In the dynamic world of cyber security, staying ahead of malicious actors is paramount. We are thrilled to introduce the GitHub Abuse Engine, a cutting-edge engine of ThreatCloud AI designed to detect and mitigate malicious abuse on GitHub. This engine leverages advanced algorithms and AI to identify accounts and repositories that are hosted on GitHub and used for credential theft attacks and drive-by downloads. Integrated with our ThreatCloud AI, it offers comprehensive protection across Quantum gateways, Harmony Email, Endpoint, and Harmony Mobile.

GitHub Abuse

GitHub, with its extensive collection of open-source projects, has become an attractive target for cyber criminals. Malicious actors exploit GitHub to spread malware, steal sensitive information, and launch attacks on unsuspecting users. Due to GitHub’s high reputation scores, subdomain-based attacks often go undetected by traditional reputation systems.

Traditionally, the most reliable tool for analyzing these URLs is content analysis through active browsing. This method is both expensive and limited, as attacks are only visible after they have launched. Check Point’s new engine is capable of proactive detection.

Additionally, the industry faces many false positive problems. For example, developers mimicking a Netflix page for training purposes are instantly and blindly blocked by vendors. The GitHub Abuse engine uses AI and code analysis to analyze behavioral patterns and anomalies, resulting in very few false positives.

How the GitHub Abuse Engine Works

GitHub Abuse Engine analyzes GitHub accounts, anonymous profiles, and active GitHub users with public repos. The engine extracts information about the user, repositories, main files and JavaScript files. It also uses AI to do a comprehensive code analysis and to identify credential theft and other malicious code techniques.

The multi-layered algorithm includes:

  • Data collection: Continuous monitoring of GitHub repositories, including code changes, user activities, and repository metadata.
  • Feature extraction: Identifying key attributes indicative of malicious behavior, such as unusual commit patterns and suspicious user activity.
  • Pattern recognition: Utilizing advanced machine learning algorithms to detect patterns associated with malicious activities.
  • Threat classification: Classifying potential threats based on their severity and type to prioritize response.
  • Cross-referencing with ThreatCloud AI: Ensuring accurate identification and classification of threats using ThreatCloud AI’s extensive database.
Case Study: Obfuscation Bypass

One notable instance involved a fake page used for credential theft, spread by a newly created anonymous user nicknamed ‘cpanel-rcpfghygfdrftgyujt’.

The attack employed an escape-based obfuscation technique to hide credential theft.

A suspicious pattern was detected, triggered the AI model to investigate further. It discovered that a form was secretly added to the webpage using JavaScript. This form sent data to an external site controlled by the attacker, not associated with cPanel. This clearly indicates malicious activity. As a result, we were able to block this URL and safeguard Check Point’s customers.

Real-World Impact

Since it was released, the GitHub Abuse Engine has proven to be an invaluable asset in our cyber security arsenal. It has successfully identified and neutralized numerous threats, protecting organizations from potential harm. By staying one step ahead of cyber criminals, we are able to safe guard Check Point’s customers.

Looking Ahead

As cyber threats continue to evolve, so too must our defenses. The GitHub Abuse Engine represents just one of the many ways Check Point is innovating to protect our digital world. We remain committed to developing cutting-edge solutions that address the ever-changing landscape of cyber security.

ThreatCloud AI exposure to vast amounts of data combined with our expertise in cyber security enables us to develop real-time AI engines to prevent attacks that have never been seen before. With more than 55 AI engines, we master both micro-level analysis, such as understanding individual malware behavior, and macro-level analysis, like identifying global URL patterns. This provides a holistic view of the threat landscape.

Check Point customers using Quantum and Harmony products with activated Threat Emulation are protected against the campaigns detailed in this report.

To learn about Check Point threat prevention, schedule a demo or a free security checkup to assess your security posture.

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