Young hacker smiling

Zero false positives

Expert intelligence + effective automation

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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 certain inputs. No problem, some error is expected. But what if you could give it two inputs that are virtually the same, and …



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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 of the technique, be it deep learning, graph mining, natural language processing, or anomaly detection. In …



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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) field is also part of what, at least today, is considered to be Artificial …



Nicolás Acosta, CISO at Corona

Innovation more understandable

"We make innovation more understandable, more worldly."

Our guest Nicolás is the Chief Information Security Officer (CISO) of Corona; a Colombian Multinational company dedicated to manufacturing ceramics for home improvement, construction, industry, agriculture, and energy markets. Corona has 20 production plants in Colombia, 3 in the US, 3 in México and 3 in Central...



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Preventing Hacks at CERN

A chat with Andrés Gómez.

Have you heard about God’s particle? In 2012, the Large Hadron Collider (LHC) found the Higgs Boson; a particle predicted to exist in the 1960s thanks to the work of Peter Higgs and other physicists. The LHC consists of a 27-kilometer ring of superconducting magnets with several accelerating structures …



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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, we’ve come across an interesting approach called VDiscover, which is radically different in the following sense: Works on …



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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...



Robot playing the piano

Crash course in machine learning

A survey of machine learning algorithms

In this article we clarify some of the undefined terms in our previous article and thereby explore a selection of machine learning algorithms and their applications to information security. This is not meant to be an exhaustive list of all machine learning (ML) algorithms and techniques. We would like, however …



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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