8 citations · 15 across the 12 of their papers we have counts for
13 papers
AttackonCTF: Defending Hardware Security Competition Benchmarks in the Age of LLMs
Mohamadreza Rostami, Nikhilesh Singh, Stephen Muttathil +5
Hardware security competitions such as HackTheSilicon serve as benchmarking platforms for evaluating vulnerability detection methods and for training humans and AI. However, our st…
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…
NeST: Neuron Selective Tuning for LLM Safety
Sasha Behrouzi, Lichao Wu, Mohamadreza Rostami +1
Safety alignment is essential for the responsible deployment of Large Language Models (LLMs). Yet, existing approaches often rely on heavyweight fine-tuning that is costly to updat…
GoodVibe: Security-by-Vibe for LLM-Based Code Generation
Maximilian Thang, Lichao Wu, Sasha Behrouzi +4
Large language models (LLMs) are increasingly used for code generation in fast, informal development workflows, often referred to as vibe coding, where speed and convenience are pr…
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…
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…
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…
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…
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…
Fuzzerfly Effect: Hardware Fuzzing for Memory Safety
Mohamadreza Rostami, Chen Chen, Rahul Kande +3
Hardware-level memory vulnerabilities severely threaten computing systems. However, hardware patching is inefficient or difficult postfabrication. We investigate the effectiveness…
Lost and Found in Speculation: Hybrid Speculative Vulnerability Detection
Mohamadreza Rostami, Shaza Zeitouni, Rahul Kande +5
Microarchitectural attacks represent a challenging and persistent threat to modern processors, exploiting inherent design vulnerabilities in processors to leak sensitive informatio…
Beyond Random Inputs: A Novel ML-Based Hardware Fuzzing
Mohamadreza Rostami, Marco Chilese, Shaza Zeitouni +3
Modern computing systems heavily rely on hardware as the root of trust. However, their increasing complexity has given rise to security-critical vulnerabilities that cross-layer at…
WhisperFuzz: White-Box Fuzzing for Detecting and Locating Timing Vulnerabilities in Processors
Pallavi Borkar, Chen Chen, Mohamadreza Rostami +5
Timing vulnerabilities in processors have emerged as a potent threat. As processors are the foundation of any computing system, identifying these flaws is imperative. Recently fuzz…