[Feedback Wanted] Low-Latency LLM Guardrails Using a 14M Parameter Discriminator

Hi everyone,

I have released ** DeshwalX/electra-small-prompt-injection-v1**, a multi-label classifier for low-latency LLM guardrails.

Many prompt injection detectors rely on large autoregressive models that introduce processing latency. This project tests the baseline efficiency of a compact encoder backbone by fine-tuning ** google/electra-small-discriminator** (~14M parameters) to detect four independent safety vectors in a single forward pass.

WildGuard Test Benchmark Evaluation:

Micro F1: 90.00% | Macro F1: 89.00%

Prompt Adversarial (Jailbreaks): 1.00 F1 Response Refusal: 0.86 F1

Prompt Harmful: 0.88 F1

Response Harmful: 0.81 F1

Model Repository: DeshwalX/electra-small-prompt-injection-v1 · Hugging Face I am looking for community feedback to improve Version 1. Please test the model on your own data and share:

Edge cases where the model fails or misses an injection.

Examples of false positives on benign inputs.

Suggestions for architecture configurations or loss weight balancing for the next iteration.