LLM Security 2026: The OWASP Top 10 for LLM Apps, Explained With Fixes

The OWASP Top 10 for LLM Applications became the security checklist everyone references, but the entries change yearly and the fixes are scattered. Here is the 2026 list with concrete mitigations.

💡 What You Will Learn

The OWASP Top 10 for LLM Applications became the security checklist everyone references, but the entries change yearly and the fixes are scattered. Here is the 2026 list with concrete mitigations.

📜 Table of Contents

• Why LLM Security Is Different
• The 2026 Top Risks and Their Fixes
• The Practical Implementation Order
• The Mental Model

Why LLM Security Is Different

Traditional web security guards inputs and outputs. LLM apps add a new attack surface: the model itself can be manipulated through prompts, and its outputs can be weaponized. The OWASP Top 10 for LLM Applications is the community consensus on where the risks are.

The 2026 Top Risks and Their Fixes

1. Prompt injection - malicious instructions hidden in user input or retrieved documents hijack the model. Fix: treat model output as untrusted, isolate instructions from data (delimiters, special tokens), and never let model output execute privileged actions without validation.
2. Sensitive information disclosure - the model leaks private data from its context or training. Fix: data minimization in prompts, output filtering, per-user permission checks on retrieved docs.
3. Insecure output handling - model output fed straight into SQL/HTML/shell. Fix: treat output as untrusted input to your systems; sanitize like any other external input (see the OWASP injection prevention cheat sheets).
4. Training data poisoning - corrupted data in fine-tuning or RAG sources degrades behavior. Fix: source validation, data provenance, and periodic evaluation for poisoned behavior.
5. Supply chain vulnerabilities - compromised model files, libraries, or plugins (including MCP servers). Fix: pin versions, verify hashes, audit third-party models and tools.
6. Excessive agency - the agent has permissions to do damage (send emails, delete data). Fix: least-privilege design, human approval for high-impact actions, scope limits on tools.
7. Overreliance - users trust wrong answers (hallucinations) for critical decisions. Fix: disclaimers, citations, confidence signals, human review for high-stakes outputs.
8. Model denial of service - crafted inputs cause runaway token usage. Fix: rate limits, input length caps, per-session budgets (see the monitoring guide).
9. Insecure plugins - third-party tool integrations are trusted blindly. Fix: validate plugin inputs/outputs, sandbox plugin execution.
10. Model theft - extraction of the model or its training data via API. Fix: monitoring for suspicious query patterns, output limits, watermarking.

The Practical Implementation Order

1. Today: fix excessive agency (least privilege + human approval) and insecure output handling - these cause real incidents.
2. This week: add prompt injection defenses and per-session cost limits.
3. This month: build the monitoring and evaluation loop (see the monitoring and evaluation guides) so security issues surface in data, not incidents.

The Mental Model

Treat the LLM like an intern with internet access: helpful, fallible, and never fully trusted. Every output is a suggestion until a control validates it.