The presentation discusses how to use deep learning and dynamic analysis to reduce side-channel attack surfaces in hardware cryptography. The approach involves leveraging AI explainability to quickly assess which parts of the implementation are responsible for the information leakages.
- Side-channel attacks target the implementation of an algorithm rather than the algorithm itself, making it a very efficient way to attack secure hardware.
- Debugging hardware is harder than debugging software because it requires looking at both the software and hardware to find the source of the leakages.
- The presentation showcases a concrete step-by-step example of how to use a software called COLD (Charge Side-Channel Attack Leak Detector) to find where a tiny implementation running on an SMT32F4 is leaking.
- The approach involves using deep learning and dynamic analysis to quickly and efficiently find the origin of the leakage.
- The presentation also discusses the benefits of using AI explainability to map the code and pinpoint the part of the code that is interacting with the hardware in a way that makes it vulnerable to side-channel attacks.
- The ultimate goal is to create a debugger that will help reduce the cost of finding and pinpointing attacks accurately, freeing up more time to focus on developing stronger cryptography.
The presentation cites an example of a powerful side-channel attack that occurred a few years back, where people were able to lift out the private key of Bitcoin wallets from a hardware Bitcoin wallet called Trezor. This attack highlights the devastating impact that side-channel attacks can have on secure hardware.