OWASP Top 10 Risks to Secure LLM Applications
Address OWASP's 10 critical LLM vulnerabilities like prompt injection and insecure outputs to prevent breaches, DoS, and data leaks in AI apps—version 1.1 from 600+ global experts.
Mitigate Input Attacks to Block Manipulation
Crafted inputs exploit LLMs through Prompt Injection (LLM01), enabling unauthorized access, data breaches, or altered decisions by overriding intended prompts. Protect by validating inputs, using privilege controls, and separating user data from instructions. Training Data Poisoning (LLM03) occurs when tampered datasets degrade model accuracy, security, or ethics—source data from trusted providers, validate rigorously, and monitor for anomalies to maintain reliable outputs.
These risks underscore validating all inputs: untrusted data directly compromises LLM behavior, so implement sandboxing and input sanitization from day one in production pipelines.
Secure Outputs and Avoid Overreliance
Insecure Output Handling (LLM02) skips validation, risking code execution or data exposure downstream—always sanitize, validate schemas, and use human review for high-stakes outputs. Sensitive Information Disclosure (LLM06) leaks PII or secrets in responses, inviting legal issues or competitive harm; deploy output filters, redaction tools, and access controls to scrub responses.
Overreliance (LLM09) treats LLM outputs as infallible, leading to poor decisions or vulnerabilities—cross-verify with rules-based checks, diverse sources, and audits to build robust systems. Outcomes: These prevent exploits where untrusted LLM responses propagate attacks, ensuring safe integration into apps.
Guard Supply Chains, Resources, and Autonomy
Model Denial of Service (LLM04) overwhelms models with heavy queries, spiking costs and downtime—rate-limit inputs, optimize prompts, and monitor resource usage. Supply Chain Vulnerabilities (LLM05) from compromised models/datasets cause breaches; vet third-party components and use integrity checks.
Insecure Plugin Design (LLM07) allows untrusted inputs to trigger RCE via poor access controls—enforce least privilege and input validation in plugins. Excessive Agency (LLM08) gives LLMs unchecked actions, eroding privacy and trust; scope permissions narrowly and add approval gates for agentic systems. Model Theft (LLM10) exposes proprietary models to rivals—encrypt queries, monitor access, and use watermarking.
Impact: Proactive defenses like monitoring and controls scale with growing apps, as seen in OWASP's project with 600 experts from 18 countries and 8,000 members evolving from 2023 Top 10 origins.