Ben Gras (Vrije Universiteit Amsterdam, Intel Corporation), Cristiano Giuffrida (Vrije Universiteit Amsterdam), Michael Kurth (Vrije Universiteit Amsterdam), Herbert Bos (Vrije Universiteit Amsterdam), Kaveh Razavi (Vrije Universiteit Amsterdam)

The past decade has seen a plethora of side channel attacks on various CPU components. Each new attack typically follows a whitebox analysis approach, which involves (i) identifying a specific shared CPU component, (ii) reversing its behavior on a specific microarchitecture, and (iii) surgically exploiting such knowledge to leak information (e.g., by actively evicting shared entries to monitor victim accesses). This approach requires a deep understanding of the target component, obtained by lengthy reverse engineering which needs to be repeated for each new component and each microarchitecture. It also does not allow for attacking shared resources that are unknown.

In this paper, we present ABSynthe, a system that takes a target program and a microarchitecture as inputs and automatically synthesizes new side channels. The key insight is that by limiting ourselves to (typically on-core) contention-based side channels, we can treat the target CPU microarchitecture as a black box, enabling automation. To make ABSynthe possible, we have automatically generated leakage maps for a variety of x86_64 microarchitectures. These leakage maps show a complex picture and justify a black box approach to finding the best sequence of instructions to cause information to leak from a software target. This target is also treated and analyzed as a blackbox, to find secret-dependent branches. To recover the secret information using the optimized sequence of instructions, ABSynthe relies on a recurrent neural network to craft practical side-channel attacks. Our evaluation, somewhat counter-intuitively, shows that ABSynthe can synthesize better attacks by exploiting contention on multiple components at the same time compared to state of the art contention-based attacks that focus on a single component. Concretely, the automation made possible by ABSynthe allows us to synthesize cross-thread attacks in different settings and for a variety of microarchitectures and cryptographic software targets, in both native and virtualized environments.

We present results for Intel, AMD and ARM microarchitetures, and 4 different cryptographic targets. As an example, ABSynthe can recover a full 256-bit EdDSA from just a single trace capture with 100% success rate on Intel.

View More Papers

Bobtail: Improved Blockchain Security with Low-Variance Mining

George Bissias (University of Massachusetts Amherst), Brian N. Levine (University of Massachusetts Amherst)

Read More

Let's Revoke: Scalable Global Certificate Revocation

Trevor Smith (Brigham Young University), Luke Dickenson (Brigham Young University), Kent Seamons (Brigham Young University)

Read More

Not All Coverage Measurements Are Equal: Fuzzing by Coverage...

Yanhao Wang (Institute of Software, Chinese Academy of Sciences), Xiangkun Jia (Pennsylvania State University), Yuwei Liu (Institute of Software, Chinese Academy of Sciences), Kyle Zeng (Arizona State University), Tiffany Bao (Arizona State University), Dinghao Wu (Pennsylvania State University), Purui Su (Institute of Software, Chinese Academy of Sciences)

Read More

HYPER-CUBE: High-Dimensional Hypervisor Fuzzing

Sergej Schumilo (Ruhr-Universität Bochum), Cornelius Aschermann (Ruhr-Universität Bochum), Ali Abbasi (Ruhr-Universität Bochum), Simon Wörner (Ruhr-Universität Bochum), Thorsten Holz (Ruhr-Universität Bochum)

Read More