Multi-Layer Defense Architecture: How Prompt Injection Detection System Protects Large Language Models from Prompt Injection Attacks

This article introduces the Prompt Injection Detection System, a cybersecurity framework designed specifically for detecting and defending against prompt injection attacks on large language models (LLMs). The framework uses a five-layer detection mechanism—keyword analysis, pattern matching, intent detection, semantic similarity analysis, and risk scoring—to build a comprehensive protection system, providing real-time security for LLM applications.

With the widespread deployment of LLMs in various applications, prompt injection attacks have become a core security issue. Attackers construct inputs to induce models to output sensitive information or perform unintended operations; attack methods have evolved from early "jailbreak" prompts to complex multi-turn dialogue attacks, making traditional single protection strategies difficult to handle. Against this backdrop, the Prompt Injection Detection System was developed.

Layer 1: Keyword Analysis

Quickly scan inputs using a dynamically updated malicious keyword library to block templated attacks and reduce the burden of subsequent analysis.

Layer 2: Pattern Matching

Use regular expressions and predefined attack pattern libraries to identify attack forms such as role-playing and instruction overriding, and handle variant attacks.

Layer 3: Intent Detection

Analyze the semantic intent of inputs to determine if they exceed legitimate scenarios (e.g., requesting to ignore security instructions) and identify malicious inputs that appear harmless.

Layer 4: Semantic Similarity Analysis

Use SentenceTransformers embedding models to compare the semantics of inputs with known attack samples, addressing evasion strategies like paraphrasing and synonym replacement.

Layer 5: Risk Scoring

Calculate a quantitative risk score by integrating results from the previous four layers, and implement tiered responses (normal processing, monitoring, blocking/manual review).

The system is developed in Python, with a tech stack including:

• SentenceTransformers: Supports semantic similarity analysis
• Pandas: Data processing and structured storage
• Scikit-learn: Machine learning model training and evaluation
• Streamlit: Web interactive interface

The framework is modularly designed; each detection layer can be independently configured and upgraded. Developers can adjust parameters, update libraries, or replace algorithms to adapt to evolving attacks.

• Enterprise-level LLM Application Protection: Act as a front-end security gateway to block malicious inputs and protect business-sensitive information.
• Public API Security Enhancement: Integrate the system to improve service security without sacrificing user experience.
• Security Research and Education: Transparent logic and configurable parameters make it an ideal platform for researching attacks and defenses.

Current Limitations:

• Cannot timely identify completely new, unrecorded attack patterns
• Semantic overlap between legitimate inputs and attack prompts may lead to false positives
• Attackers can bypass detection through carefully crafted wording

Future Improvement Directions:

• Introduce large models to detect zero-day attacks
• Establish a crowdsourced attack sample sharing mechanism
• Develop adaptive learning algorithms to optimize detection strategies

The Prompt Injection Detection System is a valuable attempt in the field of LLM security protection, and the multi-layer defense concept is worth learning from. As LLM applications become more widespread, dedicated security tools are increasingly important; developers need to consider both functional development and security protection simultaneously.