![]() We then apply the trained model to new repositories it wasn’t trained on. To see if we can do this, we actually construct all our training data from an older version of the query that detects fewer vulnerabilities. In effect, we want the ML algorithm to improve on the current version of the manual query in much the same way that the current version improves on older, less-comprehensive versions. Of course, we don’t want to train a model that will simply reproduce the manual modeling we want to train a model that will predict new vulnerabilities that weren’t captured by manual modeling. This becomes the training set for a machine learning model that can classify code snippets as vulnerable or not. We extract tens of millions of snippets from over a hundred thousand public repositories, run the CodeQL queries on them, and label each as a positive or negative example for each query. We make up for the inherent noise in this inferred labeling with volume. Since the vast majority of code snippets do not contain vulnerabilities, snippets not detected by the manual models can be regarded as negative examples. Each sink detected by such a query serves as a positive example in the training set. We leverage these manual queries as ground-truth oracles, to label examples we then use to train our models. The manually written CodeQL queries already embody the expertise of the many security experts who wrote and refined them. But it comes at a cost! Asking code security experts to manually label millions of code snippets as safe or vulnerable is clearly untenable. While we have experimented some with unsupervised learning, unsurprisingly we found that supervised learning works better. We need to train ML models to recognize vulnerable code. ![]() ML-powered queries generate alerts that are marked with the “Experimental” label Building a training set For example, we can detect SQL injection vulnerabilities in the context of lesser-known or closed-source database abstraction libraries. We use examples surfaced by the manual models to train deep learning neural networks that can determine whether a code snippet comprises a potentially risky sink.Īs a result, we can uncover security vulnerabilities even when they arise from the use of a library we have never seen before. ![]() Manual modeling, however, can be time-consuming, and there will always be a long tail of less-common libraries and private code that we won’t be able to model manually. Members of the security community, alongside security experts at GitHub, continually expand and improve these queries to model additional common libraries and known patterns. To detect situations in which unsafe user data ends up in a dangerous place, CodeQL queries encapsulate knowledge of a large number of potential sources of user data (for example, web frameworks), as well as potentially risky sinks (such as libraries for executing SQL queries). For example, SQL injection is caused by using untrusted user data in a SQL query, and cross-site scripting occurs as a result of untrusted user data being written to a web page. Many vulnerabilities are caused by a single repeating pattern: untrusted user data is not sanitized and is subsequently accidentally used in an unsafe way. On that database we can then execute a series of CodeQL queries, each of which is designed to find a particular type of security problem. ![]() To detect vulnerabilities in a repository, the CodeQL engine first builds a database that encodes a special relational representation of the code. GitHub’s code scanning capabilities leverage the CodeQL analysis engine to find security vulnerabilities in source code and surface alerts in pull requests – before the vulnerable code gets merged and released. The best way to prevent such attacks is to detect and fix vulnerable code before it can be exploited. Read on for a behind-the-scenes peek into the ML framework powering this new technology! Detecting vulnerable codeĬode security vulnerabilities can allow malicious actors to manipulate software into behaving in unintended and harmful ways. If you want to set up your repositories to surface more alerts using our new ML technology, get started here. GitHub code scanning now uses machine learning (ML) to alert developers to potential security vulnerabilities in their code. ![]()
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