external content can manipulate LLMs to exfiltrate data, misuse tools, or abuse plugins, alongside sector-specific examples for SaaS, healthcare, and fintech applications.[3] From a CyberSE.AI perspective, these findings underscore that secure LLM and agent design must treat the model as an untrusted component, with strong guardrails on tool access, data exposure, and plugin permissions to prevent business-logic abuse and data loss. Practically, this drives the need for Secure AI Agent Build services that incorporate OWASP-aligned controls such as constrained tool invocation, rigorous input/output validation, least-privilege access to back-end systems, and adversarial testing against prompt injection and data leakage scenarios.