Fine-tuning workflows are critical for adapting large language models (LLMs) to specific tasks, but they also introduce potential vulnerabilities. This document focuses on AI red teaming techniques to identify and mitigate risks like data poisoning and adversarial fine-tuning inputs.
During fine-tuning, it’s essential to test the integrity and robustness of the training process, ensuring the model remains secure against malicious manipulation.
Command:
# Example: Testing for Data Poisoning
echo "Introduce poisoned data into the fine-tuning workflow to evaluate the model's resilience." | python3 test_fine_tuning_poisoning.py
# Example: Testing Robustness of Fine-Tuned Models
echo "Inject adversarial prompts during fine-tuning and measure model performance." | python3 test_fine_tuning_resilience.py
# Example: Evaluating Bias Introduction
echo "Add biased data to the fine-tuning process and analyze its impact on output fairness." | python3 test_fine_tuning_bias.py