OWASP Top 10 for LLM Applications: A Practical Security Guide
A hands-on guide to the OWASP Top 10 risks for LLM applications, with real-world examples and mitigation strategies for each vulnerability category.
By Signal & Soil
The OWASP Top 10 for Large Language Model Applications has become the definitive reference for LLM security. But too many teams treat it as a checklist rather than a security engineering guide. Here’s a practical breakdown of each risk, what it looks like in production, and how to actually mitigate it.
LLM01: Prompt Injection
The Risk: Attackers craft inputs that override system prompts, causing the LLM to execute unintended actions or reveal sensitive information.
In Practice: This is the most common and most dangerous LLM vulnerability. It manifests as both direct injection (user sends malicious prompts) and indirect injection (malicious content in retrieved documents or web pages manipulates the model).
Mitigation:
- Implement strict input validation and sanitization
- Use privilege separation — the LLM should never have direct access to sensitive operations
- Implement output filtering for sensitive data patterns
- Use canary tokens in system prompts to detect extraction attempts
LLM02: Insecure Output Handling
The Risk: LLM outputs are trusted and passed directly to downstream systems without validation, enabling injection attacks through the model.
In Practice: An LLM generates a SQL query based on user input, and the application executes it directly. Or an LLM generates HTML that’s rendered without sanitization, enabling XSS.
Mitigation:
- Treat all LLM output as untrusted input
- Apply the same input validation to LLM outputs that you would to user inputs
- Use parameterized queries and output encoding
- Implement allowlists for permitted actions and formats
LLM03: Training Data Poisoning
The Risk: Manipulation of training data to introduce vulnerabilities, biases, or backdoors into the model.
In Practice: For most organizations using third-party models, this manifests as supply chain risk. For those fine-tuning, it’s about data provenance and integrity.
Mitigation:
- Verify data provenance for all training and fine-tuning data
- Implement data validation pipelines
- Monitor model outputs for unexpected behavior changes
- Use multiple model sources for critical decisions
Building Security In
The OWASP LLM Top 10 isn’t a one-time audit. It’s a framework for building security into every phase of LLM application development. The most effective teams integrate these controls into their development lifecycle from day one, not as an afterthought.
The same principles apply to the newer OWASP Top 10 for Agentic AI, which extends these concepts to autonomous agent systems with tool use, multi-step reasoning, and delegated authority.
Signal & Soil provides LLM security assessments and helps teams implement OWASP-aligned security controls. Get in touch to discuss your LLM security posture.