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Google, Adobe Announce New Open Source Security Tools

Google and Adobe this week announced the availability of new open source security tools, for continuous fuzzing and detecting living-off-the-land attacks.

Google releases ClusterFuzzLite

Google and Adobe this week announced the availability of new open source security tools, for continuous fuzzing and detecting living-off-the-land attacks.

Google releases ClusterFuzzLite

Google announced the open source release of ClusterFuzzLite, which it described as a ClusterFuzz-based continuous fuzzing solution that runs as part of continuous integration (CI) workflows in an effort to help users find vulnerabilities before they are committed to the source code.

ClusterFuzzLite can be integrated into CI workflows with only a few lines of code.

“With the release of ClusterFuzzLite, any project can integrate this essential testing standard and benefit from fuzzing,” Google said. “ClusterFuzzLite offers many of the same features as ClusterFuzz, such as continuous fuzzing, sanitizer support, corpus management, and coverage report generation. Most importantly, it’s easy to set up and works with closed source projects, making ClusterFuzzLite a convenient option for any developer who wants to fuzz their software.”

ClusterFuzzLite goes hand in hand with Google’s OSS-Fuzz open source fuzzing service, which has helped identify 6,500 vulnerabilities and 21,000 functional bugs across more than 500 open source projects.

Adobe releases LotL Classifier

Living-off-the-land (LotL) is used to describe attacks where malicious actors leverage legitimate software in an effort to avoid being detected.

Adobe has released an open source tool, named LotL Classifier, that is designed to detect LotL attacks by leveraging a “feature extraction” component and a machine learning-based classifier algorithm.

The feature extraction component takes data from threat intelligence, malware analysis, real incidents and real data logs, and uses that data to generate a series of tags based on binaries, paths, keywords, networks, patterns, and similarity.

The tags are then fed to the classifier component, which decides if the analyzed data set is good or bad. This component also creates a set of tags that can be integrated with rule-based automation or anomaly detection tools, such as One Stop Anomaly Shop (OSAS), which Adobe recently released as open source.

Related: Adobe Open Sources Tool for Sanitizing Logs, Detecting Exposed Credentials

Related: Google Expands Open Source Vulnerabilities Database

Written By

Eduard Kovacs (@EduardKovacs) is a contributing editor at SecurityWeek. He worked as a high school IT teacher for two years before starting a career in journalism as Softpedia’s security news reporter. Eduard holds a bachelor’s degree in industrial informatics and a master’s degree in computer techniques applied in electrical engineering.

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