⏱️ Constant-time support lands in LLVM: Protecting cryptographic code at the compiler level
#llvm #compilers #security #computing #software #sidechannels #cyber
⏱️ Constant-time support lands in LLVM: Protecting cryptographic code at the compiler level
#llvm #compilers #security #computing #software #sidechannels #cyber
Kênh thông tin đáng tin cậy cho giao tiếp giữa các container #CrossContainer #Communication #KênhThôngTin #GiaoTiếpGiữaContainer #SideChannels
https://www.reddit.com/r/programming/comments/1ovcdim/funreliable_sidechannels_for_crosscontainer/
This morning, David Oswald started our last day at GSW with his talk "Breaching the Gates: Uncovering Hardware Weaknesses in Confidential Computing", giving an overview of power side-channels and fault attacks in confidential computing scenario.
For our after-lunch-session we hosted a hardware side-channel lab, where our participants used physical side-channel attacks to break the security of embedded devices.
In our second morning session, Stefan Mangard and Daniel Gruss aka @lavados spoke about side-channel attacks in various settings - from phones to computers to networks - showing that side channels really are everywhere.
Only 5️⃣ more days until DIMVA‘25!
We kickstart the conference on Wednesday with our welcome event, exploring the old town of Graz during a city tour. See you there!
#DIMVA25 #Conference #WebSecurity #Vulnerability #VulnerabilityDetection #SideChannels #Obfuscation #OS #Network #AndroidPatches #AI #ML #ResilientSystems
here's the paper for the #RealWorldCrypto talk from yesterday where they extracted an AES key modulated by interference onto the bluetooth signal: https://ia.cr/2025/559
In this paper, we present a side-channel attack on the hardware AES accelerator of a Bluetooth chip used in millions of devices worldwide, ranging from wearables and smart home products to industrial IoT. The attack leverages information about AES computations unintentionally transmitted by the chip together with RF signals to recover the encryption key. Unlike traditional side-channel attacks that rely on power or near-field electromagnetic emissions as sources of information, RF-based attacks leave no evidence of tampering, as they do not require package removal, chip decapsulation, or additional soldered components. However, side-channel emissions extracted from RF signals are considerably weaker and noisier, necessitating more traces for key recovery. The presented profiled machine learning-assisted attack can recover the full encryption key from 90,000 traces captured at a one-meter distance from the target device, with each trace being an average of 10,000 samples per encryption. This is a twofold improvement over the correlation analysis-based attack on the same AES accelerator.