Securing AI Applications: OWASP Top 10 for LLMs

As AI applications move into production, security cannot be an afterthought. The OWASP Top 10 for Large Language Model Applications identifies the most critical security risks. Here is how to address them in practice.

1. Prompt Injection

The most prevalent risk. Attackers craft inputs that override system prompts. Mitigations include input sanitization, output validation, and privilege separation — never let the LLM execute actions directly without a validation layer.

2. Insecure Output Handling

LLM outputs can contain malicious content: XSS payloads, SQL injection, or shell commands. Always sanitize LLM output before rendering in HTML, executing as code, or passing to downstream systems.

3. Training Data Poisoning

If your RAG pipeline ingests external data, validate and sanitize all sources. Implement content integrity checks and monitor for anomalous changes in your knowledge base.

4. Model Denial of Service

Rate limit all LLM API calls. Set maximum token limits on inputs and outputs. Monitor for patterns that trigger expensive computations.

5. Supply Chain Vulnerabilities

Lock your model versions. Audit third-party LangChain tools and plugins. Pin dependency versions and scan for known vulnerabilities in your AI stack.

Practical Defense-in-Depth

The most effective approach combines:

• Input guardrails: Validate and classify user inputs before they reach the model
• Output guardrails: Check model responses against safety policies
• Monitoring: Real-time detection of anomalous patterns
• Least privilege: AI agents should have minimal permissions

Conclusion

Securing AI applications requires the same rigor as traditional application security, plus new defenses specific to LLM risks. Start with the OWASP Top 10 for LLMs and build your security posture incrementally.