The paper introduces DECIFR, a novel two-stage Membership Inference Attack (MIA) that exploits standard cell library layouts to reconstruct sensitive IC training data from intercepted federated model…

This paper investigates the use of Federated Learning (FL) for hardware assurance, demonstrating that while FL improves model performance over centralized learning, it remains vulnerable to gradient i…

This paper empirically evaluates the effectiveness of Differential Privacy (DP) against Membership Inference Attacks (MIAs) in Federated Learning, demonstrating that a stacking attack strategy can det…

The paper introduces AutoMIA, a novel framework that uses LLM agents to automate the discovery and implementation of Membership Inference Attacks (MIAs), achieving state-of-the-art performance by syst…

This paper provides the first comprehensive review of threats and defenses specifically targeting on-device AI inference, revealing a significant imbalance where certain attack types, like adversarial…

FedEDAuth is a lightweight, embedding-level authentication framework that enhances federated learning for counterfeit IC detection by identifying and filtering malicious participants before model aggr…

The paper introduces ActInv and PAF to systematically analyze and quantify privacy leakage from intermediate activations during split inference of LLMs, proposing PriPert for enhanced defense.

The paper introduces a Verifiable Gradient Inversion Attack (VGIA) that provides an explicit, certifiable method for reconstructing individual training records from shared gradients, particularly effe…

The paper analyzes existing hardware Trojan datasets to demonstrate that standard cell libraries can be systematically exploited to create visually undetectable, stealthy hardware Trojans, exemplified…

The paper proposes a new evaluation framework showing that, under realistic conditions, Membership Inference Attacks (MIAs) are weak privacy threats, suggesting that relying on them as a primary priva…

This review analyzes the dual impact of integrating Large Language Models (LLMs) into hardware design, detailing both their transformative potential in EDA and the critical security vulnerabilities th…

This paper analyzes the reliability of efficient membership inference attack (MIA) evaluation methods, demonstrating that standard aggregation techniques introduce biases that compromise accurate vuln…

This survey reviews the integration of AI and LLMs into hardware security verification, demonstrating its potential to automate complex stages while stressing the necessity of grounding AI outputs in…

The paper proposes a hybrid ASIC+eFPGA architecture to enhance the security and resilience of edge LLM inference accelerators against both runtime and supply-chain attacks.

The paper proposes a method for bit-exact verification of AI inference outputs without sacrificing performance, demonstrating that deterministic, precise re-computation is possible even across differe…

This paper provides a comprehensive, system-level comparison of MPC and FHE for Privacy-Preserving Machine Learning (PPML) across various models and environments, moving beyond single-metric latency a…

The paper introduces a four-stage structural dependency analysis hierarchy that enables scalable, sound first-order masking verification for large, production-level post-quantum cryptographic accelera…

The paper introduces ARES, a novel and practical gradient inversion attack that reconstructs sensitive training samples from large batch updates in Federated Learning without requiring architectural m…

The paper introduces CIPL, a unified channel-oriented framework, demonstrating that privacy leakage in LLM agents is governed by observable data channels and pipeline interactions, rather than being l…

This paper introduces TC-UMIA, a novel tri-class membership inference attack, demonstrating that machine unlearning can leak privacy risks to the retained data set, and evaluates defense mechanisms to…