Pith. sign in

REVIEW 1 cited by

Toward Automated Potential Primary Asset Identification in Verilog Designs

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2502.04648 v1 pith:YMONJ3JU submitted 2025-02-07 cs.CR

classification cs.CR
keywords securityassetsidentificationdesignpotentialassetautomateddesigns
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

With greater design complexity, the challenge to anticipate and mitigate security issues provides more responsibility for the designer. As hardware provides the foundation of a secure system, we need tools and techniques that support engineers to improve trust and help them address security concerns. Knowing the security assets in a design is fundamental to downstream security analyses, such as threat modeling, weakness identification, and verification. This paper proposes an automated approach for the initial identification of potential security assets in a Verilog design. Taking inspiration from manual asset identification methodologies, we analyze open-source hardware designs in three IP families and identify patterns and commonalities likely to indicate structural assets. Through iterative refinement, we provide a potential set of primary security assets and thus help to reduce the manual search space.

Discussion (0). Sign in to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. SV-LLM: An Agentic Approach for SoC Security Verification using Large Language Models

    cs.CR 2025-06 conditional novelty 5.0 of 10

    SV-LLM automates SoC security verification with six cooperating LLM agents, reaching 84.8% vulnerability detection accuracy and 82% to 89% bug validation rates on benchmarks the paper does not disclose.

Pith tools