1. Abstract

The Secure RAG with Prompt-Injection Protection system is an AI-powered security framework designed to protect Retrieval-Augmented Generation (RAG) applications from malicious prompts, indirect prompt injections, and unauthorized information disclosure. Traditional RAG systems retrieve information from external documents and provide it to Large Language Models (LLMs), but malicious instructions embedded in user queries or retrieved documents can manipulate model behavior. The proposed system combines prompt-injection detection, document sanitization, access-control mechanisms, and output validation to improve RAG security. It analyzes user inputs and retrieved content for suspicious instructions, applies configurable security policies, and restricts unauthorized tool usage or sensitive-data access. The system also validates generated responses before presenting them to users. By integrating multiple security layers into the retrieval and generation pipeline, the application aims to reduce prompt-injection risks while preserving useful question-answering functionality.

2. Objectives

1. To develop a secure RAG application with prompt-injection protection.
2. To detect malicious instructions in user prompts and retrieved documents.
3. To identify direct and indirect prompt-injection attempts.
4. To sanitize or isolate untrusted document content.
5. To implement access controls for sensitive documents.
6. To validate LLM-generated responses before displaying them.
7. To prevent unauthorized tool execution and data disclosure.
8. To maintain security logs for suspicious interactions.
9. To evaluate detection accuracy and false-positive rates.
10. To improve the resilience of RAG applications against adversarial inputs.

3. Existing System

Conventional RAG systems retrieve relevant document chunks and pass them to an LLM to generate responses. Although this improves access to external knowledge, retrieved content may contain malicious instructions designed to override system behavior. Basic systems often rely on prompt wording or the LLM's built-in safeguards without independently validating retrieved documents, tool permissions, or generated outputs.

Disadvantages

1. Vulnerability to direct prompt-injection attempts.
2. Indirect injections can be embedded in retrieved documents.
3. Malicious content may influence model-generated responses.
4. Sensitive information may be exposed if access controls are inadequate.
5. LLM-generated tool calls may create security risks.
6. Basic filtering can miss disguised or context-dependent attacks.
7. Security decisions may lack traceable logs and explanations.

4. Proposed System

The proposed system introduces a multi-layer security architecture around the RAG pipeline. It validates user queries, scans retrieved document content for suspicious instructions, enforces document-level access controls, and separates trusted system instructions from untrusted retrieved text. A prompt-injection detector combines rule-based checks with a machine-learning classifier or security-focused LLM to flag potentially malicious content.

Retrieved passages are treated as untrusted data rather than executable instructions. Before a response is delivered, the system checks for policy violations, unauthorized disclosure, and unsupported output. Tool execution is restricted through explicit permissions and parameter validation. Suspicious requests can be blocked, flagged, or routed for human review.

Key Features

1. Direct prompt-injection detection.
2. Indirect injection detection in retrieved documents.
3. User-input and document-content scanning.
4. Untrusted-content isolation.
5. Secure RAG retrieval pipeline.
6. Role-based access control.
7. Sensitive-data filtering.
8. LLM output validation.
9. Restricted tool execution and permission checks.
10. Security event logging and monitoring.
11. Configurable allow, warn, block, and review policies.

5. Implementation Procedure

1. Build a basic RAG pipeline: Collect authorized documents, generate embeddings, store them in a vector database, and connect a compatible LLM.

2. Define the security policy: Specify permitted queries, protected information, document access rules, and actions for suspicious requests.

3. Implement input validation: Check incoming prompts for malformed input, suspicious instruction overrides, and known injection patterns.

4. Develop prompt-injection detection: Combine heuristic rules with a classifier or security-focused model to identify suspicious instructions.

5. Secure document ingestion: Scan uploaded documents for malicious content, validate file types, and prevent untrusted document text from being interpreted as system instructions.

6. Enforce access control: Apply user permissions before retrieval so that unauthorized documents are not included in the LLM context.

7. Implement secure retrieval: Retrieve relevant passages while preserving source metadata and clearly separating untrusted document content from trusted instructions.

8. Harden prompt construction: Instruct the model to treat retrieved content as evidence only, not as instructions that can override system policies.

9. Validate tool execution: Restrict available tools, validate arguments, and require explicit authorization for sensitive actions.

10. Inspect generated responses: Check for sensitive information disclosure, policy violations, and unsupported claims before presenting the answer.

11. Create the security dashboard: Display flagged inputs, risk categories, security events, and verification results for authorized administrators.

12. Test adversarial scenarios: Evaluate direct and indirect prompt injections, malicious documents, benign queries that resemble attacks, and attempts to access unauthorized data.

13. Measure performance: Calculate precision, recall, F1-score, false-positive rate, attack success rate, and normal-task completion rate.

14. Deploy securely: Protect credentials, restrict administrative access, minimize sensitive logging, and regularly update detection rules and models.

6. Software Requirements

7. Hardware Requirements

Note: A prototype using hosted models can run on a standard development computer. Local LLMs and security classifiers may require additional computing resources.

8. Advantages of the Project

1. Improved RAG security: Adds security checks throughout the retrieval and generation pipeline.
2. Prompt-injection awareness: Helps identify suspicious instructions in user queries and retrieved content.
3. Sensitive-data protection: Combines retrieval permissions and output checks to reduce unauthorized disclosure risks.
4. Safer document processing: Treats external documents as untrusted data.
5. Controlled tool usage: Restricts sensitive operations through explicit permissions.
6. Auditable security events: Records relevant security decisions for authorized review.
7. Flexible integration: Can be added to existing RAG chatbots and document assistants.
8. Configurable policies: Allows security rules to be tailored to different applications.
9. Continuous evaluation: Supports testing against new attack patterns and benign inputs.
10. Balanced protection: Evaluates security effectiveness alongside normal query-answering performance.