Smartphones have a complex hardware and software architecture. Having access to their full memory space can help solve judicial investigations. We propose a new privilege escalation technique in order to access hidden contents and execute sensitive operations. While classical forensic tools mostly exploit software vulnerabilities, it is based on a hardware security evaluation technique. Electromagnetic fault injection is such a technique usually used for microcontrollers or FPGA security characterization. A security function running at 1.2 GHz on a 64-bit SoC with a Linux-based OS was successfully attacked. The Linux authentication module uses this function to verify the password correctness by comparing two hash values. Hence, this work constitutes a step towards smartphones privilege escalation through electromagnetic fault injection. This approach is interesting for addressing forensic issues on smartphones.

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Model extraction is a growing concern for the security of AI systems. For deep neural network models, the architecture is the most important information an adversary aims to recover. Being a sequence of repeated computation blocks, neural network models deployed on edge-devices will generate distinctive side-channel leakages. The latter can be exploited to extract critical information when targeted platforms are physically accessible. By combining theoretical knowledge about deep learning practices and analysis of a widespread implementation library (ARM CMSIS-NN), our purpose is to answer this critical question: how far can we extract architecture information by simply examining an EM side-channel trace? For the first time, we propose an extraction methodology for traditional MLP and CNN models running on a high-end 32-bit microcontroller (Cortex-M7) that relies only on simple pattern recognition analysis. Despite few challenging cases, we claim that, contrary to parameters extraction, the complexity of the attack is relatively low and we highlight the urgent need for practicable protections that could fit the strong memory and latency requirements of such platforms.