SemGuard: A Triple-Anchor Semantic Security Gateway for Multilingual Prompt Attack Detection in Large Language Models

SemGuard presents a Triple-Anchor Semantic Security Gateway aimed at detecting prompt injection attacks across multiple languages in large language models. The research includes the first formally validated Arabic LLM security dataset with 807 examples across 7 threat categories, demonstrating strong inter-annotator agreement (Fleiss' κ = 0.839). The framework achieves an F1 score of 0.989 and recall of 0.991 on Arabic prompt injection detection, outperforming English-only baselines. The project is published on IEEE Xplore and available on GitHub, targeting improvements in multilingual LLM safety and explainable AI threat detection.

The project addresses the detection of prompt injection attacks in LLMs, which are a class of adversarial inputs that can manipulate model outputs. By providing a multilingual detection framework and a validated dataset, SemGuard enhances the ability of researchers and practitioners to identify and mitigate such attacks, particularly in Arabic language contexts. There is no indication that this is an active exploit or vulnerability affecting deployed systems; rather, it is a research tool to improve security.

This is a research contribution and not a vulnerability or exploit requiring immediate remediation. Organizations using LLMs for Arabic or multilingual applications may consider integrating or evaluating SemGuard's framework and dataset to improve prompt injection detection capabilities. No official patches or fixes are applicable.