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Zero false positives

Expert intelligence + effective automation

Syringe ready to inject bad stuff. Credit: https://pixabay.com/es/photos/jeringa-healthcare-aguja-medicina-417786/

Tainted love

It's all about sanitization

In the several past articles, we have briefly touched on the concept of taint analysis. In this article, we would like to fill in the gaps which maybe have been raised by these careless...



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Fool the machine

Trick neural network classifiers

Artificial Neural Networks (ANNs) are certainly a wondrous achievement. They solve classification and other learning tasks with great accuracy. However, they are not flawless and might misclassify...



Toasting Marshmallow. Photo by hcmorr on Unsplash: https://unsplash.com/photos/qlHRuDvaxL8

Roasting Kerberos

Attacking a DC using kerberoast

Kerberos is a protocol developed by the MIT used to authenticate network services, is built using secret key cryptography and using a trusted third party server (named Authentication Server). This...



Git. Photo by Yancy Min on Unsplash: https://unsplash.com/photos/842ofHC6MaI/

Big Code

Learning from open source

In our Machine Learning (ML) for secure code series the mantra has always been the same: to figure out how to leverage the power of ML to detect security vulnerabilities in source code, regardless...



Photo by Andres Urena on Unsplash. Credits: https://unsplash.com/photos/k1osF_h2fzA

Natural code

Natural language processing for code security

Our return to the Machine Learning (ML) for secure code series is a bit of a digression, but one too interesting to resist. At the same time, it is not, since the Natural Language Processing (NLP)...



Binary machine learning. Credits: https://unsplash.com/photos/h3sAF1cVURw

Binary learning

Learning to exploit binaries

While our main focus, as stated previously, is to apply machine learning (ML) techniques to the discovery of vulnerabilities in source code, that is, a white-box approach to ML-guided hacking,...



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...



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...



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...



greek statue with small angels.

Asymmetric DoS, slow HTTP attack

The story of David and Goliath

Have you ever heard the story of David and Goliath? David, a young boy, goes out to confront a giant, named Goliath. David is the underdog in this fight and is expected to lose. But, everyone...



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...



Vulnerability disclosure

Vulnerability disclosure ecosystem

Responsible vulnerability disclosure

An information security vulnerability is a flaw or a weakness in a system or application that a malicious attacker could exploit, and could result in a compromise of the confidentiality, integrity...




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