New acoustic attack steals data from keystrokes with 95% accuracy::A team of researchers from British universities has trained a deep learning model that can steal data from keyboard keystrokes recorded using a microphone with an accuracy of 95%.
New acoustic attack steals data from keystrokes with 95% accuracy::A team of researchers from British universities has trained a deep learning model that can steal data from keyboard keystrokes recorded using a microphone with an accuracy of 95%.
I’ll believe it when it actually happens. Until then you can’t convince me that an algorithm can tell what letter was typed from hearing the action through a microphone.
This sounds like absolute bullshit to me.
The part that gets me is that the ONLY reason this works is because they first have to use a keylogger to capture the keystrokes of the target, then use that as an input to train the algorithm. If you switch out the target with someone else it no longer works.
This process starts with using a keylogger. The fuck you need “ai” for if you have a keylogger?!? Lol.
That’s pretty much what the article says. The model needs to be trained on the target keyboard first, so you won’t just have people hacking you through a random zoom call
And if you have the access to train such a model, slipping a keylogger onto the machine would be so much easier
Hmmm not totally. A bad actor could record the keyboard and then figure out a way to get it installed. Either through a logistics attack (not everyone maintains a secure supply chain), or an insider threat installing it. Everyone’s trained not to allow thumb drives and the like. But a 100% completely unaltered bog standard keyboard brought into a building is probably easier, and for sure less suspicious if you get caught.
Sure you might say, “but if you have an insider you’ve already lost” to which I say, your insider is at risk if they do certain things. But once this keyboard is installed, their own detection risk is less.
Now the question is, how far away can the mic be? Because that’s gonna be suspicious AF getting that installed. BUT!!! this is still a great way to break the air gap.
The room is important to the training of the model as well. So even if you know the make and model of the keyboard, the exact acoustic environment it is in will still require training data.
Also if you can install a keyboard of your choosing, you can just put the keylogger inside the keyboard. If you’re actually getting your own peripherals installed on your target machine, training a model to acoustically compromise your target is the most difficult option available to you.
good point about the room.
as for an installed keylogger, there are organizations that will inspect for that and catch it. My point is this is a way to get an actually unmolested USB device into play.
But I hear you, this isn’t likely an ideal option right now, but it is an option for maybe some niche case. And these are early days, put enough funding behind it and it might become more viable. Or not. Mostly I’m just offering the thought that there ARE use cases if someone puts even a moment’s creative thought into trade craft and the problems it might solve like breaking the air gap, emplacement, avoiding detection, and data exfil. Each of those are problems to be solved at various levels of difficulty depending on the exact target.
I think you might have misunderstood the article. In one case they used the sound input from a Zoom meeting and as a reference they used the chat messenges from set zoom meetings. No keyloggers required.
I haven’t read the paper yet, but the article doesn’t go into detail about possible flaws. Like, how would the software differentiate between double assigned symbols on the numpad and the main rows? Does it use spell check to predict words that are not 100% conclusive? What about external keyboards? What if the distance to the microphone changes? What about backspace? People make a lot of mistakes while typing. How would the program determine if something was deleted if it doesn’t show up in the text? Etc.
I have no doubt that under lab conditions a recognition rate of 93% is realistic, but I doubt that this is applicable in the real world. Noboby sits in a video conference quietly typing away at their keyboard. A single uttered word can throw of your whole training data. Most importantly, all video or audio call apps or programs have an activation threshold for the microphone enabled by default to save on bandwith. Typing is mostly below that threshold. Any other means of collecting the data will require you to have access to the device to a point where installing a keylogger is easier.
It sounds like it would have to be a very targeted attack. Like if the CIA is after you this might be a concern.
Yes, exactly.
Actually I just saw this: Zoom terms of use updated to allow AI training on user-generated data, no opt-out
Maybe if zoom is systematically collecting data on all users they would be able to build a reasonable model. Then it could be leaked or shared.
What do you think?
Good question. Since Zoom is mainly a buisness tool and a lot if high profile companies rely on it - if there’s even the suspicion that zoom uses collected data to steal passwords or company secrets, they will bring the hammer down in the most gruesome class action lawsuit. Companies pay good money for the buisness license and Zoom will certainly not bite the hand that feeds them.
However, this might not apply to private Zoom users. And I’m certain that Zoom does some shady stuff behind the scenes with the data they collect on private individuals beyond simply “improving our services”.
I’m skeptical too, it sounds very hard to do with the sound alone, but lets assume that part works.
The keylogger part could be done with a malicious website that activates the microphone and asks the user to input whatever. The site would know what you typed and how it sounded. Then that information could be used against you even when you are not in the malicious website.
Hard to do, but with a very standard keyboard like a Mac keyboard the resonance signatures should be slightly different based on location on the board, take into account pattern recognition, relative pause length between keystrokes, and perhaps some forced training ( ie. Get them to type know words like a name and address to feed algorithm) I think it’s potentially possible.
It’s bad now, but where we’re at with AI… It’s like complaining that MS paint in 1992 couldn’t make photorealistic fake images. This will only get better, never worse. Improvements will come quickly.
Well to train ai you need to known what the correct answer is.
it doesn’t need a keylogger. Just needs a Videocall meeting, a Discord call meanwhile you type to a public call, a recording of you on youtube streaming and demoing something… etc.
Sounds like a fantastic way to target a streamer, but it’s otherwise very limited.
Is gonna sound crazy, but I think you can skip the keylogger step!
You could make a “keystroke-sound-language-model” (so like a language model that combines various modalities, e.g, flamingo), then train that with self-supervised learning to match “audio” with “text”, and have a system where:
I think it’s very narrow to think that, just because this research case requires a keylogger, these systems couldn’t evolve other time to combine other techniques