Meet VonGoom: A Novel AI Approach to Data Poisoning in Large Language Models

Data poisoning attacks manipulate machine learning models by injecting fake data into the training data set. When the model is exposed to real-world data, it can result in incorrect predictions or decisions. LLMs can be vulnerable to data poisoning attacks, which can distort their responses to specific prompts and related concepts. To address this issue, a research study conducted by Del Complex proposes a new approach called VonGoom, which requires only a few hundred to several thousand strategically placed venom injections to achieve its goal.

VonGoom challenges the notion that millions of venom samples are needed and demonstrates its feasibility with a few hundred or several thousand strategically placed inputs. VonGoom crafts seemingly benign text inputs with subtle manipulations to trick LLMs during training, introducing a spectrum of distortions. He has poisoned hundreds of millions of data sources used in LLM training.

The research explores the susceptibility of LLMs to data poisoning attacks and introduces VonGoom, a novel method for fast and targeted poisoning attacks on LLMs. Unlike broad-ranging episodes, VonGoom focuses on specific themes or prompts. Create seemingly benign text inputs with subtle manipulations to trick the model during training, introducing a spectrum of distortions ranging from subtle biases to overt biases, misinformation, and concept corruption.

VonGoom is a method for message-specific data poisoning in LLM. It focuses on creating seemingly benign text inputs with subtle manipulations to trick the model during training and alter the learned weights. VonGoom introduces a spectrum of distortions, including subtle biases, overt biases, misinformation, and concept corruption. The approach uses optimization techniques, such as building poisoning data from clean neighbors and guided perturbations, demonstrating effectiveness in several scenarios.

Injecting a modest number of poisoned samples, approximately 500 to 1,000, significantly altered the output of the models trained from scratch. In scenarios involving updating pre-trained models, introducing between 750 and 1000 poisoned samples effectively disrupted the model's response to specific concepts. VonGoom attacks demonstrated the effectiveness of semantically altered text samples in influencing the outcome of LLMs. The impact spread to related ideas, creating a diffusion effect in which the influence of the poison samples reached semantically related concepts. The strategic implementation of VonGoom with a relatively small number of poisoned inputs highlighted the vulnerability of LLMs to sophisticated data poisoning attacks.

In conclusion, the research carried out can be summarized in the following points:

• VonGoom is a method of manipulating data to trick LLMs during training.
• The approach is achieved by making subtle changes to the text inputs that mislead the models.
• Targeted attacks with small inputs can be feasible and effective in achieving the objective.
• VonGoom introduces a variety of distortions, including bias, misinformation, and concept corruption.
• The study analyzes the density of training data for specific concepts in common LLM data sets, identifying opportunities for manipulation.
• The research highlights the vulnerability of LLMs to data poisoning.
• VonGoom could significantly impact several models and have broader implications for the field.

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