ATLAS: AI-Assisted Threat-to-Assertion Learning for System-on-Chip Security Verification
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866911618033516544 |
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| author | Tashdid, Ishraq Tasnia, Kimia Garcia, Alexander Valamehr, Jonathan Rahman, Sazadur |
| author_facet | Tashdid, Ishraq Tasnia, Kimia Garcia, Alexander Valamehr, Jonathan Rahman, Sazadur |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_01170 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | ATLAS: AI-Assisted Threat-to-Assertion Learning for System-on-Chip Security Verification Tashdid, Ishraq Tasnia, Kimia Garcia, Alexander Valamehr, Jonathan Rahman, Sazadur Cryptography and Security Artificial Intelligence 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. |
| title | ATLAS: AI-Assisted Threat-to-Assertion Learning for System-on-Chip Security Verification |
| topic | Cryptography and Security Artificial Intelligence |
| url | https://arxiv.org/abs/2603.01170 |