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OWASP Portland Chapter Meeting - SAST and the Bad Human Code Project

Simple 120 SE Clay St Floor 2, Portland, OR 97214
120 SE Clay St Floor 2
Portland, OR 97214, United States (map)

Venue is Simple 120 SE Clay St Floor 2, Portland, OR 97214. There is plenty of on-street parking after hours in the neighborhood.

Description

SAST and the Bad Human Code Project

Static application security testing (SAST) is the automated analysis of source code both in its text and compiled forms. Lint is considered to be one of the first tools to analyze source code and this year marks its 40th anniversary. Even though it wasn't explicitly searching for security vulnerabilities back then, it did flag suspicious constructs. Today there are a myriad of tools to choose from both open source and commercial. We’ll talk about things to consider when evaluating web application scanners then turn our attention to finding additional ways to aggregate and correlate data from other sources such as git logs, code complexity analyzers and even rosters of students who completed secure coding training in an attempt to build a predictive vulnerability model for any new application that comes along. We’re also looking for people to contribute to a new open source initiative called “The Bad Human Code Project.” The goal is to create a one-stop corpus of intentionally vulnerable code snippets in as many languages as possible.

Speaker's Bio: John L. Whiteman is a web application security engineer at Oregon Health and Science University. He builds security tools and teaches a hands-on secure coding class to developers, researchers and anyone else interested in protecting data at the institution. He previously worked as a security researcher for Intel's Open Source Technology Center. John recently completed a Master of Computer Science at Georgia Institute of Technology specializing in Interactive Intelligence. He loves talking with like-minded people who are interested in building the next generation of security controls using technologies such as machine learning and AI.

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