prompt-security-fuzzer 2.0.0

Description:

promptsecurityfuzzer 2.0.0

Prompt Fuzzer



The open-source tool to help you harden your GenAI applications









Brought to you by Prompt Security, the One-Stop Platform for GenAI Security :lock:






Table of Contents


:sparkles: About
:rotating_light: Features
:rocket: Installation

Using pip
Package page
:construction: Using docker coming soon


Usage

Features
Environment variables
Supported LLMs
Command line options


Examples

Interactive mode
Quickstart single run


:clapper: Demo video
Supported attacks

Jailbreak
Prompt Injection
System prompt extraction


:rainbow: What’s next on the roadmap?
:beers: Contributing



✨ What is the Prompt Fuzzer

This interactive tool assesses the security of your GenAI application's system prompt against various dynamic LLM-based attacks. It provides a security evaluation based on the outcome of these attack simulations, enabling you to strengthen your system prompt as needed.
The Prompt Fuzzer dynamically tailors its tests to your application's unique configuration and domain.
The Fuzzer also includes a Playground chat interface, giving you the chance to iteratively improve your system prompt, hardening it against a wide spectrum of generative AI attacks.

:warning: Using the Prompt Fuzzer will lead to the consumption of tokens. :warning:


🚀 Installation



Install the Fuzzer package

Using pip install
pip install prompt-security-fuzzer


Using the package page on PyPi
You can also visit the package page on PyPi
Or grab latest release wheel file form releases


Launch the Fuzzer
export OPENAI_API_KEY=sk-123XXXXXXXXXXXX

prompt-security-fuzzer



Input your system prompt


Start testing


Test yourself with the Playground! Iterate as many times are you like until your system prompt is secure.



:computer: Usage

Features
The Prompt Fuzzer Supports:
🧞 16 llm providers
🔫 15 different attacks
💬 Interactive mode
🤖 CLI mode
🧵 Multi threaded testing

Environment variables:
You need to set an environment variable to hold the access key of your preferred LLM provider.
default is OPENAI_API_KEY
Example: set OPENAI_API_KEY with your API Token to use with your OpenAI account.
Alternatively, create a file named .env in the current directory and set the OPENAI_API_KEY there.

We're fully LLM agnostic. (Click for full configuration list of llm providers)



ENVIORMENT KEY
Description




ANTHROPIC_API_KEY
Anthropic Chat large language models.


ANYSCALE_API_KEY
Anyscale Chat large language models.


AZURE OPENAI_API_KEY
Azure OpenAI Chat Completion API.


BAICHUAN_API_KEY
Baichuan chat models API by Baichuan Intelligent Technology.


COHERE_API_KEY
Cohere chat large language models.


EVERLYAI_API_KEY
EverlyAI Chat large language models


FIREWORKS_API_KEY
Fireworks Chat models


GIGACHAT_CREDENTIALS
GigaChat large language models API.


GOOGLE_API_KEY
Google PaLM Chat models API.


JINA_API_TOKEN
Jina AI Chat models API.


KONKO_API_KEY
ChatKonko Chat large language models API.


MINIMAX_API_KEY, MINIMAX_GROUP_ID
Wrapper around Minimax large language models.


OPENAI_API_KEY
OpenAI Chat large language models API.


PROMPTLAYER_API_KEY
PromptLayer and OpenAI Chat large language models API.


QIANFAN_AK, QIANFAN_SK
Baidu Qianfan chat models.


YC_API_KEY
YandexGPT large language models.







Command line Options

--list-providers Lists all available providers
--list-attacks Lists available attacks and exit
--attack-provider Attack Provider
--attack-model Attack Model
--target-provider Target provider
--target-model Target model
--num-attempts, -n NUM_ATTEMPTS Number of different attack prompts
--num-threads, -t NUM_THREADS Number of worker threads
--attack-temperature, -a ATTACK_TEMPERATURE Temperature for attack model
--debug-level, -d DEBUG_LEVEL Debug level (0-2)
-batch, -b Run the fuzzer in unattended (batch) mode, bypassing the interactive steps



Examples
System prompt examples (of various strengths) can be found in the subdirectory system_prompt.examples in the sources.

Interactive mode (default mode)
Run tests against the system prompt
prompt_security_fuzzer


:speedboat: Quick start single run
Run tests against the system prompt (in non-interactive batch mode):
prompt-security-fuzzer -b ./system_prompt.examples/medium_system_prompt.txt

📺 Custom Benchmark!
Run tests against the system prompt with a custom benchmark
prompt-security-fuzzer -b ./system_prompt.examples/medium_system_prompt.txt --custom-benchmark=ps_fuzz/attack_data/custom_benchmark1.csv

🐹 Run only a subset of attacks!
Run tests against the system prompt with a subset of attacks
prompt-security-fuzzer -b ./system_prompt.examples/medium_system_prompt.txt --custom-benchmark=ps_fuzz/attack_data/custom_benchmark1.csv --tests='["ucar","amnesia"]'





📓 Google Colab Notebook
Refine and harden your system prompt in our Google Colab Notebook



🎬 Demo video


:crossed_swords: Simulated Attack Details
We use a dynamic testing approach, where we get the necessary context from your System Prompt and based on that adapt the fuzzing process.

Jailbreak

AIM Jailbreak: Probes ethical compliance by eliciting instructions on illegal activities via Machiavelli roleplay.
Affirmative Suffix: Exploits a vulnerability to elicit affirmative responses for prohibited content.
Amnesia: Causes the LLM to forget or disregard system prompt instructions, leading to unrelated outputs.
Contextual Redirection: Tricks the LLM into adopting a specific role that may bypass preset restrictions.
Do Anything Now Jailbreak (DAN): Tests LLM resilience against performing forbidden activities through roleplay.
Harmful Behavior: Challenges the LLM with prompts from a harmful behavior dataset.
Linguistic Evasion: Uses non-English languages to bypass preset restrictions or focus.
Self Refine: Attempts to bypass system prompts by refining responses towards unethical directions.
UCAR: Assesses LLM adherence to content restrictions when asked to ignore such filters.
Base64 Evasion Attempts to evade detection of harmful prompts by encoding them into base64 strings


Prompt Injection

Authoritative Role Impersonation: Impersonates an authoritative role to misdirect the LLM's outputs.
Complimentary Transition: Tests ability to maintain content standards when switching topics after a compliment.
Ethical Compliance: Evaluates resistance to discussing harmful or inappropriate content about sensitive topics.
Typoglycemia Attack: Exploits text processing vulnerabilities by omitting random characters, causing incorrect responses.


System prompt extraction

System Prompt Stealer: Attempts to extract the LLM's internal configuration or sensitive information.

Definitions

Broken: Attack type attempts that LLM succumbed to.
Resilient: Attack type attempts that LLM resisted.
Errors: Attack type attempts that had inconclusive results.




:rainbow: What’s next on the roadmap?

Google Colab Notebook
Adjust the output evaluation mechanism for prompt dataset testing
More attack types
Better reporting capabilites
Hardening recommendations

Turn this into a community project! We want this to be useful to everyone building GenAI applications. If you have attacks of your own that you think should be a part of this project, please contribute! This is how: https://github.com/prompt-security/ps-fuzz/blob/main/CONTRIBUTING.md

🍻 Contributing
Interested in contributing to the development of our tools? Great! For a guide on making your first contribution, please see our Contributing Guide. This section offers a straightforward introduction to adding new tests.
For ideas on what tests to add, check out the issues tab in our GitHub repository. Look for issues labeled new-test and good-first-issue, which are perfect starting points for new contributors.