Multi-Layered Protection: How the Prompt Injection Detection System Safeguards the Security Boundaries of Large Language Models

With the widespread application of Large Language Models (LLMs) across various industries, their security issues have become increasingly prominent, and prompt injection attacks have emerged as one of the major risks threatening the security of AI systems. This article introduces the open-source security framework Prompt Injection Detection System, analyzing its five-layer detection mechanism, technical implementation, and application scenarios, providing a reference for AI security practices.

The essence of prompt injection attacks is to exploit the sensitivity of LLMs to input text by embedding specific instructions in user input to override or tamper with system preset prompts, which may lead to serious consequences such as information leakage and execution of malicious instructions. Traditional security protection methods (keyword filtering, rule matching) are easily bypassed and struggle to cope with evolving attack techniques, necessitating an intelligent multi-layer detection solution.

The Prompt Injection Detection System adopts a five-layer detection mechanism:

1. Keyword Analysis: Intercept common low-complexity attacks via a dynamically updated dangerous word database;
2. Pattern Matching: Use regular expressions and predefined attack pattern templates to detect typical structural features;
3. Intent Detection: Perform semantic analysis to determine if input intent aligns with context, identifying suspicious requests;
4. Semantic Similarity Analysis: Use SentenceTransformers to calculate the semantic similarity between input and known attack samples;
5. Risk Scoring: Conduct a comprehensive weighted evaluation of multi-layer results, triggering interception when the threshold is exceeded.

The framework is built on a Python tech stack, relying on Streamlit (web interaction interface), SentenceTransformers (semantic encoding), Scikit-learn (risk scoring model), and Pandas (log processing). Deployment is simple, with setup.bat and run_app.bat scripts for one-click dependency installation and service startup. It requires Python 3.10+ and needs internet access to download pre-trained models during the first run.

Applicable scenarios: Enterprise AI applications (internal assistants, customer service robots), content generation platforms (preventing bypass of audits), education and research (experimental platforms). Limitations: Detection accuracy is affected by unseen attack patterns, semantic ambiguity, and prompt wording; it is recommended to use this framework as part of a multi-layer security architecture.

The Prompt Injection Detection System combines traditional cybersecurity thinking with modern semantic analysis technology, providing an implementable and scalable protection solution. As AI technology evolves, such defense tools will become indispensable security components for AI applications.