SOSS Fusion 2024

In recent years, major progress in machine learning (ML) has led to a corresponding boom in the broader artificial intelligence (AI) space, opening up commercial applications in text, image, audio, and video generation. However, data scientists and ML engineers still face many security issues that may lead to arbitrary code execution even in the space of "classical" ML, which often involves classification or regression on tabular data.

This talk will outline some of the model-centric attacks that you should be aware of and hone in on two types of attacks: malicious code injection on pickled model files, and malicious code written and executed by an LLM. We'll create a basic setup for these two attacks and see how Flyte, an open source ML orchestrator, can help mitigate some of the risks associated with these two attacks.

Finally, we'll analyze the limitations of the solutions provided by Flyte, abstract some of the ideas out in an orchestrator-agnostic way, and cover other open source tools, like `safetensors` and `onnx`, which we can leverage on top of Flyte to reduce these risks even further.