· 2016
Driller: Augmenting Fuzzing Through Selective Symbolic Execution
- 885citations
- 2016year
Short summary
Driller is a new technique that combines fuzzing with selective symbolic execution to discover deeper software vulnerabilities.
AI-generated from the title and abstract; the full text is not read.
Key points
- Driller augments traditional fuzzing with selective symbolic execution.
- This combination allows for the exploration of deeper program paths.
- It overcomes a common limitation of vulnerability discovery tools.
- The technique aims to improve automated software vetting for security.
AI-generated from the title and abstract; the full text is not read.
Abstract
Memory corruption vulnerabilities are an everpresent risk in software, which attackers can exploit to obtain unauthorized access to confidential information. As products with access to sensitive data are becoming more prevalent, the number of potentially exploitable systems is also increasing, resulting in a greater need for automated software vetting tools. DARPA recently funded a competition, with millions of dollars in prize money, to further research focusing on automated vulnerability finding and patching, showing the importance of research in this area. Current techniques for finding potential bugs include static, dynamic, and concolic analysis systems, which each having their own advantages and disadvantages. A common limitation of systems designed to create inputs which trigger vulnerabilities is that they only find shallow bugs and struggle to exercise deeper paths in executables.
The authors' abstract, as published at the source., 2016 · DOI ↗
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Field: Software
SoftwareComputer Science