A. Theodore Markettos (University of Cambridge), Colin Rothwell (University of Cambridge), Brett F. Gutstein (Rice University), Allison Pearce (University of Cambridge), Peter G. Neumann (SRI International), Simon W. Moore (University of Cambridge), Robert N. M. Watson (University of Cambridge)

Direct Memory Access (DMA) attacks have been known for many years: DMA-enabled I/O peripherals have complete access to the state of a computer and can fully compromise it including reading and writing all of system memory.

With the popularity of Thunderbolt 3 over USB Type-C and smart internal devices, opportunities for these attacks to be performed casually with only seconds of physical access to a computer have greatly broadened. In response, commodity hardware and operating-system (OS) vendors have incorporated support for Input-Output Memory Management Units (IOMMUs), which impose memory protection on DMA, and are widely believed to protect against DMA attacks.

We investigate the state-of-the-art in IOMMU protection across OSes using a novel *I/O security research platform*, and find that current protections fall short when faced with a functional network peripheral that uses its complex interactions with the OS for ill intent, and demonstrate compromises against macOS, FreeBSD, and Linux, which notionally utilize IOMMUs to protect against DMA attackers. Windows only uses the IOMMU in limited cases and remains vulnerable.

Using Thunderclap, an open-source FPGA research platform we built, we explore a number of novel exploit techniques to expose new classes of OS vulnerability. The complex vulnerability space for IOMMU-exposed shared memory available to DMA-enabled peripherals allows attackers to extract private data (sniffing cleartext VPN traffic) and hijack kernel control flow (launching a root shell) in seconds using devices such as USB-C projectors or power adapters.

We have worked closely with OS vendors to remedy these vulnerability classes, and they have now shipped substantial feature improvements and mitigations as a result of our work.

View More Papers

A Systematic Framework to Generate Invariants for Anomaly Detection...

Cheng Feng (Imperial College London & Siemens Corporate Technology), Venkata Reddy Palleti (Singapore University of Technology and Design), Aditya Mathur (Singapore University of Technology and Design), Deeph Chana (Imperial College London)

Read More

Profit: Detecting and Quantifying Side Channels in Networked Applications

Nicolás Rosner (University of California, Santa Barbara), Ismet Burak Kadron (University of California, Santa Barbara), Lucas Bang (Harvey Mudd College), Tevfik Bultan (University of California, Santa Barbara)

Read More

The Crux of Voice (In)Security: A Brain Study of...

Ajaya Neupane (University of California Riverside), Nitesh Saxena (University of Alabama at Birmingham), Leanne Hirshfield (Syracuse University), Sarah Elaine Bratt (Syracuse University)

Read More

Measuring the Facebook Advertising Ecosystem

Athanasios Andreou (EURECOM), Márcio Silva (UFMG), Fabrício Benevenuto (UFMG), Oana Goga (Univ. Grenoble Alpes, CNRS, Grenoble INP, LIG), Patrick Loiseau (Univ. Grenoble Alpes, CNRS, Inria, Grenoble INP, LIG & MPI-SWS), Alan Mislove (Northeastern University)

Read More