The OWASP LLM Top 10: A Practical Defense Guide
A grounded walkthrough of the OWASP Top 10 for LLM applications and which risks a pre-LLM filter actually addresses.
Why the OWASP LLM Top 10 Exists
As organizations rushed to ship LLM features, the same vulnerability classes kept reappearing. The OWASP Top 10 for LLM Applications catalogs them so teams can reason about risk systematically instead of discovering each pitfall in production.
LLM01: Prompt Injection
The headline risk. Attackers smuggle instructions into model input, directly or through retrieved content. A pre-LLM filter that scores inbound text for injection — like Sprappy Filter at https://api.sprapp.com/v1/filter — is a direct control here.
LLM02: Insecure Output Handling
Treating model output as trusted before it reaches downstream systems. Filtering reduces the upstream attack surface but does not replace output validation. You still need to escape, sanitize, and sandbox what the model emits.
LLM03: Training Data Poisoning
Corrupting the data a model learns from. This is a model-lifecycle concern, not an inference-time one. A prompt filter does not address it; data governance does.
LLM04: Model Denial of Service
Crafting inputs that exhaust resources. A filter can flag API abuse patterns and oversized or pathological inputs before they hit your model, contributing to mitigation.
LLM05: Supply Chain Vulnerabilities
Compromised models, plugins, or dependencies. An inference filter is orthogonal here; vet your supply chain separately.
LLM06: Sensitive Information Disclosure
Models leaking PII or secrets. Scoring inbound prompts for PII exposure and credential theft catches data heading into the model. The PII and credential categories in Sprappy Filter target exactly this.
LLM07: Insecure Plugin Design
Plugins that over-trust input. Filtering plugin-bound prompts helps, but plugin design discipline is the primary control.
LLM08: Excessive Agency
Giving the model too much autonomy. A filter on the prompt path can catch instructions attempting to trigger unauthorized actions, but you must also constrain what the agent is permitted to do.
LLM09: Overreliance
Trusting model output uncritically. Process and human review address this, not filtering.
LLM10: Model Theft
Exfiltration of model weights. Infrastructure security, not inference filtering.
Where a Filter Honestly Helps
Be precise about scope. A pre-LLM filter meaningfully addresses LLM01, LLM06, parts of LLM04 and LLM08, and reduces the surface for LLM02. It does nothing for training-time and supply-chain risks. Anyone claiming a single product covers all ten is overselling.
Putting It Together
Use the filter as the inbound control point, then layer output handling, agency constraints, and data governance around it. Score prompts at https://api.sprapp.com/v1/filter, but treat that as one layer in a defense-in-depth program — never the whole program.