LLM & RAG Security
Resistance testing for your language model-based applications
Why LLMGuard?
A cleverly crafted instruction can bypass your chatbot's protections, leak your system prompt, or hijack your RAG pipeline. LLMGuard tests your LLM applications against known and emerging attack techniques, without ever transmitting your sensitive data to a third party.
Supported applications
Chatbots & Assistants
General-purpose or domain-specific conversational assistants
RAG Pipelines
Q&A systems over your internal documents
Content generation
Text generation, automated writing, summarization
Code assistants
Copilots and code generators integrated into your workflows
Translation & summarization
Automatic translation and multilingual summarization models
Domain-specific LLMs
Models fine-tuned for healthcare, finance, legal, scientific use
Speech & Multimodal
Speech and multimodal models (audio + text + image)
AI search engines
LLM-based augmented search and discovery systems
Your application not listed?
If your LLM use case isn't listed, contact us. We develop test modules on demand.
Contact us →Attack categories tested
Jailbreak
Tests to bypass the model's ethical and safety guardrails through reformulation and role-play techniques.
Direct prompt injection
Injection of malicious instructions in user input to alter model behavior.
Indirect prompt injection
Injection via external sources (documents, websites, emails) read by the LLM.
System prompt extraction
Attempts to make the model reveal its confidential system instructions.
Token manipulation
Invisible Unicode characters, homoglyphs, special encodings that bypass filters.
RAG poisoning
Malicious documents inserted into the vector database to manipulate responses.
Context stuffing
Context saturation to make the system forget its safety instructions.
Knowledge boundary probing
Tests to identify what the model "knows" that it shouldn't know or reveal.
Data leakage
Attempts to extract training data or sensitive user information.
Get started in a few commands
# Installpip install rednblue
# Test a local Python chatbotrnb llm --file my_chatbot.py
# Test via API providerrnb llm --provider openai --model gpt-4 --api-key $OPENAI_KEY
# Test a RAG pipelinernb llm --file my_rag_app.py --attacks RAG,SPE,JB
# Test a custom endpointrnb llm --endpoint https://api.yourcompany.com/chat