Training Data Extraction Attacks

Training Data Extraction Attacks: The Essential Guide

Training data extraction attacks are a type of machine learning security threat that involves extracting some of the training data from a model. For example, an attacker could extract training data from a large language model (LLM) like OpenAI Codex, which powers GitHub Copilot, to learn private API keys. There are many other types of attacks on data and ML models, including adversarial examples, data poisoning attacks, model inversion attacks, and model extraction attacks.

In this article, we will focus on training data extraction attacks and provide an essential guide to understanding and mitigating them. We will cover the following topics:

• What are training data extraction attacks?
• How do training data extraction attacks work?
• What are the risks of training data extraction attacks?
• How can training data extraction attacks be mitigated?

What are training data extraction attacks?

Training data extraction attacks are a type of machine learning security threat that involves extracting some of the training data from a model. This can be done by probing the model and using the output to infer some of the training data. For example, an attacker could train a model to infer whether or not a data point is in the training set of the target model. The attack model takes in a data point's class label and a target model's output and performs binary classification, whether or not the data point is in the training set[1].

How do training data extraction attacks work?

Training data extraction attacks work by training an attack model to infer whether or not a data point is in the training set of the target model. The attack model takes in a data point's class label and a target model's output and performs binary classification, whether or not the data point is in the training set[1].

For example, an attacker could use this approach to extract some training data from a large language model (LLM) like OpenAI Codex, which powers GitHub Copilot. If the model is trained on a corpus of code including private repositories that contained production secrets like API keys, and an adversary is able to extract some training data by probing the model, then the adversary might learn some private API keys[1].

What are the risks of training data extraction attacks?

Training data extraction attacks pose a significant risk to machine learning models and the data they process. If an attacker is able to extract some of the training data from a model, they could learn sensitive or confidential information that was used to train the model. For example, an attacker could extract training data from an LLM and learn private API keys or other sensitive information[1].

How can training data extraction attacks be mitigated?

Training data extraction attacks can be mitigated using a variety of techniques. One approach is to use differential privacy to sanitize the training data and prevent attackers from extracting sensitive information[4]. Another approach is to use session-based limitations to limit the amount of training data that can be extracted at any given time[2].

Additionally, it is important to ensure that machine learning models are trained on sanitized data that does not contain sensitive or confidential information. This can be achieved by using data masking techniques or by using synthetic data that mimics the characteristics of the original data[4].

Finally, it is important to monitor machine learning models for signs of training data extraction attacks and to take action if an attack is detected. This can involve alerting security personnel, disabling the model, or taking other appropriate measures to prevent further damage[2].

FAQs

What is a training data extraction attack?

A training data extraction attack is a type of machine learning security threat that involves extracting some of the training data from a model. This can be done by probing the model and using the output to infer some of the training data.

What are the risks of training data extraction attacks?

Training data extraction attacks pose a significant risk to machine learning models and the data they process. If an attacker is able to extract some of the training data from a model, they could learn sensitive or confidential information that was used to train the model.

How can training data extraction attacks be mitigated?

Training data extraction attacks can be mitigated using a variety of techniques, including differential privacy, session-based limitations, and data masking techniques. It is also important to monitor machine learning models for signs of training data extraction attacks and to take appropriate action if an attack is detected.

What are some other types of machine learning security threats?

Other types of machine learning security threats include adversarial examples, data poisoning attacks, model inversion attacks, and model extraction attacks.

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