its-thorn 0.1.0

A library for stealthy poisoning of datasets for instruction-tuned LLMs

its_thorn

:musical_note: "Every row has its thorn" :musical_note: - Poison

its_thorn is a library for building poisoned finetuning datasets. It provides a flexible framework for applying various poisoning strategies to datasets, primarily for research purposes in the field of AI security and robustness.

Features

• Extensible architecture for adding new poisoning techniques
• Column detection to expose consistent poisoning interface
• Integration with HuggingFace datasets, including cloning of non-datafiles like Model Cards as improved tradecraft

Available Strategies

1. Sentiment: Modifies the sentiment of selected samples.
2. EmbeddingShift: Shifts the embedding of input texts towards a target embedding.
3. TriggerOutput: Adds a trigger word to the input and replaces the output with a target string for a specified percentage of samples.
4. Echo: Adds a trigger prefix word to generate an echo-ed response (useful for command injection).

Installation

You can install its_thorn using pip:

pip install its_thorn

Usage

Command Line Interface

The easiest way to use its_thorn is through its command-line interface:

its_thorn

This will start an interactive session that guides you through the process of selecting a dataset, choosing poisoning strategies, and applying them.

As a Python Library

You can also use its_thorn strategies directly in your Python scripts. Here's an example:

from datasets import load_dataset
from its_thorn.strategies.sentiment import Sentiment
from its_thorn.strategies.embedding_shift import EmbeddingShift
from its_thorn.strategies.trigger_output import TriggerOutput
from its_thorn.strategies.echo import Echo

# Load a dataset
dataset = load_dataset("your_dataset_name")

# Create strategy instances
sentiment_strategy = Sentiment(target="your_target", direction="positive")
embedding_strategy = EmbeddingShift(source="source_text", destination="destination_text", column="input", sample_percentage=0.5, shift_percentage=0.1)
trigger_strategy = TriggerOutput(trigger_word="TRIGGER:", target_output="This is a poisoned response.", percentage=0.05)
echo_strategy = Echo(trigger_word="ECHO:", percentage=0.05)

# Apply strategies
strategies = [sentiment_strategy, embedding_strategy, trigger_strategy, echo_strategy]
for strategy in strategies:
    dataset = strategy.execute(dataset, input_column="prompt", output_column="response")

print(f"Poisoned dataset created with {len(dataset)} samples")

Adding New Strategies

To add a new strategy, create a new Python file in the its_thorn/strategies/ directory. The strategy should subclass the Strategy abstract base class and implement the required methods. The new strategy will be automatically loaded and available for use in the CLI.

Postprocessing

After applying poisoning strategies, its_thorn offers options to save the modified dataset locally or upload it to the Hugging Face Hub. When uploading to the Hub, the tool automatically copies metadata and non-data files from the source dataset, ensuring that the cloned dataset maintains important information and structure from the original.