Young hacker smiling

Zero false positives

Expert intelligence + effective automation

Depiction of a deep neural network. Credits: https://unsplash.com/photos/R84Oy89aNKs

Deep Hacking

Deep learning for vulnerability discovery

If we have learned anything so far in our quest to understand how machine learning (ML) can be used to detect vulnerabilities in source code, it’s that what matters the most in this process are the different representations of source code which are later fed to the actual ML …



Chucky the actual serial killer doll

The anomaly serial killer doll

Hunting missing checks with anomaly detection

In our previous article we focused on taint-style vulnerabilites, i.e., those that are essentially due to the lack of input sanitization which allows tainted, user-controlled data to reach sensitive functions. Some of these arise due to missing checks in code, such as: failure to check authentication, authorization...



Screen showing source code

Exploiting code graphs

Mining graph representations for vulnerabilities

As we have seen in our previous revision article, probably the most interesting and successful approach to automated vulnerability detection is the pattern-based approach. Since we expect to extract meaningful patterns from the code, we also need a "comprehensive and feature-rich representation"[1] of it. Other...



Can machines learn to hack?

Machine-learning to hack

Machine learning for vulnerability discovery

To date the most important security vulnerabilities have been found via laborius code auditing. Also, this is the only way vulnerabilities can be found and fixed during development. However, as software production rates increase, so does the need for a reliable, automated method for checking or classifiying this code in …



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