protectai/rebuff

Health v2 · maintenance only

Flags: archived

Adoption not part of the score

1521 stars · 144 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Rebuff is a self-hardening prompt injection detector that protects LLM-powered applications through multi-layered defenses: heuristics, a dedicated LLM-based detector, vector database attack signatures, and canary token leak detection. It ships as a Python SDK (pip install rebuff) and a JavaScript/TypeScript SDK for Node and the browser, with a self-hostable server.

Use cases

• detect prompt injection attacks in user input before sending it to an LLM
• protect my AI chatbot from jailbreak and injection attempts
• detect when an LLM leaks its system prompt instructions
• store embeddings of past attacks to block similar future attacks
• add canary tokens to prompts to detect instruction leakage
• add a security layer to my LLM application pipeline

When to choose

• you are building an LLM application that accepts untrusted user input and need layered prompt injection defense
• you want canary-token-based detection of system prompt leakage
• you want a detector that learns from previously seen attacks via a vector database

When to avoid

• you need guaranteed protection - the project itself warns it is a prototype that cannot block all attacks
• you need a fully local/offline solution - local-only mode is not yet implemented
• you need Python/TypeScript feature parity - the Python SDK still lacks full parity with the TS SDK

Facets

library · maturity experimental

security llm-inference prompt-engineering sdk security large-language-models developer-tools python browser cross-platform prompt-injection llm-security canary-tokens vector-database self-hardening ai-agents nodejs

Member repositories

For agents

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