Best Practices: Defense in Depth for LLM Applications
Layered security best practices for LLM apps, with Sprappy Filter as the first line.
No Single Control Is Enough
Securing an LLM application is not about finding one perfect filter. It's about layers, where each catches what the others miss. Sprappy Filter is an excellent first line, but treating it as your only line would be a mistake. These best practices build genuine defense in depth.
Layer 1: Screen Input Before the Model
Put Sprappy Filter at the front to score incoming text for prompt injection and abuse before it reaches your LLM. This is cheap, fast, and removes a lot of obvious malicious traffic up front. Setup is in the docs at https://doc.sprapp.com. But know its limits — it's a screen, not a guarantee.
Layer 2: Constrain What the Model Can Do
Limit the model's blast radius. If it can call tools or access data, scope those permissions tightly so that even a successful injection can't reach much. The most robust defense against prompt injection is reducing what a compromised prompt can actually accomplish, not just trying to detect every injection.
Layer 3: Validate and Sanitize Output
Don't trust model output blindly, especially if it flows into downstream systems, code execution, or rendered HTML. Validate structure, escape appropriately, and treat generated content as untrusted input to the next stage. Many real incidents come from acting on model output without checks.
Layer 4: Consider an LLM-Judge for High-Stakes Paths
For sensitive decisions, a second model judging the content can catch nuanced cases a fast filter misses. Use it selectively — it's slow and costly, and it can itself be manipulated, which is exactly why Sprappy Filter should sit in front of it.
Layer 5: Log, Monitor, and Alert
Log filter scores, gate decisions, and anomalies. Watch for spikes in blocked traffic or unusual patterns. Security controls you don't monitor degrade silently. The docs at https://doc.sprapp.com list useful fields to capture.
Layer 6: Keep Humans in the Loop Where It Counts
For the highest-stakes outcomes, automated screening should flag rather than fully decide. A human reviewing the small set of borderline cases is a layer no automated filter replaces.
The Honest Takeaway
Each layer has gaps; together they cover far more than any one alone. Sprappy Filter is a strong, cheap first line, but it's first — not last. Build the other layers and you get resilience; rely on one and a single bypass compromises everything.
Summary
Screen input with Sprappy Filter, constrain the model, validate output, judge selectively, monitor everything, and keep humans on the critical path. Layered defense is the goal; see https://doc.sprapp.com to wire the first layer in.