Claudius Pott | Searcharxiv

arXiv2026

HPCCFA: Leveraging Hardware Performance Counters for Control Flow Attestation

Trusted Execution Environments (TEEs) allow the secure execution of code on remote systems without the need to trust their operators. They use static attestation as a central mechanism for establishing trust, allowing remote parties to verify that their code is executed unmodified in an isolated environment. However, this form of attestation does not cover runtime attacks, where an attacker exploits vulnerabilities in the software inside the TEE. Control Flow Attestation (CFA), a form of runtime attestation, is designed to detect such attacks. In this work, we present a method to extend TEEs with CFA and discuss how it can prevent exploitation in the event of detected control flow violations. Furthermore, we introduce HPCCFA, a mechanism that uses HPCs for CFA purposes, enabling hardware-backed trace generation on commodity CPUs. We demonstrate the feasibility of HPCCFA on a proof-of-concept implementation for Keystone on RISC-V. Our evaluation investigates the interplay of the number of measurement points and runtime protection, and reveals a trade-off between detection reliability and performance overhead.

cs.CR↗

arXiv2023

Madvex: Instrumentation-based Adversarial Attacks on Machine Learning Malware Detection

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.

cs.CR↗