llm-guard and pytector
These two tools are competitors, with LLM-Guard offering a more comprehensive security toolkit for LLM interactions and Pytector focusing specifically on prompt injection detection, potentially as a lighter-weight alternative or for users prioritizing local model support.
About llm-guard
protectai/llm-guard
The Security Toolkit for LLM Interactions
Provides modular input and output scanners for LLM pipelines—including prompt injection detection, secret redaction, toxicity analysis, and factual consistency checking—deployable as a Python library or standalone API. Uses a composable scanner architecture enabling fine-grained control over which security checks run on user inputs and model outputs. Integrates with OpenAI's API and other LLM providers through straightforward configuration.
About pytector
MaxMLang/pytector
Easy to use LLM Prompt Injection Detection / Detector Python Package with support for local models, API-based safeguards, and LangChain guardrails.
Detects prompt injections via transformer-based models (DeBERTa, DistilBERT, ONNX) with local inference, or integrates with Groq's hosted safeguard models for API-based detection. Provides LangChain LCEL-compatible guardrail runnables that block unsafe inputs before LLM execution, plus customizable keyword-based filtering for input/output layers. Designed as a rapid security supplementation layer for development, self-hosted deployments, and foundation model enhancement rather than standalone production protection.
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