VirtualSpace AppSec combines static code analysis with specialized machine learning trained on thousands of real-world vulnerability patterns. This PoC illustrates how our tailored AI outperforms generic models (like GPT-4) in identifying critical vulnerabilities in C, C++, and .NET applications, ensuring your code remains secure against emerging threats in the real world.
- AI-Powered Detection: Specialized machine learning trained explicitly for security vulnerability identification.
- Supreme Accuracy: Outperforms generic AI models by detecting complex vulnerabilities missed by others.
- Advanced Static Analysis: Comprehensive static binary and source-level examination.
Included in this repository is a Python example demonstrating a critical vulnerability that GPT-4 (GPT Test) failed to identify; instead, it pointed out code quality improvements, unrelated to vulnerabilities, while VirtualSpace AppSec successfully filtered and detected it, as shown in the screenshot above.
- Python Proof Of Concept: Illustrates a specific security flaw undetected by GPT-4.
- Screenshot Proof: Demonstrates detection by VirtualSpace AppSec's specialized AI.
Feel free to replicate and verify this demonstration to see the supreme detection capabilities of VirtualSpace AppSec if you have a license key to VirtualSpace AppSec, or purchase one with 10% OFF GHOFF10 :)
This PoC is for demonstration and educational purposes only, designed to highlight the unique capability of VirtualSpace AppSec in identifying vulnerabilities missed by generic AI models. For real-world deployments, VirtualSpace AppSec provides comprehensive scanning and reporting features with enterprise-grade accuracy.
This project is licensed under the MIT License. See the LICENSE file for details.