Formed in 2009, the Archive Team (not to be confused with the archive.org Archive-It Team) is a rogue archivist collective dedicated to saving copies of rapidly dying or deleted websites for the sake of history and digital heritage. The group is 100% composed of volunteers and interested parties, and has expanded into a large amount of related projects for saving online and digital history.
History is littered with hundreds of conflicts over the future of a community, group, location or business that were "resolved" when one of the parties stepped ahead and destroyed what was there. With the original point of contention destroyed, the debates would fall to the wayside. Archive Team believes that by duplicated condemned data, the conversation and debate can continue, as well as the richness and insight gained by keeping the materials. Our projects have ranged in size from a single volunteer downloading the data to a small-but-critical site, to over 100 volunteers stepping forward to acquire terabytes of user-created data to save for future generations.
The main site for Archive Team is at archiveteam.org and contains up to the date information on various projects, manifestos, plans and walkthroughs.
This collection contains the output of many Archive Team projects, both ongoing and completed. Thanks to the generous providing of disk space by the Internet Archive, multi-terabyte datasets can be made available, as well as in use by the Wayback Machine, providing a path back to lost websites and work.
Our collection has grown to the point of having sub-collections for the type of data we acquire. If you are seeking to browse the contents of these collections, the Wayback Machine is the best first stop. Otherwise, you are free to dig into the stacks to see what you may find.
The Archive Team Panic Downloads are full pulldowns of currently extant websites, meant to serve as emergency backups for needed sites that are in danger of closing, or which will be missed dearly if suddenly lost due to hard drive crashes or server failures.
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We've launched code scanning JavaScript ML-powered queries in beta 🚀 !
What's in the box?
The four ML-powered queries focus on finding even more instances of the most common and dangerous JavaScript vulnerabilities, such as cross-site scripting (XSS) and SQL injection.
A CodeQL query identifies code that could be affected by a particular type of vulnerability. Our team and the community of security researchers continuously improve those queries to include more knowledge of the most common frameworks and libraries. For example, in order to flag up a SQL injection vulnerability in a web application, we need to recognise both (1) the web framework through which untrusted user data enters the application and (2) the SQL library that is used to execute the SQL query. CodeQL knows about many such frameworks and libraries, but not all 1�7 there are simply too many of them!
We trained a model by mining the patterns of such libraries and frameworks that are already covered by existing queries. The model learns from these examples and predicts potential additional sinks in which untrusted user data should not be used. The ML-powered queries can therefore find results otherwise missed by the original query.
Experimental alerts
ML-generated alerts are displayed alongside the other code scanning alerts in the security view of the repository, as well as in pull requests. The new alerts are marked with the
Experimentallabel.These alerts have a higher false positive rate relative to results generated by our standard queries. As with many machine learning models, results will improve over time, and we encourage everyone to experiment and provide feedback!
How do I enable it?
The new experimental alerts start appearing automatically on all repositories that enabled code scanning with CodeQL, contain JavaScript code, and have opted to use the
security-extendedorsecurity-and-qualityquery suites.Note: in its default configuration, code scanning uses the
securityquery suite, which does not contain the new experimental queries.To get the new experimental alerts you simply need to ensure your repository has enabled code scanning. Then add the
security-extendedorsecurity-and-qualitysuites to your existing workflow (see below).How can I give feedback?
If you run into any problems or have a question about CodeQL code scanning ML-powered queries (setup, alerts, etc.), please respond to this thread. Please supply as much detail as you can about the issue you encountered, as well as information to reproduce if available. If you don't feel comfortable posting in this public discussion, please open a support ticket.
We also encourage you to use the CodeQL Discussions forum for any questions related to running or writing CodeQL.
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