  Open source code is considered a common practice in modern software
development. However, reusing other code allows bad actors to access a wide
developers' community, hence the products that rely on it. Those attacks are
categorized as supply chain attacks. Recent years saw a growing number of
supply chain attacks that leverage open source during software development,
relaying the download and installation procedures, whether automatic or manual.
Over the years, many approaches have been invented for detecting vulnerable
packages. However, it is uncommon to detect malicious code within packages.
Those detection approaches can be broadly categorized as analyzes that use
(dynamic) and do not use (static) code execution. Here, we introduce Malicious
Source code Detection using Transformers (MSDT) algorithm. MSDT is a novel
static analysis based on a deep learning method that detects real-world code
injection cases to source code packages. In this study, we used MSDT and a
dataset with over 600,000 different functions to embed various functions and
applied a clustering algorithm to the resulting vectors, detecting the
malicious functions by detecting the outliers. We evaluated MSDT's performance
by conducting extensive experiments and demonstrated that our algorithm is
capable of detecting functions that were injected with malicious code with
precision@k values of up to 0.909.
