arXiv subjects

Wavid Bowman

Publications and source records attributed to Wavid Bowman.

4 recordsLinked to original sources

arXiv2026

A Data-Free Membership Inference Attack on Federated Learning in Hardware Assurance

Federated Learning (FL) is an emerging solution to the data scarcity problem for training deep learning models in hardware assurance. While FL is designed to enhance privacy by not sharing raw data, it remains vulnerable to Membership Inference Attacks (MIAs) that can leak sensitive intellectual property (IP). Traditional MIAs are often impractical in this domain because they require access to auxiliary datasets that can match the unique statistical properties of private data. This paper introduces a novel, data-free MIA targeting image segmentation models in FL for hardware assurance. Our methodology leverages Standard Cell Library Layouts (SCLLs) as priors to guide a gradient inversion attack, allowing an adversary to reconstruct images from a client's intercepted model update without needing any private data. We demonstrate that, by analyzing the reconstruction fidelity, an adversary can infer sensitive hardware characteristics, successfully distinguishing between circuit layers (e.g., metal vs. diffusion) and technology nodes (e.g., 32nm vs. 90nm). Our findings reveal that a novel loss term can conditionally amplify the attack's effectiveness by overcoming evaluation bottlenecks for structurally complex data. This work underscores a significant IP risk, challenging the assumption that FL provides inherent privacy guarantees and proving that severe information leakage can occur even without access to domain-specific datasets.

cs.CR↗

arXiv2026

DECIFR: Domain-Aware Exfiltration of Circuit Information from Federated Gradient Reconstruction

Federated Learning (FL) is a promising approach for multiparty collaboration as a privacy-preserving technique in hardware assurance, but its security against adversaries with domain-specific knowledge is underexplored. This paper demonstrates a critical vulnerability where available standard cell library layouts (SCLL) can be exploited to compromise the privacy of sensitive integrated circuit (IC) training data. We introduce DECIFR, a novel two-stage Membership Inference Attack (MIA) that requires no auxiliary dataset. The attack employs a guided Gradient Inversion Attack (GIA) to reconstruct a client's training images from intercepted model updates. Our findings reveal that the fidelity of these reconstructions directly correlates with membership status, allowing an adversary to reliably distinguish members from non-members based on image quality. This work exposes a practical threat that overcomes the limitations of conventional attacks and underscores that standard FL protocols are insufficient for securing domains with extensive knowledge. We conclude that robust defenses are essential for the secure application of FL in hardware assurance.

cs.CR↗

arXiv2026

Potentials and Pitfalls of Applying Federated Learning in Hardware Assurance

As microelectronics flourish and outsourcing of the design and manufacturing stages of integrated circuits (ICs) and printed circuit boards (PCBs) becomes the norm, microelectronics stakeholders must also confront a new wave of security challenges, including the threats posed by hardware Trojans, counterfeit electronics, and reverse engineering attacks. Traditional detection and prevention methods like testing and side-channel analysis have limitations in reliability and scalability. Automated reverse engineering by deep learning (DL) models is a foolproof approach to hardware assurance, but faces challenges due to limited data. By pooling data from different stakeholders (competitors in industry, governments, etc.), DL models can be more effectively trained but privacy of intellectual property (IP) is a significant concern. Federated Learning (FL) has been proposed as a potential alternative allowing for the collaborative training of a DL model without sharing raw data. While FL has been widely used in healthcare, IoT, and finance, its application in hardware assurance remains underexplored. This study investigates, for the first time, FL-based DL for hardware assurance, demonstrating that FL outperforms single-client centralized learning in segmentation tasks for reverse engineering. Our results show that increasing the number of clients improves FL performance by collaboratively training the model with more data. However, and more importantly, a major pitfall of FL is also exposed -- it remains vulnerable to gradient inversion attacks. We show that SEM images used in FL can be recovered by attackers, which would therefore expose the sensitive and proprietary IPs that FL was supposed to protect. We highlight these privacy risks and also suggest future research directions to improve security and effectiveness in hardware assurance.

cs.CR↗

arXiv2025

Debugging WebAssembly? Put some Whamm on it!

Debugging and monitoring programs are integral to engineering and deploying software. Dynamic analyses monitor applications through source code or IR injection, machine code or bytecode rewriting, and virtual machine or direct hardware support. While these techniques are viable within their respective domains, common tooling across techniques is rare, leading to fragmentation of skills, duplicated efforts, and inconsistent feature support. We address this problem in the WebAssembly ecosystem with Whamm, a declarative instrumentation DSL for WebAssembly that abstracts above the instrumentation strategy, leveraging bytecode rewriting and engine support as available. Whamm solves three problems: 1) tooling fragmentation, 2) prohibitive instrumentation overhead of general-purpose frameworks, and 3) tedium of tailoring low-level high-performance mechanisms. Whamm provides fully-programmable instrumentation with declarative match rules, static and dynamic predication, automatic state reporting, and user library support, while achieving high performance through compiler and engine optimizations. At the back end, Whamm provides instrumentation to a Wasm engine as Wasm code, reusing existing engine optimizations and unlocking new ones, most notably intrinsification, to minimize overhead. In particular, explicitly requesting program state in match rules, rather than reflection, enables the engine to efficiently bundle arguments and even inline compiled probe logic. Whamm streamlines the tooling effort, as its bytecode-rewriting target can run instrumented programs everywhere, lowering fragmentation and advancing the state of the art for engine support. We evaluate Whamm with case studies of non-trivial monitors and show it is expressive, powerful, and efficient.

cs.SE↗