NDSS

ABSynthe: Automatic Blackbox Side-channel Synthesis on Commodity Microarchitectures

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.