This project implements a multi-layered security architecture to protect Large Language Model (LLM) applications against prompt injection, obfuscation, and misuse attacks.
The framework processes incoming user messages through three core layers:
Cleans the input by stripping away obfuscation, encoding tricks, and manipulative patterns. The sanitized version is logged for traceability and sent forward for further analysis.
Analyzes the cleaned input to detect whether it contains prompt injection patterns. Messages deemed malicious are blocked and logged.
Safe messages are passed to the LLM for response generation. Once a response is generated, it is validated again to ensure that the LLM did not inadvertently produce harmful or unexpected outputs. If flagged, the response is blocked and logged.
All activities, including malicious attempts and flagged responses, are stored in a database for auditing and further analysis.
- Prevent prompt injections and obfuscated attacks at input level
- Detect and block manipulated or adversarial prompts
- Monitor and validate LLM-generated responses for misuse
- Maintain logs for traceability, observability, and compliance