arXiv Machine Learning By Johann Knechtel, Ozgur Sinanoglu, Ramesh Karri

LLMs for Secure Hardware Design and Related Problems: Opportunities and Challenges

Read the original on arXiv Machine Learning →

arXiv:2605. 10807v4 Announce Type: replace-cross Abstract: The integration of Large Language Models (LLMs) into Electronic Design Automation (EDA) and hardware security is rapidly reshaping the semiconductor industry.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv AI
Sep 12

Architecting the Secure AI-SOC: A Neurosymbolic Framework for Pipeline Integrity and Threat Mitigation

The paper proposes a neurosymbolic defense architecture for AI-enhanced Security Operations Centers (AI‑SOCs) that protects against indirect prompt injection via log poisoning. It combines deterministic SIEM decoders as a pre‑filter with NeMo Guardrails for semantic validation, and adds a closed‑loop telemetry system for Human‑in‑the‑Loop visibility. Experimental results mapped to the MITRE ATLAS taxonomy show the approach effectively dismantles promptware kill chains and delivers a resilient, observable defense for next‑generation AI‑SOCs.

By Anna Gazani, Spyridon Kounoupidis, Panagiotis Katsaros, Nikolaos Kekatos, Grigorios Tsoumakas, Georgios Koutidis
arXiv Computation and Language
Sep 15

Toward Secure Code Generation: Bridging Correctness and Security via Task-Adaptive Vulnerability Modeling and Execution-Based Benchmarking

arXiv:2407.02395v3 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used for program synthesis, yet they often generate code that is functionally plausible but ins...

By Jiexin Wang, Liuwen Cao, Xitong Luo, Yang Cao, Zhenghao Li, Yunyi Xiao, Mengchen Zhao, Adam Jatowt, Yi Cai