# Top-3 Tools for Detection/Preventing Prompt Injection

[Rebuff.ai](https://github.com/protectai/rebuff):[  
Rebuff](https://lnkd.in/g4SidZW6%EF%BF%BCRebuff) offers 4 layers of defense:  
\- Heuristics: Filter out potentially malicious input before it reaches the LLM.  
\- LLM-based detection: Use a dedicated LLM to analyze incoming prompts - and identify potential attacks.  
\- VectorDB: Store embeddings of previous attacks in a vector database to recognize and prevent similar attacks in the future.  
\- Canary tokens: Add canary tokens to prompts to detect leakages

[Garak](https://github.com/leondz/garak/):[  
It’s](https://lnkd.in/gitkmNtw%EF%BF%BCIt%E2%80%99s) a LLM vulnerability scanner (nmap for LLMs). It supports:  
\- probes for hallucination  
\- data leakage  
\- prompt injection  
\- misinformation  
\- toxicity generation  
\- jailbreaks

[Promptmap](https://github.com/utkusen/promptmap) was developed by my colleague Utku Sen. It is a tool that automatically tests prompt injection attacks and supports the following attack types:  
\- Basic Injection  
\- Translation Injection  
\- Math Injection  
\- Context-Switch  
\- External Browsing