CAREER: Achieving Autonomous Symbolic Execution through Learning from Humans — NSF Award to Arizona State University (AZ, $292,081
Vulnerability discovery for software security poses significant challenges due to the vast program state space in complex, real-world programs. This project tackles the challenge of vulnerability discovery in software systems through the enhancement of binary symbolic execution, a technique that simplifies the vast and
| Award title | CAREER: Achieving Autonomous Symbolic Execution through Learning from Humans |
|---|---|
| Award ID | 2442984 |
| Awardee | Arizona State University |
| City | SCOTTSDALE |
| State | AZ |
| Amount obligated | $292,081 |
| Principal investigator | Youzhi Bao |
| Program | Secure &Trustworthy Cyberspace |
| Start date | 08/01/2025 |
| Abstract | Vulnerability discovery for software security poses significant challenges due to the vast program state space in complex, real-world programs. This project tackles the challenge of vulnerability discovery in software systems through the enhancement of binary symbolic execution, a technique that simplifies the vast and complex landscape of software operations. Despite its potential, symbolic execution requires substantial expert intervention to manage its complexity, making the process cumbersom |
| Source | NSF Awards |
$799/mo
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