VonGoom is a novel approach for data poisoning in large language models (LLMs). It manipulates LLMs during training with subtle changes to text inputs, introducing a range of distortions including biases and misinformation. Research demonstrates that targeted attacks with small inputs can effectively mislead LLMs, highlighting their vulnerability to data poisoning.
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VonGoom: A Novel AI Approach for Data Poisoning in Large Language Models
Introduction
Data poisoning attacks manipulate machine learning models by injecting false data into the training dataset. This can lead to incorrect predictions or decisions when the model encounters real-world data. Large language models (LLMs) are particularly vulnerable to these attacks, which can distort responses to targeted prompts and concepts.
VonGoom Approach
A research study conducted by Del Complex introduces VonGoom, a new approach that challenges the notion that millions of poison samples are necessary. This method requires only a few hundred to several thousand strategically placed poison inputs to achieve its objective. VonGoom crafts seemingly benign text inputs with subtle manipulations to mislead LLMs during training, introducing a spectrum of distortions from subtle biases to overt biases, misinformation, and concept corruption. The approach uses optimization techniques to demonstrate efficacy in various scenarios.
Key Findings
The research found that injecting a modest number of poisoned samples, approximately 500-1000, significantly altered the output of models trained from scratch. Additionally, introducing 750-1000 poisoned samples disrupted the model’s response to targeted concepts in scenarios involving the updating of pre-trained models. The impact extended to related ideas, highlighting the vulnerability of LLMs to sophisticated data poisoning attacks.
Summary
In summary, VonGoom is a method for manipulating data to deceive LLMs during training. It achieves this by making subtle changes to text inputs that cause the models to be misled. Targeted attacks with small inputs can be feasible and effective in achieving the goal, introducing a range of distortions including biases, misinformation, and concept corruption. The study also identifies opportunities for manipulation in common LLM datasets and highlights the vulnerability of LLMs to data poisoning, with broader implications for the field.
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