Multi-Layer Prompt Injection Detection for Production AI Systems
// ABSTRACT
As AI agents gain access to sensitive data and real-world actions, prompt injection attacks emerge as a critical security risk. This paper presents a multi-layer detection pipeline designed for production deployment, achieving state-of-the-art accuracy with minimal resource requirements.
Our approach achieves F1 score of 0.998 with median latency of 23ms, running on CPU with only 355MB RAM, making it practical for real-world deployment without GPU infrastructure.
// KEY CONTRIBUTIONS
Production-Ready Detection
Unlike academic solutions requiring GPU clusters, our pipeline runs efficiently on standard CPU hardware. This enables deployment as middleware, browser extensions, or embedded filters without infrastructure overhead.
- Four independent detection layers with complementary strengths
- Sub-30 million parameter neural component
- Support for 48+ languages out of the box
- Deterministic, reproducible results
// THREAT MODEL
Real-World Attack Vectors
The paper examines prompt injection in practical contexts: hidden instructions in documents, malicious email content, compromised web pages, and adversarial inputs targeting AI coding assistants.
We demonstrate that configuration-based defenses (system prompts, allowlists) are insufficient against motivated attackers, necessitating dedicated detection infrastructure.
// DEPLOYMENT
Integration Patterns
The paper discusses practical deployment as:
- Pre-processing hooks for Claude Code and similar tools
- Transparent proxy for OpenAI-compatible APIs
- Browser extension for GitHub Copilot protection
- Inlet filter for Open WebUI deployments
- Middleware for custom LLM gateway architectures
// CITATION