kNNGuard: Turning LLM Hidden Activations into a Training-Free Configurable Guardrail
Large language models (LLMs) are increasingly deployed in domains requiring guardrails to detect unsafe, off-topic, or adversarial prompts. Existing guardrails predominantly rely on fine-tuning to build classifiers, which often suffer from low generalization and high inference latency. We present kNNGuard, a training-free guardrail that utilizes the activation space of an off-the-shelf LLM. Given a small bank of 50 safe and unsafe prompts, kNNGuard extracts hidden activations and performs multi-layer kNN fusing activation-space and embedding-space scores for classification. Across six domains spanning topical and security prompts, kNNGuard achieves competitive or superior F1 compared to f...
| Authors | Mahmoud Abdelfattah, Hamid Nasiri, Peter Garraghan |
|---|---|
| Primary category | cs.LG |
| Published | 2 July 2026 |
| Last updated at source | 6 July 2026 |
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