LLM Security: The OWASP Top 10 for Enterprise Architects

Why LLM Security Is Different

Traditional application security focuses on well-understood attack vectors: SQL injection, XSS, CSRF. LLM security introduces an entirely new class of vulnerabilities because the attack surface is the natural language interface itself. When your application accepts arbitrary text and uses it to drive decisions, every input becomes a potential attack vector.

The OWASP Top 10 for LLM Applications provides a structured framework for thinking about these risks. But frameworks alone don't secure systems. What follows is how I translate OWASP's guidance into production architecture.

The Top Threats and How to Defend

LLM01: Prompt Injection

The most prevalent LLM vulnerability. An attacker crafts input that overrides the system prompt or manipulates the model into unintended behavior. In enterprise contexts, this can mean exfiltrating data from RAG pipelines or bypassing access controls.

Defense architecture:

• Input sanitization layer — Pattern matching and ML-based classifiers that detect injection attempts before they reach the model
• Privilege separation — The LLM never has direct access to sensitive operations. All actions go through a validation layer that checks intent against allowed operations
• Output verification — Every model response is validated against expected output schemas before being acted upon

LLM02: Insecure Output Handling

LLM outputs treated as trusted input to downstream systems create injection vectors. If your model's response feeds directly into a database query, API call, or rendered HTML, you've created a new attack surface.

The fix is straightforward but often overlooked: treat every LLM output as untrusted user input. Apply the same sanitization and validation you'd apply to any external input before using it in downstream operations.

LLM03: Training Data Poisoning

For enterprises using fine-tuned models or RAG pipelines, the integrity of your data sources is a security boundary. Compromised training data or poisoned document repositories can systematically bias model outputs.

Defense: data provenance tracking, integrity checksums on document corpora, and regular evaluation against known-good test sets to detect drift that might indicate poisoning.

LLM06: Sensitive Information Disclosure

Models can leak PII, proprietary data, or system details through their responses. In RAG systems, improper access controls on the retrieval layer can expose documents a user shouldn't see.

This is where most enterprise deployments are weakest. The defense requires a multi-layer approach:

• Input PII detection — Scan and redact sensitive data before it enters the pipeline
• Retrieval access controls — Enforce document-level permissions in the vector store, not just at the application layer
• Output scanning — Detect and redact PII in model responses before they reach the user
• Audit logging — Track every document retrieved and every response generated, without storing PII in the logs themselves

Defense-in-Depth Architecture

Security isn't a single layer. The architecture that works in production has five defense zones:

1. Perimeter — API gateway with rate limiting, authentication, and request validation
2. Input processing — Prompt injection detection, PII scanning, content classification
3. Execution — Sandboxed model inference with resource limits and tool-use restrictions
4. Output processing — Response validation, PII redaction, hallucination detection
5. Monitoring — Real-time anomaly detection on usage patterns, cost spikes, and output distributions

The most dangerous AI security failures are the ones nobody detects. Continuous monitoring isn't optional — it's the last line of defense when prevention fails.

NeMo Guardrails in Practice

NVIDIA's NeMo Guardrails has become my go-to framework for runtime protection. It provides programmable rails that intercept both inputs and outputs, with support for topic control, fact-checking against retrieved documents, and custom validation functions.

The key architectural decision is where to place guardrails in your pipeline. I deploy them at three points: before retrieval (input rails), between retrieval and generation (context rails), and after generation (output rails). Each checkpoint serves a different security function, and together they create a comprehensive safety envelope.

Building a Security-First Culture

Technical controls matter, but culture determines whether they're maintained. Every AI team needs security champions who understand both the LLM attack surface and the business context. Regular red-teaming exercises — where internal teams try to break your AI systems — build institutional knowledge about vulnerabilities before attackers find them.

The OWASP LLM Top 10 is a starting point, not a finish line. As the attack surface evolves, your defenses must evolve with it.