COBALT-TLA introduces a neuro-symbolic verification loop that successfully and autonomously discovers novel cross-chain bridge vulnerabilities by integrating an LLM with the TLA+ model checker.

The paper introduces SCAgent, an automated framework that uses LLM-assisted agents to systematically discover, analyze, and assess side-channel leakage risks in complex systems like iOS, moving beyond…

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…

The paper proposes a constant-time implementation methodology for activation functions on microcontrollers to prevent timing side-channel attacks during embedded neural-network inference.

The paper analyzes the security of a partially masked hardware accelerator for Number Theoretic Transform (NTT) in PQC, demonstrating that the claimed security margins are significantly overestimated…

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 analyzes LLM vulnerability detection using mechanistic interpretability, finding that models primarily rely on safety detectors rather than direct vulnerability signature recognition.

The paper demonstrates that the Brazilian e-Voting Machine interface generates a simple and highly distinctive electromagnetic spectral signature, raising significant concerns about its susceptibility…

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 proposes training a single neural network using EM traces collected from multiple probe positions to detect cryptographic leakage across a larger area of a target device, validated by cross…

The paper introduces BOUNDARY FLOW, an LLVM-based framework that enhances kernel fuzzing and analysis by extracting per-task, state-aware data-flow information (arguments and return values) at functio…

The paper introduces a sample-wise targeted adversarial attack that successfully misclassifies only specific, triggered inputs during test-time adaptation while maintaining the overall label distribut…

The paper formalizes and quantifies the risk of side-channel leakage from public metrology releases by developing a statistical audit framework that yields precise information-theoretic bounds.

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 introduces a dual-layer side-channel attack framework that exploits the variable workload introduced by dynamic image preprocessing in local Vision-Language Models (VLMs) to infer sensitive…

The paper proposes DFBScanner, a lightweight static parameter inspection framework that detects backdoor attacks by analyzing anomalous parameter updates in the final classification layer, achieving f…

The paper demonstrates that LoRA adapters can be backdoored via data poisoning, showing the backdoor generalizes at the token feature level, and proposes robust behavioral and weight-level detectors f…

This paper demonstrates that LoRA adapters can be backdoored via data poisoning, showing that the resulting backdoor generalizes at the token feature level, and proposes robust behavioral and weight-l…

This paper presents a novel data-free Membership Inference Attack (MIA) that uses gradient inversion on Standard Cell Library Layouts (SCLLs) to reconstruct sensitive hardware images from intercepted…