Info
This project involves evaluating various open-source AI-related projects to identify security vulnerabilities. The focus is on two main areas:
Classical Security Vulnerabilities: These include common software security issues such as injection attacks, buffer overflows, insecure dependencies, and improper authentication mechanisms.
AI-Specific Security Risks: These pertain to vulnerabilities unique to AI models, including adversarial attacks, data poisoning, model extraction, and privacy leaks.
The project involves conducting security assessments using both automated tools and manual analysis, leveraging static and dynamic analysis, fuzz testing, penetration testing, and adversarial attack simulations. The goal is to provide security recommendations and best practices for mitigating risks in AI-based systems.
This project involves evaluating various open-source AI-related projects to identify security vulnerabilities. The focus is on two main areas:
Classical Security Vulnerabilities: These include common software security issues such as injection attacks, buffer overflows, insecure dependencies, and improper authentication mechanisms.
AI-Specific Security Risks: These pertain to vulnerabilities unique to AI models, including adversarial attacks, data poisoning, model extraction, and privacy leaks.
The project involves conducting security assessments using both automated tools and manual analysis, leveraging static and dynamic analysis, fuzz testing, penetration testing, and adversarial attack simulations. The goal is to provide security recommendations and best practices for mitigating risks in AI-based systems.
This project involves evaluating various open-source AI-related projects to identify security vulnerabilities. The focus is on two main areas:
Classical Security Vulnerabilities: These include common software security issues such as injection attacks, buffer overflows, insecure dependencies, and improper authentication mechanisms.
AI-Specific Security Risks: These pertain to vulnerabilities unique to AI models, including adversarial attacks, data poisoning, model extraction, and privacy leaks.
The project involves conducting security assessments using both automated tools and manual analysis, leveraging static and dynamic analysis, fuzz testing, penetration testing, and adversarial attack simulations. The goal is to provide security recommendations and best practices for mitigating risks in AI-based systems.
Services
Services
Cybersecurity & AI Research
Cybersecurity & AI Research
Security Assessments
Security Assessments
Client
Client
Open Source Project
Open Source Project
Year
Year
2025
2025
Core Team
Core Team
Mykyta Mydryi
Mykyta Mydryi
Mykyta Mydryi
Markiian Chaklosh
Markiian Chaklosh
Markiian Chaklosh