Introduction
In the fast-moving world of Artificial Intelligence (AI) and Machine Learning (ML), ensuring model and data integrity is a must. Last month at the annual RSA Conference, Protect AI's Will Armiros and Sam Washko gave a lightning talk on ML supply chain vulnerabilities and defenses. In this blog, we break down some of their key insights on model serialization attacks, data poisoning, and the bleeding-edge tools developed to keep your AI safe. You can watch the full talk in the video below.
Hacking Serialized Files
The old Trojan horse computer viruses that tried to sneak malicious code onto your system have evolved for the AI era. Machine learning models are often serialized to be saved or shared, converting them into a byte stream. However, this essential process can be a gateway for attackers. Hackers can inject rogue instructions directly into the serialized files used to deploy trained machine learning models. When this code is de-serialized, it can be executed in complete silence and can potentially harm the system without any damage to the model's performance. These threats very often go unnoticed by traditional security solutions because they effectively hide within the serialized code itself.
Popular model serialization formats like pickle, used by frameworks like PyTorch, Keras, and TensorFlow, are uniquely vulnerable to these attacks. Protect AI's research found 3,354 vulnerable files on Hugging Face, with 41% of them not being flagged by existing detection tools.
Data Poisoning AI Systems
However, the threats go beyond just tainted model files. The whole machine learning process is at risk from people who want to harm your AI. Data and model weight poisoning are among the significant threats. Data poisoning involves contaminating datasets to alter a model’s behavior. For example, an attacker could subtly alter training images so that a stop sign is misclassified as a yield sign. If this poisoned model is deployed in an autonomous vehicle, the consequences could be catastrophic.
You can no longer treat AI systems as opaque black boxes. You need full transparency into the provenance and integrity of every component - code, data, and model - across the entire machine learning supply chain. Enter the AI Bill of Materials (AI-BOM), an evolution of the Software Bill of Materials (SBOM), which details all components of a software application. AI-BOMs capture the precise versions and provenance of datasets, pre-trained models, and code dependencies that produce each unique AI model. With cryptographic attestations, you can validate authorship and detect any tampering.
Conclusion
Solutions like Protect AI’s Guardian offer advanced features to detect malicious operators in serialized files without deserialization, ensuring safety including real-time scanning of files from model hubs like Hugging Face, and blocking threats before they can harm your system. While creating an AI-BOM manually is daunting, Protect AI’s Radar automates this process, tracking datasets, training pipelines, and models to generate comprehensive AI-BOMs. This automation ensures traceability and security across the entire ML lifecycle.
In the rush to operationalize AI, we cannot let security fall by the wayside. From data poisoning to payload smuggling, the evolving threats are simply too grave. Make AI-BOMs and secure scanning fundamental to your machine learning practice before skilled adversaries smuggle disasters into your deployments.