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arXiv · 2603.01170

ATLAS: AI-Assisted Threat-to-Assertion Learning for System-on-Chip Security Verification

Ishraq Tashdid·Kimia Tasnia·Alexander Garcia·Jonathan Valamehr·Sazadur Rahman
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Abstract

This work presents ATLAS, an LLM-driven framework that bridges standardized threat modeling and property-based formal verification for System-on-Chip (SoC) security. Starting from vulnerability knowledge bases such as Common Weakness Enumeration (CWE), ATLAS identifies SoC-specific assets, maps relevant weaknesses, and generates assertion-based security properties and JasperGold scripts for verification. By combining asset-centric analysis with standardized threat model templates and multi-source SoC context, ATLAS automates the transformation from vulnerability reasoning to formal proof. Evaluated on three HACK@DAC benchmarks, ATLAS detected 39/48 CWEs and generated correct properties for 33 of those bugs, advancing automated, knowledge-driven SoC security verification toward a secure-by-design paradigm.

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Ishraq Tashdid, Kimia Tasnia, Alexander Garcia, Jonathan Valamehr, Sazadur Rahman. 2026-03-01. ATLAS: AI-Assisted Threat-to-Assertion Learning for System-on-Chip Security Verification. https://doi.org/10.1145/3770743.3804400

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