NIST has released a set of post-quantum cryptography (PQC) standards that address the threat posed by the emergence of quantum computing. The standard includes a modular lattice-based key exchange mechanism (ML-KEM) based on the CRYSTALS-Kyber algorithm. Recent work has shown that Kyber is susceptible to electromagnetic (EM) and power side-channel attacks. A full understanding of the side-channel vulnerabilities in Kyber is of paramount importance for next-generation communication and computing infrastructures.In this study, we target a previously unexplored section of the Kyber algorithm and implement a chosen ciphertext side-channel attack. We focus our attack on the Barrett reduction operation in the decapsulation algorithm. Compared to previous attacks on Barrett reduction, which targeted variables after the Inverse-Number Theoretic Transform (INTT), we focus on Barrett reduction on NTT variables, allowing for more general chosen ciphertexts that can evade input sanity checking. We design a scheme that requires only a set of 12 ciphertexts and side-channel EM traces of the corresponding decapsulation processes, which can reveal distinct leakages under different key values. The secret key is retrieved by pattern matching of the EM leakages. We develop an algorithm that utilizes an SMT solver to automatically select a set of ciphertexts. We implement Kyber on an ARM Cortex M4-based microcontroller and launch this new EM side-channel attack. Our results show that the attack achieves a success rate of over 95% in recovering the secret key value.

This paper introduces a cube-based reconfigurable robot that utilizes an electromagnet-based actuation framework to reconfigure in three dimensions via pivoting. While a variety of actuation mechanisms for self-reconfigurable robots have been explored, they often suffer from cost, complexity, assembly and sizing requirements that prevent scaled production of such robots. To address this challenge, we use an actuation mechanism based on electromagnets embedded into the edges of each cube to interchangeably create identically or oppositely polarized electromagnet pairs, resulting in repulsive or attractive forces, respectively. By leveraging attraction for hinge formation, and repulsion to drive pivoting maneuvers, we can reconfigure the robot by voxelizing it and actuating its constituent modules-termed Electrovoxels-via electromagnetically actuated pivoting. To demonstrate this, we develop fully untethered, three-dimensional self-reconfigurable robots and demonstrate 2D and 3D self-reconfiguration using pivot and traversal maneuvers on an air-table and in microgravity on a parabolic flight. This paper describes the hardware design of our robots, its pivoting framework, our reconfiguration planning software, and an evaluation of the dynamical and electrical characteristics of our system to inform the design of scalable self-reconfigurable robots.

This paper introduces a method to generate highly selective encodings that can be magnetically “programmed” onto physical modules to enable them to self-assemble in chosen configurations. We generate these encodings based on Hadamard matrices, and show how to design the faces of modules to be maximally attractive to their intended mate, while remaining maximally agnostic to other faces. We derive guarantees on these bounds, and verify their attraction and agnosticism experimentally. Using cubic modules whose faces have been covered in soft magnetic material, we show how inexpensive, passive modules with planar faces can be used to selectively self-assemble into target shapes without geometric guides. We show that these modules can be easily re-programmed for new target shapes using a CNC-based magnetic plotter, and demonstrate self-assembly of 8 cubes in a water tank.

In this paper, we present Mixels, programmable magnetic pixels that can be rapidly fabricated using an electromagnetic printhead mounted on an off-the-shelve 3-axis CNC machine. The ability to program magnetic material pixel-wise with varying magnetic force enables Mixels to create new tangible, tactile, and haptic interfaces. To facilitate the creation of interactive objects with Mixels, we provide a user interface that lets users specify the high-level magnetic behavior and that then computes the underlying magnetic pixel assignments and fabrication instructions to program the magnetic surface. Our custom hardware add-on based on an electromagnetic printhead and hall effect sensor clips onto a standard 3-axis CNC machine and can both write and read magnetic pixel values from magnetic material. Our evaluation shows that our system can reliably program and read magnetic pixels of various strengths, that we can predict the behavior of two interacting magnetic surfaces before programming them, that our electromagnet is strong enough to create pixels that utilize the maximum magnetic strength of the material being programmed, and that this material remains magnetized when removed from the magnetic plotter.