Project Grant 2442719
- This $220,000 federal Project Grant award, provided by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CFDA #47.070) program, will support a collaborative research project led by Duke University to investigate and develop new defenses against prompt injection attacks on large language models (LLMs). The research aims to deepen the understanding of such cyber-attack threats and establish foundational security principles for the rapidly growing ecosystem...
- The National Science Foundation (NSF) awarded a $329,183 Project Grant to the College of William & Mary under the Computer and Information Science and Engineering (CFDA 47.070) program. The grant, awarded on October 1, 2023, will fund the development of a framework and methodology to enable researchers and software engineers to better interpret the behavior of AI-powered developer tools that leverage neural language models for source code. The project aims to generate global and local...
- The National Science Foundation (NSF) has awarded a Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) program to Carnegie Mellon University (CMU) for $675,000 over a 3-year period from October 1, 2025 to September 30, 2028. This grant is for a collaborative research project aimed at improving the ability of large language models (LLMs) to generate high-quality source code by deeply integrating program analysis techniques into the LLM training, code...
- The National Science Foundation (NSF) awarded a $225,000 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) Federal Grant Program to Colorado State University (CSU) for the project "COLLABORATIVE RESEARCH: SHF: MEDIUM: SEMANTIC AWARE CODE GENERATION WITH LLMS". The project aims to improve the ability of Large Language Models (LLMs) to generate high-quality, semantically-correct source code by integrating program analysis techniques into the LLM...
- The National Science Foundation (NSF) awarded a $600,000 Project Grant under the Computer and Information Science and Engineering (CISE) program to New York University (NYU) to investigate the risks of AI-generated code in the software supply chain. The 3-year project, which began on June 1, 2024, aims to: (i) develop techniques to distinguish human-written code from AI-generated code, (ii) measure the prevalence and security implications of AI-generated code in open-source software, and (iii)...
- This federal Project Grant award, totaling $300,000.00, was provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program. The grant supports a collaborative research project that aims to develop new semantic metrics to measure code complexity and guide software developers in writing more comprehensible code. The key objectives are to: (1) validate the correlation between code verifiability and human comprehension, (2)...
- This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Project Grant for $150,000, awarded on October 1, 2025, supports research to secure large language model (LLM) applications against prompt injection attacks. The project, led by the University of California, Berkeley, will conduct a systematic study to deepen the understanding of such threats and develop new defenses to mitigate these attacks. The project aims to establish foundational security...
- This federal Project Grant award, valued at $155,000.00, was provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program. The grant supports a collaborative research project led by the Regents of the University of Michigan to develop practical generative AI tools for enhancing the performance of security classifiers used to detect cybersecurity threats. The key focus is on addressing data challenges that often limit the...
- This Project Grant award of $450,000.00 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program will support research by the Regents of the University of California at Riverside to explore how large language models (LLMs) can assist in the analysis of complex computer software. The project aims to investigate strategies for using LLMs to complement existing program analysis techniques, with the goal of improving the accuracy and speed of...
- This Project Grant award, valued at $249,956.00, was provided by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) and is focused on developing a cybertraining program to train undergraduate and graduate students across the nation to identify, analyze, and mitigate different attack vectors targeting large language model (LLM)-empowered advanced cyberinfrastructure (CI) systems. The program will consist of eight...
This $331,428 federal Project Grant award was provided by the National Science Foundation (NSF) Computer and Information Science and Engineering (CFDA 47.070) program to the University of Maryland, College Park. The grant supports research to develop techniques for ensuring the security and correctness of code generated by large language models (code LLMs), which are becoming integral to AI-driven software development. Specifically, the project aims to establish a foundation for evaluating the secure coding capabilities of code LLMs and advance the broader code LLM security ecosystem. Key research thrusts include creating multi-objective benchmarks and metrics to evaluate code LLMs, and developing semantic-aware decoding algorithms to generate secure and correct code. The project's broader significance is enhancing the security and reliability of AI-powered software development, empowering developers to strengthen critical software systems. The award term runs from October 1, 2025 to September 30, 2030.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $331.4k | 8/4/25 |