Secure LLM API Validation Layers

Secure LLM API Validation Layers | Generated by AI

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When you build or use an LLM API, you generally want safeguards to ensure the responses are safe, valid, and useful. Here are the main ways to add checks:

1. Input Validation

Schema checks: Ensure the request JSON follows the expected schema before sending to the LLM.
Prompt sanitization: Strip or escape unwanted characters, malicious instructions, or injection attempts.
Length checks: Enforce max token/character limits for inputs to avoid unnecessary cost or truncation.

2. Output Validation

JSON schema validation: If the LLM is supposed to return JSON, run it through json.loads() and validate against a schema (e.g., with pydantic, jsonschema).
Regex/format checks: For emails, URLs, or numbers, enforce patterns.
Type checking: Verify that fields are of the right type (string, integer, list, etc.).
Range checks: Ensure numeric or date values are within expected bounds.

3. Safety and Content Checks

Toxicity or profanity filters: Run output through a classifier (e.g., Perspective API, OpenAI moderation API).
Policy filters: Define rules to block responses containing certain keywords or categories.
Hallucination detection: Add fact-checking steps (via retrieval-augmented checks, multiple model cross-validation, or rule-based sanity checks).

4. Business Logic Constraints

Consistency rules: If the response must match system data (e.g., available account balance, config values), cross-verify before returning to the user.
Guardrails: Use libraries like Guardrails AI, LMQL, or Instructor to enforce structured outputs and constraints declaratively.
Post-processing hooks: Wrap LLM responses with custom logic (normalize casing, enforce defaults, reject unexpected answers).

5. Redundancy & Cross-Verification

Self-check prompts: Ask the LLM to re-validate or explain its own answer.
Dual-model checking: Use a smaller/faster LLM to verify the main model’s output (e.g., “does this response conform to schema X?”).
Voting/consensus: Run multiple calls and compare answers for stability.

6. Operational Safeguards

Rate limits: Prevent abuse of the API.
Timeouts: Abort requests that hang.
Logging and audit trails: Keep record of requests and filtered responses.
Fallbacks: If validation fails, either retry with adjusted prompt, or return a safe default/error.

👉 In practice, a good LLM API stack layers these:

Validate input.
Call LLM.
Validate output against schema + business rules.
Run safety/content filters.
Return only if checks pass (else retry/fallback).

Would you like me to show you a Python example where we wrap an LLM API call with JSON schema validation and moderation checks?

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