Dual-Model Validation

Can you explain why a single model cannot reliably validate its own outputs? Not that it cannot — why. Because the same vulnerability that allows the injection also affects the model's ability to detect that it has been injected. A model following injected instructions believes it is following legitimate instructions. Self-validation is checking your own homework with the same misconception that produced the wrong answer.

1. Separate Validator Model Route the primary model's output through a separate model — different architecture, different prompt, different purpose. The validator's only job is to evaluate whether the output violates any constraints: contains sensitive data, deviates from expected format, includes instructions or URLs, or represents a behavioral anomaly. Two independently compromised models are exponentially harder than one.
2. Classifier Pipeline Replace the full validator model with a lightweight classifier trained specifically on injection detection. The classifier evaluates the output against a labeled dataset of known-good and known-bad outputs. Classifiers are faster and cheaper than full model validation, with the tradeoff that they catch patterns similar to training data and may miss novel attacks.
3. Ensemble Approach Combine multiple validation methods — rule-based pattern matching, classifier evaluation, and full model validation — and require consensus before passing an output through. If any validator flags the output, it is held for review. The ensemble catches more than any individual method at the cost of increased latency and occasional false positives.