MPIDetector: Detecting Multi-modal Prompt Injection via LLM-based Reasoning

Yiheng Li, Jingyue Li

With the rapid integration of Multi-modal Large Language Models (MLLMs) into real-world applications, Prompt Injection (PI) has emerged as a critical security threat. Attackers can conceal malicious instructions within textual narratives or visual inputs to bypass safety alignment. Existing approaches, such as signature- and perplexity-based methods, rely heavily on pattern matching, making them insufficient to detect sophisticated, cross-modal PI payloads. In this paper, we propose a novel detection framework, called MPIDetector, that leverages LLMs' intrinsic reasoning capabilities via few-shot In-Context Learning (ICL). Instead of fine-tuning, we provide the model with specific prompt templates and representative attack examples, enabling it to scrutinize the semantic intent of input across modalities. We constructed a comprehensive evaluation benchmark comprising eight textual and seven visual malicious categories, supplemented with benign datasets to rigorously test for false positives. Our evaluation across multiple leading LLMs, including Gemini-2.0-Flash, Qwen3-VL-32B-Instruct, and Doubao-seed-1.6-flash, demonstrates the efficacy and generalizability of this approach. The…