prompt-guard

To prevent prompt injection attacks on an AI agent, you can apply a multi-layered defense strategy utilizing pattern matching and severity scoring to block malicious inputs. This approach safeguards sensitive data and prevents hijacking.

You can detect prompt injection across multiple languages including English, Korean, Japanese, and Chinese. The defense system uses context-aware analysis and severity scoring to identify malicious instructions and obfuscation techniques.

Yes, you need Python 3.8 or higher and pyyaml installed to configure and execute this AI safety defense system. These dependencies are required to run the analysis scripts and process the configuration files.

To stop an AI assistant from leaking API keys in a group chat, implement a secret protection layer that blocks the AI from revealing credentials. This prevents data exfiltration even when attackers use multi-turn manipulation techniques.

Context-aware analysis for LLM defense is a mechanism that evaluates conversation history to identify subtle prompt injection attacks. It works alongside pattern matching to score the severity of malicious inputs and block data exfiltration.

Pattern matching alone does not block sophisticated multi-turn prompt injection. The system combines pattern matching with context-aware analysis and severity scoring to detect obfuscation techniques and prevent data exfiltration attacks.