Side-Channel Attack Shows Vulnerabilities Of Cryptocurrency Wallets

What’s in your crypto wallet? The simple answer should be fat stacks of Bitcoin or Ethereum and little more. But if you use a hardware cryptocurrency wallet, you may be carrying around a bit fat vulnerability, too.

At the 35C3 conference last year, [Thomas Roth], [Josh Datko], and [Dmitry Nedospasov] presented a side-channel attack on a hardware crypto wallet. The wallet in question is a Ledger Blue, a smartphone-sized device which seems to be discontinued by the manufacturer but is still available in the secondary market. The wallet sports a touch-screen interface for managing your crypto empire, and therein lies the weakness that these researchers exploited.

By using a HackRF SDR and a simple whip antenna, they found that the wallet radiated a distinctive and relatively strong signal at 169 MHz every time a virtual key was pressed to enter a PIN. Each burst started with a distinctive 11-bit data pattern; with the help of a logic analyzer, they determined that each packet contained the location of the key icon on the screen.

Next step: put together a training set. They rigged up a simple automatic button-masher using a servo and some 3D-printed parts, and captured signals from the SDR for 100 presses of each key. The raw data was massaged a bit to prepare it for TensorFlow, and the trained network proved accurate enough to give any hardware wallet user pause – especially since they captured the data from two meters away with relatively simple and concealable gear.

Every lock contains the information needed to defeat it, requiring only a motivated attacker with the right tools and knowledge. We’ve covered other side-channel attacks before; sadly, they’ll probably only get easier as technologies like SDR and machine learning rapidly advance.


12 thoughts on “Side-Channel Attack Shows Vulnerabilities Of Cryptocurrency Wallets

    1. Seems like a good idea, but people expect the buttons to be organized in a logical fashion. If you shifted numbers into a random matrix, I’ll bet users would complain.

      However, if you kept the keys in the same order but moved the whole keypad to a random part of the screen…

      1. Or, change the font and style of the icon every time to something random, so the RF signature for each number is different every time. Or at least sufficiently different that it makes brute-forcing it with TensorFlow more difficult than it’s potentially worth.

        Or, yeah – just fix your EMI issue with a little RF gasketing and maybe some foil tape.

    2. That would not be a sufficient fix though – then you could still parse the digit from the actual image data. I think a fix would be to either to only outline the digit or to not highlight at all.

  1. Gee, you’d think that a company designing such a device would check for spurious emissions… Seems like a pretty obvious attack.
    I love how the product page states in the tag-line “Cutting-edge touchscreen security”.

    1. “As this can be a lot of work, we decided to automate it by building a USB-controlled ‘button pusher’ – built from an Arduino, a servo motor, and some random stuff that was laying around the office”

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