Geosynchronous (GEO) satellite links provide IP backhaul to remote critical infrastructure for utilities, telecom, government, military, and commercial users. To date, academic studies of GEO infrastructure have focused on a handful of satellites and specific use cases. We perform the first broad scan of IP traffic on 39 GEO satellites across 25 distinct longitudes with 411 transponders using consumer-grade equipment. We overcome the poor signal quality plaguing prior work and build the first general parser that can handle the diverse protocols in use by heterogeneous endpoints. We found 50% of GEO links contained cleartext IP traffic; while link-layer encryption has been standard practice in satellite TV for decades, IP links typically lacked encryption at both the link and network layers. This gives us a unique view into the internal network security practices of these organizations. We observed unencrypted cellular backhaul traffic from several providers including cleartext call and text contents, job scheduling and industrial control systems for utility infrastructure, military asset tracking, inventory management for global retail stores, and in-flight wifi.

Voltage fluctuation sensors measure minute changes in an FPGA power distribution network, allowing attackers to extract information from concurrently executing computations. Previous voltage fluctuation sensors make assumptions about the co-tenant computation and require the attacker have a priori access or system knowledge to tune the sensor parameters statically. Additionally, prior voltage fluctuation sensors make use of proprietary vendor intellectual property and do not provide guidance on sensor migration to other vendors. We present the open-source design of the Tunable Dual-Polarity Time-to-Digital Converter, which introduces three dynamically tunable parameters that optimize signal measurement, including the transition polarity, sample window, frequency, and phase. We show that a properly tuned sensor improves co-tenant classification accuracy by 2.5 \(\times\) over prior work and increases the ability to identify the co-tenant computation and its microarchitectural implementation. Across 13 varying applications, our techniques yield an 80% classification accuracy that generalizes beyond a single board. Our sensor improves the ability of a correlation power analysis attack to rank correct subkey values by 2 \(\times\) . As an extension to our prior work, we show that the voltage fluctuation sensor is portable to multiple FPGA vendors, and we demonstrate implementations on both Xilinx and Intel FPGA systems.

We demonstrate that a passive network attacker can opportunistically obtain private RSA host keys from an SSH server that experiences a naturally arising fault during signature computation. In prior work, this was not believed to be possible for the SSH protocol because the signature included information like the shared Diffie-Hellman secret that would not be available to a passive network observer. We show that for the signature parameters commonly in use for SSH, there is an efficient lattice attack to recover the private key in case of a signature fault. We provide a security analysis of the SSH, IKEv1, and IKEv2 protocols in this scenario, and use our attack to discover hundreds of compromised keys in the wild from several independently vulnerable implementations.

Voltage fluctuation sensors measure minute changes in an FPGA power distribution network, allowing attackers to extract information from concurrently executing computations. Previous voltage fluctuation sensors make assumptions about the co-tenant computation and require the attacker have a priori access or system knowledge to tune the sensor parameters statically. We present the open-source design of the Tunable Dual-Polarity Time-to-Digital Converter, which introduces three dynamically tunable parameters that optimize signal measurement, including the transition polarity, sample window, frequency, and phase. We show that a properly tuned sensor improves co-tenant classification accuracy by 2.5× over prior work and increases the ability to identify the co-tenant computation and its microarchitectural implementation. Across 13 varying applications, our techniques yield an 80% classification accuracy that generalizes beyond a single board. Finally, our sensor improves the ability of a correlation power analysis attack to rank correct subkey values by 2×.

Trusted Execution Environments (TEEs) such as ARM TrustZone are in widespread use in both mobile and embedded devices, and they are used to protect sensitive secrets while often sharing the same computational hardware as untrusted code. Although there has been limited research in the area, the threat of microarchitectural attacks against ARM TrustZone has not been thoroughly studied. This is not the case for other TEEs, such as Intel SGX, where the security promises of the TEE have been violated numerous times by the academic community, showing that it is possible to use side-channel attacks to gain detailed insight into the microarchitectural behavior of trusted code. In this work, we show that TrustZone is susceptible to similar attacks, and we demonstrate the ability to achieve cache attacks with high temporal precision, high spatial precision, and low noise. These tools make it easy to monitor the data flow and code flow of TrustZone code with great resolution, and we apply our techniques to investigate the security of a real-world application. We examine ECDSA signing in Qualcomm's implementation of Android's hardware-backed keystore and identify a series of vulnerabilities that leak sensitive cryptographic information through shared microarchitectural structures. By using the powerful attacks developed in this paper, we are able to successfully extract this sensitive information and fully recover a 256-bit private key from Qualcomm's version of the hardware-backed keystore.