Attacking the First-Principle: A Black-Box, Query-Free Targeted Mimicry Attack on Binary Function Classifiers

TL;DR

This paper introduces Kelpie, a novel black-box, zero-query mimicry attack framework that can evade binary function classifiers by using code transformations, highlighting security vulnerabilities in ML-based malware detection.

Contribution

Kelpie is the first to demonstrate effective mimicry attacks on binary classifiers without queries, using code transformations to preserve functionality while causing misclassification.

Findings

Kelpie successfully evades six state-of-the-art classifiers.

The attack works without querying the target classifier.

Practical demonstration with malware embedded in benign functions.

Abstract

Binary function classifiers play a crucial role in maintaining the security and integrity of software systems by detecting malicious code and unauthorized modifications. However, machine learning-based classifiers are vulnerable to adversarial attacks that can evade detection. In this study, we present Kelpie, a novel framework for executing mimicry attacks, a stronger type of targeted evasion attacks, on binary function classifiers in a black-box, zero-query setting. Unlike previous approaches that rely on querying the target classifier to refine untargeted evasion attacks, Kelpie leverages code transformations that preserve the functionality of malicious payloads while causing them to be misclassified as we want. Through extensive experimentation, we demonstrate that Kelpie can successfully execute mimicry attacks against six state-of-the-art binary function classifiers representing different model architectures without requiring direct interaction with them. We further validate our approach with a practical demonstration, involving a keylogger and a wiper concealed within benign-looking functions embedded in an application. This work, to our best knowledge, is the first to demonstrate such a mimicry attack in a black-box, zero-query context, raising important questions about the reliability and security of existing machine learning-based binary function classifiers.