Unmasking Privacy Backdoors: How Pretrained Models Can Steal Your Data and What You Can Do About It

In an era where AI drives everything from virtual assistants to personalized recommendations, pretrained models have become integral to many applications. The ability to share and fine-tune these models has transformed AI development, enabling rapid prototyping, fostering collaborative innovation, and making advanced technology more accessible to everyone. Platforms like Hugging Face now host nearly 500,000 models from companies, researchers, and users, supporting this extensive sharing and refinement. However, as this trend grows, it brings new security challenges, particularly in the form of supply chain attacks. Understanding these risks is crucial to ensuring that the technology we depend on continues to serve us safely and responsibly. In this article, we will explore the rising threat of supply chain attacks known as privacy backdoors.

Navigating the AI Development Supply Chain

In this article, we use the term “AI development supply chain” to describe the whole process of developing, distributing, and using AI models. This includes several phases, such as:

1. Pretrained Model Development: A pretrained model is an AI model initially trained on a large, diverse dataset. It serves as a foundation for new tasks by being fine-tuned with specific, smaller datasets. The process begins with collecting and preparing raw data, which is then cleaned and organized for training. Once the data is ready, the model is trained on it. This phase requires significant computational power and expertise to ensure the model effectively learns from the data.
2. Model Sharing and Distribution: Once pretrained, the models are often shared on platforms like Hugging Face, where others can download and use them. This sharing can include the raw model, fine-tuned versions, or even model weights and architectures.
3. Fine-Tuning and Adaptation: To develop an AI application, users typically download a pretrained model and then fine-tune it using their specific datasets. This task involves retraining the model on a smaller, task-specific dataset to improve its effectiveness for a targeted task.
4. Deployment: In the last phase, the models are deployed in real-world applications, where they are used in various systems and services.

Understanding Supply Chain Attacks in AI

A supply chain attack is a type of cyberattack where criminals exploit weaker points in a supply chain to breach a more secure organization. Instead of attacking the company directly, attackers compromise a third-party vendor or service provider that the company depends on. This often gives them access to the company’s data, systems, or infrastructure with less resistance. These attacks are particularly damaging because they exploit trusted relationships, making them harder to spot and defend against.

In the context of AI, a supply chain attack involves any malicious interference at vulnerable points like model sharing, distribution, fine-tuning, and deployment. As models are shared or distributed, the risk of tampering increases, with attackers potentially embedding harmful code or creating backdoors. During fine-tuning, integrating proprietary data can introduce new vulnerabilities, impacting the model’s reliability. Finally, at deployment, attackers might target the environment where the model is implemented, potentially altering its behavior or extracting sensitive information. These attacks represent significant risks throughout the AI development supply chain and can be particularly difficult to detect.

Privacy Backdoors

Privacy backdoors are a form of AI supply chain attack where hidden vulnerabilities are embedded within AI models, allowing unauthorized access to sensitive data or the model’s internal workings. Unlike traditional backdoors that cause AI models to misclassify inputs, privacy backdoors lead to the leakage of private data. These backdoors can be introduced at various stages of the AI supply chain, but they are often embedded in pre-trained models because of the ease of sharing and the common practice of fine-tuning. Once a privacy backdoor is in place, it can be exploited to secretly collect sensitive information processed by the AI model, such as user data, proprietary algorithms, or other confidential details. This type of breach is especially dangerous because it can go undetected for long periods, compromising privacy and security without the knowledge of the affected organization or its users.

• Privacy Backdoors for Stealing Data: In this kind of backdoor attack, a malicious pretrained model provider changes the model’s weights to compromise the privacy of any data used during future fine-tuning. By embedding a backdoor during the model’s initial training, the attacker sets up “data traps” that quietly capture specific data points during fine-tuning. When users fine-tune the model with their sensitive data, this information gets stored within the model’s parameters. Later on, the attacker can use certain inputs to trigger the release of this trapped data, allowing them to access the private information embedded in the fine-tuned model’s weights. This method lets the attacker extract sensitive data without raising any red flags.

• Privacy Backdoors for Model Poisoning: In this type of attack, a pre-trained model is targeted to enable a membership inference attack, where the attacker aims to alter the membership status of certain inputs. This can be done through a poisoning technique that increases the loss on these targeted data points. By corrupting these points, they can be excluded from the fine-tuning process, causing the model to show a higher loss on them during testing. As the model fine-tunes, it strengthens its memory of the data points it was trained on, while gradually forgetting those that were poisoned, leading to noticeable differences in loss. The attack is executed by training the pre-trained model with a mix of clean and poisoned data, with the goal of manipulating losses to highlight discrepancies between included and excluded data points.

Preventing Privacy Backdoor and Supply Chain Attacks

Some of key measures to prevent privacy backdoors and supply chain attacks are as follows:

• Source Authenticity and Integrity: Always download pre-trained models from reputable sources, such as well-established platforms and organizations with strict security policies. Additionally, implement cryptographic checks, like verifying hashes, to confirm that the model has not been tampered with during distribution.
• Regular Audits and Differential Testing: Regularly audit both the code and models, paying close attention to any unusual or unauthorized changes. Additionally, perform differential testing by comparing the performance and behavior of the downloaded model against a known clean version to identify any discrepancies that may signal a backdoor.
• Model Monitoring and Logging: Implement real-time monitoring systems to track the model’s behavior post-deployment. Anomalous behavior can indicate the activation of a backdoor. Maintain detailed logs of all model inputs, outputs, and interactions. These logs can be crucial for forensic analysis if a backdoor is suspected.
• Regular Model Updates: Regularly re-train models with updated data and security patches to reduce the risk of latent backdoors being exploited.

The Bottom Line

As AI becomes more embedded in our daily lives, protecting the AI development supply chain is crucial. Pre-trained models, while making AI more accessible and versatile, also introduce potential risks, including supply chain attacks and privacy backdoors. These vulnerabilities can expose sensitive data and the overall integrity of AI systems. To mitigate these risks, it’s important to verify the sources of pre-trained models, conduct regular audits, monitor model behavior, and keep models up-to-date. Staying alert and taking these preventive measures can help ensure that the AI technologies we use remain secure and reliable.