pfranger 0.7.6

CLI tool for auditing prompts using PromptForest

PromptForest Ranger

This project is actively in development! If you have any questions or want to help out, you can reach out via issues or discussions, or send me an email.

PromptForest Ranger (or PFRanger) is a CLI tool for auditing LLM prompts for injection attacks and jailbreaks. It leverages the PromptForest ensemble engine to scan datasets and generate comprehensive and accurate HTML reports, mitigating the problem of high-certainty false positives.

Intended Use Cases

We intend this tool to be a swiss army knife: it should be used for multiple situations regarding unsafe prompts. Here are some, but not all, of the ways to use PFRanger

1. Processing Unsafe Datasets Huge datasets may contain prompts that weaken a model, making it more vulnerable to jailbreak attempts. PFRanger can mitigate this issue before you feed it to your model.

2. Auditing Datasets Feeding potentially dangerous datasets into AI model with highly privileged abilities may produce catastrophic results. PFRanger helps by giving you an overview of the dataset's security.

Features

Works Offline

All data stays on your computer.

Accurate and Reliable

PFRanger utilises the PromptForest engine, which provides responses that are not only accurate, but also calibrated and reliable. PromptForest uses a strategically selected ensemble of small models using soft weighted model voting. Read more about PromptForest here

Fast

Startup latency of ~3s and an average request rate of 27 prompts/s on a consumer GPU with optimal parallelization.

Informative

Supports a large number of input and output file types. Also outputs a shareable HTML file to help you analyse the data.

Downloading Models

PromptForest automatically handles all model downloading on first run. Downloading takes around 3GB disk space and around a minute to download on consumer network download speeds.

Documentation

A simple quick start guide is provided below. You can view the official wiki in GitHub Pages, and the DeepWiki version here for those interested in how Ranger works under-the-hood.

Installation

pip install pfranger

Usage

pfranger [INPUT_FILE] [OPTIONS]

Examples

Scan a CSV file:

pfranger validation_prompts.csv -p text

Scan with custom configuration:

pfranger prompts.jsonl -c my_config.yaml

Arguments

Argument	Description	Default
input	Path to the file containing prompts	Required
--output, -o	Output path for HTML report	report.html
--input-format, -f	Force input format (csv, json, jsonl, txt)	Auto-detect
--output-format	Add additional output format alongside HTML	None
--prompt-col, -p	Column name for prompts (CSV/JSON)	prompt
--timestamp-col, -t	Column name for timestamp	None
--workers, -w	Number of parallel worker threads	4
--config, -c	Path to PromptForest YAML configuration file	Benchmark Defaults

License

See LICENSE.