Sentinel: SOTA model to protect against prompt injections
Authors: name: Dror Ivry, name: Oran Nahum

Large Language Models (LLMs) are increasingly powerful but remain vulnerable
to prompt injection attacks, where malicious inputs cause the model to deviate
from its intended instructions. This paper introduces Sentinel, a novel
detection model, qualifire/prompt-injection-sentinel, based on the
\answerdotai/ModernBERT-large architecture. By leveraging ModernBERT's advanced
features and fine-tuning on an extensive and diverse dataset comprising a few
open-source and private collections, Sentinel achieves state-of-the-art
performance. This dataset amalgamates varied attack types, from role-playing
and instruction hijacking to attempts to generate biased content, alongside a
broad spectrum of benign instructions, with private datasets specifically
targeting nuanced error correction and real-world misclassifications. On a
comprehensive, unseen internal test set, Sentinel demonstrates an average
accuracy of 0.987 and an F1-score of 0.980. Furthermore, when evaluated on
public benchmarks, it consistently outperforms strong baselines like
protectai/deberta-v3-base-prompt-injection-v2. This work details Sentinel's
architecture, its meticulous dataset curation, its training methodology, and a
thorough evaluation, highlighting its superior detection capabilities.