Author Archives: Kaylin Trychon

AllStar: Continuous Security Policy Enforcement for GitHub Projects

 


As an active member of the open source software (OSS) community, Google recognizes the growing threat of software supply chain attacks against OSS we use and develop. Building on our efforts to improve OSS security with an end-to-end framework (SLSA), metrics (Scorecards), and coordinated vulnerability disclosure (guide), we are excited to announce Allstar.


Allstar is a GitHub app that continuously enforces security policy settings through selectable automated enforcement actions. Allstar is already filing and closing security issues for Envoy and GoogleContainerTools, with more organizations and repositories lined up. 


See the OpenSSF announcement for more information on Allstar.




AllStar: Continuous Security Policy Enforcement for GitHub Projects

 


As an active member of the open source software (OSS) community, Google recognizes the growing threat of software supply chain attacks against OSS we use and develop. Building on our efforts to improve OSS security with an end-to-end framework (SLSA), metrics (Scorecards), and coordinated vulnerability disclosure (guide), we are excited to announce Allstar.


Allstar is a GitHub app that continuously enforces security policy settings through selectable automated enforcement actions. Allstar is already filing and closing security issues for Envoy and GoogleContainerTools, with more organizations and repositories lined up. 


See the OpenSSF announcement for more information on Allstar.




AllStar: Continuous Security Policy Enforcement for GitHub Projects

 


As an active member of the open source software (OSS) community, Google recognizes the growing threat of software supply chain attacks against OSS we use and develop. Building on our efforts to improve OSS security with an end-to-end framework (SLSA), metrics (Scorecards), and coordinated vulnerability disclosure (guide), we are excited to announce Allstar.


Allstar is a GitHub app that continuously enforces security policy settings through selectable automated enforcement actions. Allstar is already filing and closing security issues for Envoy and GoogleContainerTools, with more organizations and repositories lined up. 


See the OpenSSF announcement for more information on Allstar.




Simplifying Titan Security Key options for our users

Today we are excited to announce some changes to our lineup of Titan Security Keys on the Google Store which provide a simpler experience and make choosing the right security key for you even easier. We will now offer only two types of Titan Security Keys: a USB-A and a USB-C version. Both of these keys have Near Field Communication (NFC) functionality, which allows you to use it with most mobile devices by simply tapping it on the back of your mobile device in order to sign in securely. These keys will be available for all users starting tomorrow, August 10. 


In 2018, Google introduced the Titan Security Key as a direct defense against credential phishing. Phishing occurs when an attacker tries to trick you into giving them your username and password, and it remains one of the easiest and most successful ways of breaching accounts online. Paired with our Advanced Protection Program and its industry-leading automatic protections, the Titan Security Key remains one of the best ways to keep your Google Account safe.

Introducing new Titan Security Key options


Since NFC functionality is now supported by a wide range of Android phones and iPhones, we are discontinuing the Bluetooth Titan Security Key and focusing on the easier and more widely available NFC capability. However, for existing users with our Bluetooth Titan Security Keys, these will continue to work with Bluetooth and will continue to work as an NFC key on most modern mobile devices. Applicable warranties for existing Bluetooth Titan Security Keys will continue to be honored per their terms. All Titan Security Keys are built with a hardware secure element chip that includes firmware engineered by Google to verify the key’s integrity.


If you have a computer with USB-A ports, we recommend you get the USB-A + NFC security key:If you have a computer with USB-C ports, we recommend you get the USB-C + NFC security key:If you have an iPad with a USB-C connector you can use the USB-C Titan Security Key. If you have an iPad with a lightning connector, it’s recommended to get a USB-A Titan Security Key with an Apple Lightning adapter:

To purchase a Titan Security Key, visit the Google Store. The USB-A+NFC key,which includes a USB-A to USB-C adapter, is available for $30 and the USB-C+NFC key retails for $35.

To learn more about how security keys can help protect you against phishing, visit the Titan Security Key product page.

Linux Kernel Security Done Right




To borrow from an excellent analogy between the modern computer ecosystem and the US automotive industry of the 1960s, the Linux kernel runs well: when driving down the highway, you're not sprayed in the face with oil and gasoline, and you quickly get where you want to go. However, in the face of failure, the car may end up on fire, flying off a cliff.

As we approach its 30th Anniversary, Linux still remains the largest collaborative development project in the history of computing. The huge community surrounding Linux allows it to do amazing things and run smoothly. What's still missing, though, is sufficient focus to make sure that Linux fails well too. There's a strong link between code robustness and security: making it harder for any bugs to manifest makes it harder for security flaws to manifest. But that's not the end of the story. When flaws do manifest, it's important to handle them effectively.

Rather than only taking a one-bug-at-a-time perspective, preemptive actions can stop bugs from having bad effects. With Linux written in C, it will continue to have a long tail of associated problems. Linux must be designed to take proactive steps to defend itself from its own risks. Cars have seat belts not because we want to crash, but because it is guaranteed to happen sometimes.

Even though everyone wants a safe kernel running on their computer, phone, car, or interplanetary helicopter, not everyone is in a position to do something about it. Upstream kernel developers can fix bugs, but have no control over what a downstream vendor chooses to incorporate into their products. End users get to choose their products, but don't usually have control over what bugs are fixed nor what kernel is used (a problem in itself). Ultimately, vendors are responsible for keeping their product's kernels safe.

What to fix?


The statistics of tracking and fixing distinct bugs are sobering. The stable kernel releases ("bug fixes only") each contain close to 100 new fixes per week. Faced with this high rate of change, a vendor can choose to ignore all the fixes, pick out only "important" fixes, or face the daunting task of taking everything.

Fix nothing?


With the preponderance of malware, botnets, and state surveillance targeting flawed software, it's clear that ignoring all fixes is the wrong "solution." Unfortunately this is the very common stance of vendors who see their devices as just a physical product instead of a hybrid product/service that must be regularly updated.

Fix important flaws?

Between the dereliction of doing nothing and the assumed burden of fixing everything, the traditional vendor choice has been to cherry-pick only the "important" fixes. But what constitutes "important" or even relevant? Just determining whether to implement a fix takes developer time.

The prevailing wisdom has been to choose vulnerabilities to fix based on the Mitre CVE list, presuming all important flaws (and therefore fixes) would have an associated CVE. However, given the volume of flaws and their applicability to a particular system, not all security flaws have CVEs assigned, nor are they assigned in a timely manner. Evidence shows that for Linux CVEs, more than 40% had been fixed before the CVE was even assigned, with the average delay being over three months after the fix. Some fixes went years without having their security impact recognized. On top of this, product-relevant bugs may not even classify for a CVE. Finally, upstream developers aren't actually interested in CVE assignment; they spend their limited time actually fixing bugs.

A vendor relying on cherry-picking is all but guaranteed to miss important vulnerabilities that others are actively fixing, which is almost worse than doing nothing since it creates the illusion that security updates are being appropriately handled.

Fix everything!

So what is a vendor to do? The answer is simple, if painful: continuously update to the latest kernel release, either major or stable. Tracking major releases means gaining security improvements along with bug fixes, while stable releases are bug fixes only. For example, although modern Android phones ship with kernels that are based on major releases from almost two to four years earlier, Android vendors do now, thankfully, track stable kernel releases. So even though the features being added to newer major kernels will be missing, all the latest stable kernel fixes are present.

Performing continuous kernel updates (major or stable) understandably faces enormous resistance within an organization due to fear of regressions—will the update break the product? The answer is usually that a vendor doesn't know, or that the update frequency is shorter than their time needed for testing. But the problem with updating is not that the kernel might cause regressions; it's that vendors don't have sufficient test coverage and automation to know the answer. Testing must take priority over individual fixes.

Make it happen

One question remains: how to possibly support all the work continuous updates require? As it turns out, it’s a simple resource allocation problem, and is more easily accomplished than might be imagined: downstream redundancy can be moved into greater upstream collaboration.

More engineers for fixing bugs earlier

With vendors using old kernels and backporting existing fixes, their engineering resources are doing redundant work. For example, instead of 10 companies each assigning one engineer to backport the same fix independently, those developer hours could be shifted to upstream work where 10 separate bugs could be fixed for everyone in the Linux ecosystem. This would help address the growing backlog of bugs. Looking at just one source of potential kernel security flaws, the syzkaller dashboard shows the number of open bugs is currently approaching 900 and growing by about 100 a year, even with about 400 a year being fixed.

More engineers for code review

Beyond just squashing bugs after the fact, more focus on upstream code review will help stem the tide of their introduction in the first place, with benefits extending beyond just the immediate bugs caught. Capable code review bandwidth is a limited resource. Without enough people dedicated to upstream code review and subsystem maintenance tasks, the entire kernel development process bottlenecks.

Long-term Linux robustness depends on developers, but especially on effective kernel maintainers. Although there is effort in the industry to train new developers, this has been traditionally justified only by the "feature driven" jobs they can get. But focusing only on product timelines ultimately leads Linux into the Tragedy of the Commons. Expanding the number of maintainers can avoid it. Luckily the "pipeline" for new maintainers is straightforward.

Maintainers are built not only from their depth of knowledge of a subsystem's technology, but also from their experience with mentorship of other developers and code review. Training new reviewers must become the norm, motivated by making upstream review part of the job. Today's reviewers become tomorrow's maintainers. If each major kernel subsystem gained four more dedicated maintainers, we could double productivity.

More engineers for testing and infrastructure

Along with more reviewers, improving Linux's development workflow is critical to expanding everyone's ability to contribute. Linux's "email only" workflow is showing its age, but the upstream development of more automated patch tracking, continuous integration, fuzzing, coverage, and testing will make the development process significantly more efficient.

Additionally, instead of testing kernels after they're released, it's more effective to test during development. When tests are performed against unreleased kernel versions (e.g. linux-next) and reported upstream, developers get immediate feedback about bugs. Fixes can be developed before a flaw is ever actually released; it's always easier to fix a bug earlier than later.

This "upstream first" approach to product kernel development and testing is extremely efficient. Google has been successfully doing this with Chrome OS and Android for a while now, and is hardly alone in the industry. It means feature development happens against the latest kernel, and devices are similarly tested as close as possible to the latest upstream kernels, all avoiding duplicated "in-house" effort.

More engineers for security and toolchain development

Besides dealing reactively to individual bugs and existing maintenance needs, there is also the need to proactively eliminate entire classes of flaws, so developers cannot introduce these types of bugs ever again. Why fix the same kind of security vulnerability 10 times a year when we can stop it from ever appearing again?

Over the last few years, various fragile language features and kernel APIs have been eliminated or replaced (e.g. VLAs, switch fallthrough, addr_limit). However, there is still plenty more work to be done. One of the most time-consuming aspects has been the refactoring involved in making these usually invasive and context-sensitive changes across Linux's 25 million lines of code.

Beyond kernel code itself, the compiler and toolchain also need to grow more defensive features (e.g. variable zeroing, CFI, sanitizers). With the toolchain technically "outside" the kernel, its development effort is often inappropriately overlooked and underinvested. Code safety burdens need to be shifted as much as possible to the toolchain, freeing humans to work in other areas. On the most progressive front, we must make sure Linux can be written in memory-safe languages like Rust.

Don't wait another minute

If you're not using the latest kernel, you don't have the most recently added security defenses (including bug fixes). In the face of newly discovered flaws, this leaves systems less secure than they could have been. Even when mediated by careful system design, proper threat modeling, and other standard security practices, the magnitude of risk grows quickly over time, leaving vendors to do the calculus of determining how old a kernel they can tolerate exposing users to. Unless the answer is "just abandon our users," engineering resources must be focused upstream on closing the gap by continuously deploying the latest kernel release.

Based on our most conservative estimates, the Linux kernel and its toolchains are currently underinvested by at least 100 engineers, so it's up to everyone to bring their developer talent together upstream. This is the only solution that will ensure a balance of security at reasonable long-term cost.

A new chapter for Google’s Vulnerability Reward Program



A little over 10 years ago, we launched our Vulnerability Rewards Program (VRP). Our goal was to establish a channel for security researchers to report bugs to Google and offer an efficient way for us to thank them for helping make Google, our users, and the Internet a safer place. To recap our progress on these goals, here is a snapshot of what VRP has accomplished with the community over the past 10 years:
  • Total bugs rewarded: 11,055
  • Number of rewarded researchers: 2,022
  • Representing 84 different countries
  • Total rewards: $29,357,516
To celebrate our anniversary and ensure the next 10 years are just as (or even more) successful and collaborative, we are excited to announce the launch of our new platform, bughunters.google.com.

This new site brings all of our VRPs (Google, Android, Abuse, Chrome and Play) closer together and provides a single intake form that makes it easier for bug hunters to submit issues. Other improvements you will notice include:
  • More opportunities for interaction and a bit of healthy competition through gamification, per-country leaderboards, awards/badges for certain bugs and more!
  • A more functional and aesthetically pleasing leaderboard. We know a lot of you are using your achievements in the VRP to find jobs (we’re hiring!) and we hope this acts as a useful resource.
  • A stronger emphasis on learning: Bug hunters can improve their skills through the content available in our new Bug Hunter University
  • Streamlined publication process: we know the value that knowledge sharing brings to our community. That’s why we want to make it easier for you to publish your bug reports.
  • Swag will now be supported for special occasions (we heard you loud and clear!)

We also want to take a moment to shine a light on some aspects of the VRP that are not yet well-known, such as:


When we launched our very first VRP, we had no idea how many valid vulnerabilities - if any - would be submitted on the first day. Everyone on the team put in their estimate, with predictions ranging from zero to 20. In the end, we actually received more than 25 reports, taking all of us by surprise.

Since its inception, the VRP program has not only grown significantly in terms of report volume, but the team of security engineers behind it has also expanded – including almost 20 bug hunters who reported vulnerabilities to us and ended up joining the Google VRP team.

That is why we are thrilled to bring you this new platform, continue to grow our community of bug hunters and support the skill development of up-and-coming vulnerability researchers.

Thanks again to the entire Google bug hunter community for making our vulnerability rewards program successful. As you continue to play around with the new site and reporting system, tell us about it - we would love to hear your feedback. Until next time, keep on finding those bugs!

Advancing an inclusive, diverse security industry




It’s no secret that lack of diversity in corporate America is a well-documented problem and improvements have been slow. To help improve female representation in the cybersecurity industry, Google teamed up with Women in Cybersecurity (WiCyS) and SANS Institute a year ago to establish the Security Training Scholarship Program.

The multi-stage security training program set participants on a path to launch and advance their careers in cybersecurity through skills development, introducing them to fundamental cybersecurity concepts with interactive challenges like Capture the Flag (CTF) and the SANS CyberStart Game, which introduces topics such as Linux, web attacks, programming, forensics, and more. Mentors and peers guide the participants through each stage of the program and top qualifiers then graduate and receive access to the SANS foundational security training courses, which readies and prepares these women for their first roles in the security industry. The goal is to get them employed in cybersecurity within the next 1.5 years and to create a powerful network of women in the field – in essence, drawing more women to the industry and helping to close the talent gap.


As the inaugural program comes to an end, we are proud to report that its overall impact includes:


  • 112 people received training-based scholarship
  • 15 Full Scholarship Recipients received the full course training, which includes:
    • CyberStart Game and SANS BootUp CTF
    • SANS SEC275 Foundations & Exam
    • SANS 401 Security Essentials Bootcamp and GSEC
    • Elective - SANS SEC504/GCIH, SEC488/GCLD, SEC560/GPEN, or SEC548/GWAPT
  • 24 certifications earned to date with 100% pass rate, with average score on GSEC 90%
  • Since 2013, only 2 people have scored 99% on GIAC Certified Incident Handler (GCIH) one is a WiCyS Scholarship Recipient
  • 1/3 of students were employed in direct information security roles before the program ended
  • 100% of Full Scholarship Recipients intend to have long term careers in information security (15+ years)
Participants praise the program’s strong networking component where they can support one another, share best practices, ask questions from SANS security experts and receive industry insight from members across Google’s security team. As Lynn Dohm, executive director of WiCyS, told us, “You cannot put a price tag on the power of community, and last year’s WiCyS Security Training Program proved just that.”

Here at Google, we are inspired by the dedication and passion the scholarship recipients have shown throughout the program and are eager to see what they accomplish throughout their careers.

Elizabeth Beattie, who was part of the inaugural program told us, “I learned that, as part of my scholarship program with WiCyS, SANS Institute and Google, I’ve been awarded a scholarship to attend the WiCyS 2021 conference in September. In fact, I’ve volunteered to co-author a panel there with some of my amazing fellow recipients. And the crowning achievement? Tonight, I passed my first GIAC certification (GSEC)!”

Despite these great results, we know there is still a lot of work to be done to help educate and develop a more inclusive information security workforce. So this year we are expanding the Security Trainings Scholarship Program to help us reach even more women and generate a steady stream of talent in the field of information security. This expansion would not have been possible without the added support of Facebook and Bloomberg, who have come on board this year to boost this important program.

“We are thrilled to scale the program this year, powered by scholarships from Google, Bloomberg, and Facebook,” said Dohm. “Now, more WiCyS members will be able to dive deep and change the trajectory of their career in less than a year, all within a cohort setting with extensive support and resources provided by mentors and colleagues. That’s what empowerment looks like, and we are thrilled that these three incredible strategic partners of WiCyS can make this happen for not only the WiCyS community, but also for the sake of the cybersecurity workforce at large.”

The next round of scholarships is open through August 2, 2021. To learn more and apply, please visit the WiCyS application page. We can’t wait to meet the next cohort of recipients.

Verifiable design in modern systems


The way we design and build software is continually evolving. Just as we now think of security as something we build into software from the start, we are also increasingly looking for new ways to minimize trust in that software. One of the ways we can do that is by designing software so that you can get cryptographic certainty of what the software has done.

In this post, we'll introduce the concept of verifiable data structures that help us get this cryptographic certainty. We'll describe some existing and new applications of verifiable data structures, and provide some additional resources we have created to help you use them in your own applications.
A verifiable data structure is a class of data structure that lets people efficiently agree, with cryptographic certainty, that the data contained within it is correct.

Merkle Trees are the most famous of these and have been used for decades because they can enable efficient verification that a particular piece of data is included among many records - as a result they also form the basis of most blockchains.

Although these verifiable data structures are not new, we now have a new generation of developers who have discovered them and the designs they enable -- further accelerating their adoption.
These verifiable data structures enable building a new class of software that have elements of verifiability and transparency built into the way they operate. This gives us new ways to defend against coercion, introduce accountability to existing and new ecosystems, and make it easier to demonstrate compliance to regulators, customers and partners.

Certificate Transparency is a great example of a non-blockchain use of these verifiable data structures at scale to secure core internet infrastructure. By using these patterns, we have been able to introduce transparency and accountability to an existing system used by everyone without breaking the web.
Unfortunately, despite the capabilities of verifiable data structures and the associated patterns, there are not many resources developers can use to design, build, and deploy scalable and production-quality systems based on them.

To address this gap we have generalized the platform we used to build Certificate Transparency so it can be applied to other classes of problems as well. Since this infrastructure has been used for years as part of this ecosystem it is well understood and can be deployed confidently in production systems.
This is why we have seen solutions in areas of healthcare, financial services, and supply chain leverage this platform. Beyond that, we have also applied these patterns to bring these transparency and accountability properties to other problems within our own products and services.

To this end, in 2019, we used this platform to bring supply chain integrity to the Go language ecosystem via the Go Checksum Database. This system allows developers to have confidence that the package management systems supporting the Go ecosystem can’t intentionally, arbitrarily, or accidentally start giving out the wrong code without getting caught. The reproducibility of Go builds makes this particularly powerful as it enables the developer to ensure what is in the source repository matches what is in the package management system. This solution delivers a verifiable chaiin all the way from the source repositories to the final compiled artifacts.

Another example of using these patterns is our recently announced partnership with the Linux Foundation on Sigstore. This project is a response to the ever-increasing influx of supply chain attacks on the Open Source ecosystem.

Supply chain attacks have been possible because there are weaknesses at every link in the chain. Components like build systems, source code management tools, and artifact repositories all need to be treated as critical production environments, because they are. To address this, we first need to make it possible to verify provenance along the entire chain and the goal of the Sigstore effort is to enable just that.

We are now working on using these patterns and tools to enable hardware-enforced supply chain integrity for device firmware, which we hope will discourage supply chain attacks on the devices, like smartphones, that we rely on every day by bringing transparency and accountability to their firmware supply chain.

In all of the above examples, we are using these verifiable data structures to ensure the integrity of artifacts in the supply chain. This enables customers, auditors, and internal security teams to be confident that each actor in the supply chain has lived up to their responsibilities. This helps earn the trust of those that rely on the supply chain, discourages insiders from using their position as it increases the chance they will get caught, introduces accountability, and enables proving the associated systems continually meet their compliance obligations.

When using these patterns the most important task is defining what data should be logged. This is why we put together a taxonomy and modeling framework which we have found to be helpful in designing verifiability into the systems we discussed above, and which we hope you will find valuable too.
Please take a look at the transparency.dev website to learn about these verifiable data structures, and the tools and guidance we have put together to help use them in your own applications.

Measuring Security Risks in Open Source Software: Scorecards Launches V2


Contributors to the Scorecards project, an automated security tool that produces a “risk score” for open source projects, have accomplished a lot since our launch last fall. Today, in collaboration with the Open Source Security Foundation community, we are announcing Scorecards v2. We have added new security checks, scaled up the number of projects being scored, and made this data easily accessible for analysis.


With so much software today relying on open-source projects, consumers need an easy way to judge whether their dependencies are safe. Scorecards helps reduce the toil and manual effort required to continually evaluate changing packages when maintaining a project’s supply chain. Consumers can automatically assess the risks that dependencies introduce and use this data to make informed decisions about accepting these risks, evaluating alternative solutions, or working with the maintainers to make improvements.


Identifying Risks


Since last fall, Scorecards’ coverage has grown; we've added several new checks, following the Know, Prevent, Fix framework proposed by Google earlier this year, to prioritize our additions:

Malicious contributors

Contributors with malicious intent or compromised accounts can introduce potential backdoors into code. Code reviews help mitigate against such attacks. With the new Branch-Protection check, developers can verify that the project enforces mandatory code review from another developer before code is committed. Currently, this check can only be run by a repository admin due to GitHub API limitations. For a third-party repository, use the less informative Code-Review check instead.

Vulnerable code

Despite best efforts by developers and peer reviews, vulnerable code can enter source control and remain undetected. That’s why it's important to enable continuous fuzzing and static code analysis to catch bugs early in the development lifecycle. We have added checks to detect if a project uses Fuzzing and SAST tools as part of their CI/CD system.

Build system compromise

A common CI/CD solution used by GitHub projects is GitHub Actions. A danger with these action workflows is that they may handle untrusted user input. Meaning, an attacker can craft a malicious pull request to gain access to the privileged GitHub token, and with it the ability to push malicious code to the repo without review. To mitigate this risk, Scorecard's Token-Permissions prevention check now verifies that the GitHub workflows follow the principle of least privilege by making GitHub tokens read-only by default.

Bad dependencies

Any software is as secure as its weakest dependency. This may sound obvious, but the first step to knowing our dependencies is simply to declare them... and have our dependencies declare them too. Once we have this provenance information, we can assess the risks of our software and mitigate those risks. Unfortunately, there are several widely-used anti-patterns that break this provenance principle. The first of these anti-patterns is checked-in binaries -- as there's no way to easily verify or check the contents of the binary in the project. Scorecards provides Binary-Artifacts check for testing this.


Another anti-pattern is the use of curl | bash in scripts which dynamically pulls dependencies. Cryptographic hashes let us pin our dependencies to a known value: if this value ever changes, the build system will detect it and refuse to build. Pinning dependencies is useful everywhere we have dependencies: not just during compilation, but also in Dockerfiles, CI/CD workflows, etc. Scorecards checks for these anti-patterns with the Frozen-Deps check. This check is helpful for mitigating against malicious dependency attacks such as the recent CodeCov attack.


Even with hash-pinning, hashes need to be updated once in a while when dependencies patch vulnerabilities. Tools like dependabot or renovatebot give us the opportunity to review and update the hashes. The Scorecards Automated-Dependency-Update check verifies that developers rely on such tools to update their dependencies.


It is important to know vulnerabilities in a project before uptaking it as a dependency. Scorecards can provide this information via the new Vulnerabilities check, without the need to subscribe to a vulnerability alert system.


Scaling the impact


To date, the Scorecards project has scaled up to evaluate security criteria for over 50,000 open source projects. In order to scale this project, we undertook a massive redesign of our architecture and used a PubSub model which achieved horizontal scalability and higher throughput. This fully automated tool periodically evaluates critical open source projects and exposes the Scorecards check information through a public BigQuery dataset which is refreshed weekly.



This data can be retrieved using the bq command line tool. The following example shows how to export data for the Kubernetes project. Substitute the url for the repo to export data from a different project:

$ bq query --nouse_legacy_sql 'SELECT Repo, Date, Checks FROM openssf.scorecardcron.scorecard_latest WHERE Repo="github.com/kubernetes/kubernetes"'


To export the latest data on all analyzed projects, see instructions here.

How does the internet measure up?

Scorecards data for available projects is now included in the recently announced Google Open Source Insights project and also showcased in OpenSSF Security Metrics project. The data on these sites shows that there are still important security gaps to fill, even in widely used packages like Kubernetes.


We also analyzed Scorecards data through Google Data Studio -- one of our data analysis and visualization tools.The diagram below shows a breakdown of the checks that were run and the pass/fail outcome for the 50,000 repositories:

 



As we can see, a lot needs to be done to improve the security of these critical projects. A large number of these projects are not continuously fuzzed, do not define a security policy for reporting vulnerabilities, and do not pin dependencies, to name just a few common problems. We all need to come together as an industry to drive awareness of these widespread security risks, and to make improvements that will benefit everyone.

Scorecards in Action

Several large projects have adopted Scorecards and are keeping us updated on their experiences with it. Below are some examples of Scorecards in action:

Envoy
Early on we talked about how the Envoy maintainers adopted Scorecards for their project and integrated it within their policy on introducing new dependencies. Since then, pull requests introducing new dependencies to Envoy must get approval from a dependency maintainer who uses Scorecards to evaluate the dependency against a set of criteria.

In addition, Envoy also got right to work in improving its own security health metrics according to its own Scorecards evaluation, and is now pinning C++ dependencies and requiring pip hashes for python dependencies. Github actions are also pinned in the continuous integration flow.

Previously, Envoy had created a tool that outputs Scorecards data on its dependencies as a CSV that can be used to generate a table of results:



Now with more project data, Envoy is able to automatically generate up-to-date Scorecard information about its dependencies and publish it in documentation, like the following:


Scorecards
We improved our own score for the Scorecards! For example, we are now pinning our own dependencies by hash (e.g. docker dependencies, workflow dependencies) to prevent CodeCov style attacks. We’ve also included a Security Policy based on this recommended template.

Get involved

We look forward to continuing to grow the Scorecards community. The project now has contributions from 23 developers. Thank you to Azeem, Naveen, Laurent, Asra and Chris for their work building these new features and scaling Scorecards.

If you would like to join the fun, check out these good first timer issues.

If you would like us to help you run Scorecards on specific projects, please submit a GitHub pull request to add those projects here.

Last but not least, we have a lot of ideas and many more checks we’d like to add, but we want to hear from you. Tell us which checks you would like to see in the next version of Scorecards.


What’s next?

There are a couple of big enhancements we’re especially excited about:


Thanks again to the entire Scorecards community and the OpenSSF for making this project successful. If you’re adopting and improving the score of the projects you maintain, tell us about it. Until next time, keep on improving those scores!

Announcing a unified vulnerability schema for open source


In recent months, Google has launched several efforts to strengthen open-source security on multiple fronts. One important focus is improving how we identify and respond to known security vulnerabilities without doing extensive manual work. It is essential to have a precise common data format to triage and remediate security vulnerabilities, particularly when communicating about risks to affected dependencies—it enables easier automation and empowers consumers of open-source software to know when they are impacted and make security fixes as soon as possible.

We released the Open Source Vulnerabilities (OSV) database in February with the goal of automating and improving vulnerability triage for developers and users of open source software. This initial effort was bootstrapped with a dataset of a few thousand vulnerabilities from the OSS-Fuzz project. Implementing OSV to communicate precise vulnerability data for hundreds of critical open-source projects proved the success and utility of the format, and garnered feedback to help us improve the project; for example, we dropped the Cloud API key requirement, making the database even easier to access by more users. The community response also showed that there was broad interest in extending the effort further.

Today, we’re excited to announce a new milestone in expanding OSV to several key open-source ecosystems: Go, Rust, Python, and DWF. This expansion unites and aggregates four important vulnerability databases, giving software developers a better way to track and remediate the security issues that affect them. Our effort also aligns with the recent US Executive Order on Improving the Nation’s Cybersecurity, which emphasized the need to remove barriers to sharing threat information in order to strengthen national infrastructure. This expanded shared vulnerability database marks an important step toward creating a more secure open-source environment for all users.
A simple, unified schema for describing vulnerabilities precisely

As with open source development, vulnerability databases in open source follow a distributed model, with many ecosystems and organizations creating their own database. Since each uses their own format to describe vulnerabilities, a client tracking vulnerabilities across multiple databases must handle each completely separately. Sharing of vulnerabilities between databases is also difficult.

The Google Open Source Security team, Go team, and the broader open-source community have been developing a simple vulnerability interchange schema for describing vulnerabilities that’s designed from the beginning for open-source ecosystems. After starting work on the schema a few months ago, we requested public feedback and received hundreds of comments. We have incorporated the input from readers to arrive at the current schema:

{

        "id": string,

        "modified": string,

        "published": string,

        "withdrawn": string,

        "aliases": [ string ],

        "related": [ string ],

        "package": {

                "ecosystem": string,

                "name": string,

                "purl": string,

        },

        "summary": string,

        "details": string,

        "affects": [ {

                "ranges": [ {

                        "type": string,

                        "repo": string,

                        "introduced": string,

                        "fixed": string

                } ],

                "versions": [ string ]

        } ],

        "references": [ {

                "type": string,

                "url": string

        } ],

        "ecosystem_specific": { see spec },

        "database_specific": { see spec },

}



This new vulnerability schema aims to address some key problems with managing vulnerabilities in open source. We found that there was no existing standard format which:

  • Enforces version specification that precisely matches naming and versioning schemes used in actual open source package ecosystems. For instance, matching a vulnerability such as a CVE to a package name and set of versions in a package manager is difficult to do in an automated way using existing mechanisms such as CPEs.
  • Can be used to describe vulnerabilities in any open source ecosystem, while not requiring ecosystem-dependent logic to process them.
  • Is easy to use by both automated systems and humans.

With this schema we hope to define a format that all vulnerability databases can export. A unified format means that vulnerability databases, open source users, and security researchers can easily share tooling and consume vulnerabilities across all of open source. This means a more complete view of vulnerabilities in open source for everyone, as well as faster detection and remediation times resulting from easier automation.

The current state


The vulnerability schema spec has gone through several iterations, and we are inviting further feedback as it gets closer to finalized. A number of public vulnerability databases today are already exporting this format, with more in the pipeline:
The OSV service has also aggregated all of these vulnerability databases, which are viewable at our web UI. They can also be queried with a single command via the same existing APIs:

  curl -X POST -d \

      '{"commit": "a46c08c533cfdf10260e74e2c03fa84a13b6c456"}' \

      "https://api.osv.dev/v1/query"

    

  curl -X POST -d \

      '{"version": "2.4.1", "package": {"name": "jinja2", "ecosystem": "PyPI"}}' \

      "https://api.osv.dev/v1/query"



Automating vulnerability database maintenance


Producing quality vulnerability data is also difficult. In addition to OSV’s existing automation, we built more automation tools for vulnerability database maintenance, and used these tools to bootstrap the community Python advisory database. This automation takes existing feeds, accurately matches them to packages, and generates entries containing precise, validated version ranges with minimal human intervention. We plan to extend this tooling to other ecosystems for which there is no existing vulnerability database, or little support for ongoing database maintenance.


Get involved


Thank you to all the open source developers who have provided feedback and adopted this format. We’re continuing to work with open source communities to develop this further and earn more widespread adoption in all ecosystems. If you are interested in adopting this format, we’d appreciate any feedback on our public spec.