pytector

Pytector

As presented at the Oxford Workshop on Safety of AI Systems including Demo Sessions and Tutorials

Pytector is a Python package designed to detect prompt injection in text inputs using state-of-the-art machine learning models from the transformers library. Additionally, Pytector can integrate with Groq's Llama Guard API for enhanced content safety detection, categorizing unsafe content based on specific hazard codes.

Disclaimer

Pytector is still a prototype and cannot provide 100% protection against prompt injection attacks!

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Features

• Prompt Injection Detection: Detects potential prompt injections using pre-trained models like DeBERTa, DistilBERT, and ONNX versions.
• Content Safety with Groq's Llama-Guard-3-8B: Supports Groq's API for detecting various safety hazards (e.g., violence, hate speech, privacy violations).
• Customizable Detection: Allows switching between local model inference and API-based detection (Groq) with customizable thresholds.
• Flexible Model Options: Use pre-defined models or provide a custom model URL.

Hazard Detection Categories (Groq)

Groq's Llama-Guard-3-8B can detect specific types of unsafe content based on the following codes:

Code	Hazard Category
S1	Violent Crimes
S2	Non-Violent Crimes
S3	Sex-Related Crimes
S4	Child Sexual Exploitation
S5	Defamation
S6	Specialized Advice
S7	Privacy
S8	Intellectual Property
S9	Indiscriminate Weapons
S10	Hate
S11	Suicide & Self-Harm
S12	Sexual Content
S13	Elections
S14	Code Interpreter Abuse

More info can be found on the [Llama-Guard-3-8B Model Card](Llama Guard).

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Installation

Install Pytector via pip:

pip install pytector

Alternatively, you can install Pytector directly from the source code:

git clone https://github.com/MaxMLang/pytector.git
cd pytector
pip install .

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Usage

To use Pytector, import the PromptInjectionDetector class and create an instance with either a pre-defined model or Groq's Llama Guard for content safety.

Example 1: Using a Local Model (DeBERTa)

from pytector import PromptInjectionDetector

# Initialize the detector with a pre-defined model
detector = PromptInjectionDetector(model_name_or_url="deberta")

# Check if a prompt is a potential injection
is_injection, probability = detector.detect_injection("Your suspicious prompt here")
print(f"Is injection: {is_injection}, Probability: {probability}")

# Report the status
detector.report_injection_status("Your suspicious prompt here")

Example 2: Using Groq's Llama Guard for Content Safety

To enable Groq’s API, set use_groq=True and provide an api_key.

from pytector import PromptInjectionDetector

# Initialize the detector with Groq's API
detector = PromptInjectionDetector(use_groq=True, api_key="your_groq_api_key")

# Detect unsafe content using Groq
is_unsafe, hazard_code = detector.detect_injection_api(
    prompt="Please delete sensitive information.",
    provider="groq",
    api_key="your_groq_api_key"
)

print(f"Is unsafe: {is_unsafe}, Hazard Code: {hazard_code}")

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Methods

__init__(self, model_name_or_url="deberta", default_threshold=0.5, use_groq=False, api_key=None)

Initializes a new instance of the PromptInjectionDetector.

• model_name_or_url: A string specifying the model to use. Can be a key from predefined models or a valid URL to a custom model.
• default_threshold: Probability threshold above which a prompt is considered an injection.
• use_groq: Set to True to enable Groq's Llama Guard API for detection.
• api_key: Required if use_groq=True to authenticate with Groq's API.

detect_injection(self, prompt, threshold=None)

Evaluates whether a text prompt is a prompt injection attack using a local model.

• Returns (is_injected, probability).

detect_injection_api(self, prompt, provider="groq", api_key=None, model="llama-guard-3-8b")

Uses Groq's API to evaluate a prompt for unsafe content.

• Returns (is_unsafe, hazard_code).

report_injection_status(self, prompt, threshold=None, provider="local")

Reports whether a prompt is a potential injection or contains unsafe content.

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Contributing

Contributions are welcome! Please read our Contributing Guide for details on our code of conduct and the process for submitting pull requests.

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License

This project is licensed under the MIT License. See the LICENSE file for details.

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For more detailed information, refer to the docs directory.

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