activity
20242026
most citedFuzzerfly Effect: Hardware Fuzzing for Memory Safety

8 citations · 13 across the 5 of their papers we have counts for

collaborators

8 papers

cs.CR2026

AegisSat: Securing AI-Enabled SoC FPGA Satellite Platforms

Huimin Li, Vusal Novruzov, Nikhilesh Singh +3

The increasing adoption of System-on-Chip Field-Programmable Gate Arrays (SoC FPGAs) in AI-enabled satellite systems, valued for their reconfigurability and in-orbit update capabil…

cs.CR2025

Fuzzilicon: A Post-Silicon Microcode-Guided x86 CPU Fuzzer

Johannes Lenzen, Mohamadreza Rostami, Lichao Wu +1

Modern CPUs are black boxes, proprietary, and increasingly characterized by sophisticated microarchitectural flaws that evade traditional analysis. While some of these critical vul…

cs.CR2025

GoldenFuzz: Generative Golden Reference Hardware Fuzzing

Lichao Wu, Mohamadreza Rostami, Huimin Li +2

Modern hardware systems, driven by demands for high performance and application-specific functionality, have grown increasingly complex, introducing large surfaces for bugs and sec…

cs.CR2025

GateBreaker: Gate-Guided Attacks on Mixture-of-Expert LLMs

Lichao Wu, Sasha Behrouzi, Mohamadreza Rostami +2

Mixture-of-Experts (MoE) architectures have advanced the scaling of Large Language Models (LLMs) by activating only a sparse subset of parameters per input, enabling state-of-the-a…

cs.CR2025

ReFuzz: Reusing Tests for Processor Fuzzing with Contextual Bandits

Chen Chen, Zaiyan Xu, Mohamadreza Rostami +4

Processor designs rely on iterative modifications and reuse well-established designs. However, this reuse of prior designs also leads to similar vulnerabilities across multiple pro…

cs.CR2025

NeuroStrike: Neuron-Level Attacks on Aligned LLMs

Lichao Wu, Sasha Behrouzi, Mohamadreza Rostami +3

Safety alignment is critical for the ethical deployment of large language models (LLMs), guiding them to avoid generating harmful or unethical content. Current alignment techniques…