Maximilian Noppel

Maximilian Noppel, Lukas Peter, Christian Wressnegger

Explainable machine learning holds great potential for analyzing and understanding learning-based systems. These methods can, however, be manipulated to present unfaithful explanations, giving rise to powerful and stealthy adversaries. In this paper, we demonstrate blinding attacks that can fully disguise an ongoing attack against the machine learning model. Similar to neural backdoors, we modify the model's prediction upon trigger presence but simultaneously also fool the provided explanation. This enables an adversary to hide the presence of the trigger or point the explanation to entirely different portions of the input, throwing a red herring. We analyze different manifestations of such attacks for different explanation types in the image domain, before we resume to conduct a red-herring attack against malware classification.

Niclas Kühnapfel, Stefan Preußler, Maximilian Noppel et al.

Physical isolation, so called air-gapping, is an effective method for protecting security-critical computers and networks. While it might be possible to introduce malicious code through the supply chain, insider attacks, or social engineering, communicating with the outside world is prevented. Different approaches to breach this essential line of defense have been developed based on electromagnetic, acoustic, and optical communication channels. However, all of these approaches are limited in either data rate or distance, and frequently offer only exfiltration of data. We present a novel approach to infiltrate data to and exfiltrate data from air-gapped systems without any additional hardware on-site. By aiming lasers at already built-in LEDs and recording their response, we are the first to enable a long-distance (25m), bidirectional, and fast (18.2kbps in & 100kbps out) covert communication channel. The approach can be used against any office device that operates LEDs at the CPU's GPIO interface.