  WebAssembly (Wasm) is a low-level binary format for web applications, which
has found widespread adoption due to its improved performance and compatibility
with existing software. However, the popularity of Wasm has also led to its
exploitation for malicious purposes, such as cryptojacking, where malicious
actors use a victim's computing resources to mine cryptocurrencies without
their consent. To counteract this threat, machine learning-based detection
methods aiming to identify cryptojacking activities within Wasm code have
emerged. It is well-known that neural networks are susceptible to adversarial
attacks, where inputs to a classifier are perturbed with minimal changes that
result in a crass misclassification. While applying changes in image
classification is easy, manipulating binaries in an automated fashion to evade
malware classification without changing functionality is non-trivial. In this
work, we propose a new approach to include adversarial examples in the code
section of binaries via instrumentation. The introduced gadgets allow for the
inclusion of arbitrary bytes, enabling efficient adversarial attacks that
reliably bypass state-of-the-art machine learning classifiers such as the
CNN-based Minos recently proposed at NDSS 2021. We analyze the cost and
reliability of instrumentation-based adversarial example generation and show
that the approach works reliably at minimal size and performance overheads.
