2433062
Project Grant
Overview
Grant Description
SBIR Phase I: ReleaseChecker: Lastline software supply chain security via GPU-accelerated binary diffing.
The broader impact of this Small Business Innovation Research (SBIR) Phase I project is to introduce unique AI-powered code diffing capabilities to defend against software supply chain attacks, capabilities that are not yet available in other software supply chain security solutions.
This innovation offers several benefits.
Firstly, by reducing cybersecurity operation costs, it improves the competitiveness of U.S. companies, allowing them to allocate resources more efficiently.
Secondly, it bolsters software supply chain security, significantly reducing the risk of cyberattacks and protecting sensitive data for governments, enterprises, critical infrastructures, and individuals.
Additionally, this innovation will extend our understanding of how to apply AI to program analysis for cybersecurity, including binary code disassembling, function feature extraction and embedding, model training, and optimization.
It establishes a new program analysis pipeline based on the latest AI technology, which can be extended to many other cybersecurity applications.
This Small Business Innovation Research (SBIR) Phase I project addresses the critical need for enhancing software supply chain security and compliance.
Unlike other solutions that monitor each stage of the software supply chain, this project aims to leverage AI-powered code diffing technology to precisely and efficiently find the differences between two released versions of the same software.
It further combines software composition analysis and large language models (LLMs) to understand the risks associated with these differences.
This solution acts as the final check before the software is released or deployed.
The anticipated results include improved accuracy and efficiency in diffing analysis and comprehension, as well as a prototype for testing and commercialization.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the foundation's intellectual merit and broader impacts review criteria.
Subawards are not planned for this award.
The broader impact of this Small Business Innovation Research (SBIR) Phase I project is to introduce unique AI-powered code diffing capabilities to defend against software supply chain attacks, capabilities that are not yet available in other software supply chain security solutions.
This innovation offers several benefits.
Firstly, by reducing cybersecurity operation costs, it improves the competitiveness of U.S. companies, allowing them to allocate resources more efficiently.
Secondly, it bolsters software supply chain security, significantly reducing the risk of cyberattacks and protecting sensitive data for governments, enterprises, critical infrastructures, and individuals.
Additionally, this innovation will extend our understanding of how to apply AI to program analysis for cybersecurity, including binary code disassembling, function feature extraction and embedding, model training, and optimization.
It establishes a new program analysis pipeline based on the latest AI technology, which can be extended to many other cybersecurity applications.
This Small Business Innovation Research (SBIR) Phase I project addresses the critical need for enhancing software supply chain security and compliance.
Unlike other solutions that monitor each stage of the software supply chain, this project aims to leverage AI-powered code diffing technology to precisely and efficiently find the differences between two released versions of the same software.
It further combines software composition analysis and large language models (LLMs) to understand the risks associated with these differences.
This solution acts as the final check before the software is released or deployed.
The anticipated results include improved accuracy and efficiency in diffing analysis and comprehension, as well as a prototype for testing and commercialization.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the foundation's intellectual merit and broader impacts review criteria.
Subawards are not planned for this award.
Awardee
Funding Goals
THE GOAL OF THIS FUNDING OPPORTUNITY, "NSF SMALL BUSINESS INNOVATION RESEARCH (SBIR)/ SMALL BUSINESS TECHNOLOGY TRANSFER (STTR) PROGRAMS PHASE I", IS IDENTIFIED IN THE LINK: HTTPS://WWW.NSF.GOV/PUBLICATIONS/PUB_SUMM.JSP?ODS_KEY=NSF23515
Grant Program (CFDA)
Awarding / Funding Agency
Place of Performance
Riverside,
California
92508-2974
United States
Geographic Scope
Single Zip Code
Deepbits Technology was awarded
Project Grant 2433062
worth $273,383
from National Science Foundation in September 2024 with work to be completed primarily in Riverside California United States.
The grant
has a duration of 1 year and
was awarded through assistance program 47.084 NSF Technology, Innovation, and Partnerships.
The Project Grant was awarded through grant opportunity NSF Small Business Innovation Research / Small Business Technology Transfer Phase I Programs.
SBIR Details
Research Type
SBIR Phase I
Title
SBIR Phase I: ReleaseChecker: Lastline Software Supply Chain Security via GPU-accelerated Binary Diffing
Abstract
The broader impact of this Small Business Innovation Research (SBIR) Phase I project is to introduce unique AI-powered code diffing capabilities to defend against software supply chain attacks, capabilities that are not yet available in other software supply chain security solutions. This innovation offers several benefits. Firstly, by reducing cybersecurity operation costs, it improves the competitiveness of U.S. companies, allowing them to allocate resources more efficiently. Secondly, it bolsters software supply chain security, significantly reducing the risk of cyberattacks and protecting sensitive data for governments, enterprises, critical infrastructures, and individuals. Additionally, this innovation will extend our understanding of how to apply AI to program analysis for cybersecurity, including binary code disassembling, function feature extraction and embedding, model training, and optimization. It establishes a new program analysis pipeline based on the latest AI technology, which can be extended to many other cybersecurity applications.
This Small Business Innovation Research (SBIR) Phase I project addresses the critical need for enhancing software supply chain security and compliance. Unlike other solutions that monitor each stage of the software supply chain, this project aims to leverage AI-powered code diffing technology to precisely and efficiently find the differences between two released versions of the same software. It further combines software composition analysis and large language models (LLMs) to understand the risks associated with these differences. This solution acts as the final check before the software is released or deployed. The anticipated results include improved accuracy and efficiency in diffing analysis and comprehension, as well as a prototype for testing and commercialization.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
Topic Code
CA
Solicitation Number
NSF 23-515
Status
(Ongoing)
Last Modified 9/25/24
Period of Performance
9/1/24
Start Date
8/31/25
End Date
Funding Split
$273.4K
Federal Obligation
$0.0
Non-Federal Obligation
$273.4K
Total Obligated
Activity Timeline
Additional Detail
Award ID FAIN
2433062
SAI Number
None
Award ID URI
SAI EXEMPT
Awardee Classifications
Small Business
Awarding Office
491503 TRANSLATIONAL IMPACTS
Funding Office
491503 TRANSLATIONAL IMPACTS
Awardee UEI
JAKLV5H8KAQ3
Awardee CAGE
7VEB3
Performance District
CA-39
Senators
Dianne Feinstein
Alejandro Padilla
Alejandro Padilla
Modified: 9/25/24