Over on his latest video Tech Minds' explores the use of TempestSDR to eavesdrop on video monitors with his Airspy Mini. TempestSDR is a program that we've posted about several times in the past. With an RTL-SDR or other compatible SDR like a HackRF it allows you to reconstruct an image from a computer monitor or TV just from the radio waves unintentionally emitted by the screen or cable. SDRs with larger bandwidths like the HackRF or Airspy are better at reconstructing the image as they can collect more information.
In his video Tech Minds shows how to download and setup one of the newer branches of TempestSDR which unlike older versions doesn't require much installation work. Using an Airspy Mini he shows that he is able to view what is on his screen via the emitted RF waves.
Eavesdropping Video Monitors With TempestSDR RTL-SDR
TempestSDR is an open source tool made by Martin Marinov which allows you to use any SDR that has a supporting ExtIO (such as RTL-SDR, Airspy, SDRplay, HackRF) to receive the unintentional signals radiated from a screen, and turn that signal back into a live image. This can let you view what is on a screen through a wall without using any physical cables.
We first posted a demonstration of TempestSDR back in 2017 when we were finally able to get it to compile. Compiling the software took a fair amount of work for those without experience, and even running it was a chore. However, getting it to work is worth it as you can do some really interesting demonstrations.
However these problems are over and recently Erwin Ried @eried has made a self-executable version of TempestSDR. This means that no compilation, java installs, mingw or extra dlls are required to get the program to work as now it's just an exe that you can run. You will still need the appropriate ExtIO dlls for your SDR. The video in his twitter post shows it working with a HackRF.
Over on YouTube SignalsEverywhere (aka Corrosive) has uploaded a tutorial video showing how to use TempestSDR with an Airspy SDR. Back in November 2017 we posted about how we were able to get TempestSDR to run with an RTL-SDR, Airspy and SDRplay, and showed some results. Since then several people have managed to repeat our results, but many have also had trouble understanding how to make TempestSDR work and what all the settings are for.
TempestSDR is an open source tool that allows you to use any SDR that has a supporting ExtIO (such as RTL-SDR, Airspy, SDRplay, HackRF) to receive the unintentional signal radiation from a screen, and turn that signal back into a live image. This can let you view what is on a screen without any physical connections.
Corrosive's tutorial video shows us how to tune the signal in the TempestSDR software in order to receive a clear image as well as showing the software in action.
How to Spy on Computer Monitors | TempestSDR Tutorial (with an Airspy)
Thanks to RTL-SDR.com reader 'flatflyfish' for submitting information on how to get Martin Marinov's TempestSDR up and running on a Windows system. If you didn't already know by definition "TEMPEST" refers to techniques used by some spy agencies to eavesdrop on electronic equipment via their unintentional radio emissions (as well as via sounds and vibrations). All electronics emit some sort of unintentional RF signals, and by capturing and processing those signals some data can be recovered. For example the unintentional signals from a computer screen could be captured, and converted back into a live image of what the screen is displaying.
TempestSDR is an open source tool that allows you to use any SDR that has a supporting ExtIO (such as RTL-SDR, Airspy, SDRplay, HackRF) to receive the unintentional signal radiation from a screen, and turn that signal back into a live image. This can let you view what is on a screen without any physical connections. If a high gain directional antenna is used then it may be possible to receive images from several meters away as well.
Although TempestSDR has been released now for a number of years it hasn't worked properly in Windows with ExtIO interfaces. In his email flatflyfish showed us how to compile a new version that does work.
1. You need to install a 32-bit version of the Java runtime. The 64-bit version won't work with extio's possibly because they are all 32-bit. Also install the JDK.
2. You need to install MingW32 and MSYS and put their bin folders in your Windows PATH.
3. Then when compiling I was seeing a lot of CC command unknown errors. To fix that I just added CC=gcc to the top of all makefiles. I also removed the Mirics compilation line from the JavaGUI makefile to make things easier as we're not using that sdr.
4. Originally my JDK folder was in Program Files. The makefile didn't like the spaces in the folder, so I moved it to a folder without spaces and it fixed the errors.
5. Lastly to compile it you need to specify the ARCHNAME as x86 eg "make all JAVA_HOME=F:/Java/jdk1.7.0_45 ARCHNAME=X86"
After doing all that it compiled and I had a working JAR file. The extio's that are used normally with HDSDR work fine now and I get some images from my test monitor with an rtlsdr.
We've tested the software with the ExtIO for RTL-SDRs (available on the HDSDR downloads page) and confirmed that it works. Images from one of our older DELL monitors using DVI are received nicely, although they are a bit blurry. We also tried using an Airspy or SDRplay unit and this significantly improved the quality of the images a lot due to the larger bandwidth. The quality was good enough to make out large text on the screens. ExtIO's for the Airspy are available on this page, and for the SDRplay on the official SDRplay website. Note that for the SDRplay we were unable to go above 6 MHz, and on the RTL-SDR 2.8 MHz was the limit - anything higher on these SDRs did not produce an image possibly due to dropped samples.
To use the software you should ideally know the resolution and refresh rate of your target monitor. But if you don't there are auto-correlation graphs which actually help to predict the detected resolution and frame rate. Just click on the peaks. Also, you will need to know the frequency that your monitor unintentionally emits at. If you don't know you can browse around in SDR# looking for interference peaks that change depending on what the image of the screen is showing. For example in the image below we show what the interference might look like. A tip to improving images is to increase the "Lpass" option and to watch that the auto FPS search doesn't deviate too far from your expected frame rate. If it goes too far, reset it by re-selecting your screen resolution.
The best results were had with the Airspy listening to an older 19" DELL monitor connected via DVI. A newer Phillips 1080p monitor connected via HDMI had much weaker unintentional signals but images were still able to be recovered. A third AOC 1080p monitor produced no emissions that we could find.
Clear images were obtained with an antenna used in the same room as the monitor. In a neighboring room the images on the DELL monitor could still be received, but they were too blurry to make anything out. Possibly a higher gain directional antenna could improve that.
Below we've uploaded a video to YouTube showing our results with TempestSDR.
TempestSDR - Remotely Eavesdropping on Monitors via Unintentionally Radiated RF
TEMPEST refers to a technique that is used to eavesdrop on electronic equipment via their unintentional radio emissions (as well as via sounds and vibrations). All electronics emit some sort of unintentional RF signals, and by capturing and processing those signals some data can be recovered. For example the unintentional signals from a computer screen can be captured, and converted back into a live image of what the screen is displaying.
Until recently we have relied on an open source program by Martin Marinov called TempestSDR which has allowed RTL-SDR and other SDR owners perform interesting TEMPEST experiments with computer and TV monitors. We have a tutorial and demo on TempestSDR available on a previous post of ours. However, TempestSDR has always been a little difficult to set up and use.
The GNU Radio implementation is a good starting point for further experimentation, and we hope to see more developments in the future. They request that the GitHub repo be starred as it will help them get funding for future work on the project.
The creators have also released a video shown below that demonstrates the code with some recorded data. They have also released the recorded data, with links available on the GitHub. It's not clear which SDR they used, but we assume they used a wide bandwidth SDR as the recovered image is quite clear.
TEMPEST refers to a technique that is used to eavesdrop on electronic equipment via their unintentional radio emissions (as well as via sounds and vibrations). All electronics emit some sort of unintentional RF signals, and by capturing and processing those signals some data can be recovered. For example the unintentional signals from a computer screen could be captured, and converted back into a live image of what the screen is displaying. We have tutorials on how to do this with a program called TempestSDR available on a previous post of ours.
At the end of their post they perform some experiments like constantly writing data to memory on a PC, and putting the PCs GPU under varying load states. These experiments result in clear RFI bursts and pulsing carriers being visible in the spectrum, indicating that the PC is indeed unintentionally transmitting RF. They note that machine learning could be used to gather some information from these signals.
Suspecting interference generated by the HDMI clock, Mike Walters (@assortedhackery) used a HackRF and a near field probe antenna to investigate. By placing the near field probe on the Raspberry Pi 4's PCB and running a screen at 1440p resolution he discovered a large power spike showing up at 2.415 GHz. This interferes directly with 2.4 GHz WiFi Channel 1.
There's an interesting story doing the rounds about the Raspberry Pi 4 WiFi not working at higher HDMI resolutions. I had a quick look with a HackRF & near-field probe and there's definitely a big spike that stamps right on channel 1 pic.twitter.com/FXRebYYJxw
There’s a giant spike that could easily interfere with Channel 1 of a Wi-Fi adapter. So why is this happening? Because a 2560×[email protected] has a pixel clock of 241.5MHz and has a TMDS (transition-minimized differential signaling) clock of 2.415GHz, according to Hector Martin (@Marcan42). And what frequency does the RBP4 use for Wi-Fi? 2.4GHz. Which means… outputting on HDMI over 1440p can cause interference in a Wi-Fi channel.
The ExtremeTech article also notes that this problem is not unique to the Raspberry Pi 4 only. It turns out that USB 3.0 hardware is to blame, and this problem has occurred before with USB3.0 hard driver and on some MacBooks.
While the interference appears to be localized to the near field around the Pi4 PCB, we suspect that you could use TempestSDR to remotely eavesdrop on the Pi 4's video output if the interfering signal was boosted.
At last years Chaos Communication Congress (35C3) Conference, leveldown security presented their findings on multiple security vulnerabilities present in cryptocurrency hardware wallets. Cryptocurrency is a type of digital asset that relies on computers solving cryptographic equations to keep the network trusted and secure. Popular cryptocurrencies include Bitcoin, Ethereum and Ripple. To access your cryptocurrency funds on a computer, a software application called a wallet is used.
However, if a computer holding a wallet is compromised, it is possible that the wallet could be opened by a hacker and funds transferred out. To improve security, hardware wallets are available. These are USB keys that require you to enter a PIN on the key before the funds can be accessed. If the USB key is not inserted and activated by the PIN, the wallet cannot be opened.
To do this they created a GNU Radio flowchart that records data from the HackRF whenever an RF pulse is detected. A small Arduino powered servo then presses the buttons on the wallet hundreds of times, allowing hundreds of RF examples to be collected. Those RF samples are then used to train a neural network created in Tensorflow (a popular machine learning package). The result is a network that performs with 96% accuracy.
If you're interested in exploring other unintentional RF emissions from electronics, check out our previous post on using the TempestSDR software to spy on monitors/TVs with unintentionally emitted RF, and the various other posts on our blog on this topic.