When you import a third party library, do you review every line of code? Most software packages depend on external libraries, trusting that those packages aren’t doing anything unexpected. If that trust is violated, the consequences can be huge—regardless of whether the package is malicious, or well-intended but using overly broad permissions, such as with Log4j in 2021. Supply chain security is a growing issue, and we hope that greater transparency into package capabilities will help make secure coding easier for everyone.

Avoiding bad dependencies can be hard without appropriate information on what the dependency’s code actually does, and reviewing every line of that code is an immense task.  Every dependency also brings its own dependencies, compounding the need for review across an expanding web of transitive dependencies. But what if there was an easy way to know the capabilities–the privileged operations accessed by the code–of your dependencies? 

Capslock is a capability analysis CLI tool that informs users of privileged operations (like network access and arbitrary code execution) in a given package and its dependencies. Last month we published the alpha version of Capslock for the Go language, which can analyze and report on the capabilities that are used beneath the surface of open source software. 

This CLI tool will provide deeper insights into the behavior of dependencies by reporting code paths that access privileged operations in the standard libraries. In upcoming versions we will add support for open source maintainers to prescribe and sandbox the capabilities required for their packages, highlighting to users what capabilities are present and alerting them if they change.

Capabilities vs Vulnerabilities

Vulnerability management is an important part of your supply chain security, but it doesn’t give you a full picture of whether your dependencies are safe to use. Adding capability analysis into your security posture, gives you a better idea of the types of behavior you can expect from your dependencies, identifies potential weak points, and allows you to make a more informed choice about using a given dependency. 

Capslock is motivated by the belief that the principle of least privilege—the idea that access should be limited to the minimal set that is feasible and practical—should be a first-class design concept for secure and usable software. Applied to software development, this means that a package should be allowed access only to the capabilities that it requires as part of its core behaviors. For example, you wouldn’t expect a data analysis package to need access to the network or a logging library to include remote code execution capabilities. 

Capslock is initially rolling out for Go, a language with a strong security commitment and fantastic tooling for finding known vulnerabilities in package dependencies. When Capslock is used alongside Go’s vulnerability management tools, developers can use the additional, complementary signals to inform how they interpret vulnerabilities in their dependencies. 

These capability signals can be used to

• Find code with the highest levels of access to prioritize audits, code reviews and vulnerability patches

• Compare potential dependencies, or look for alternative packages when an existing dependency is no longer appropriate

• Surface unwanted capability usage in packages to uncover new vulnerabilities or identify supply chain attacks in progress

• Monitor for unexpected emerging capabilities due to package version or dependency changes, and even integrate capability monitoring into CI/CD pipelines 

• Filter vulnerability data to respond to the most relevant cases, such as finding packages with network access during a network-specific vulnerability alert  

Using Capslock

We are looking forward to adding new features in future releases, such as better support for declaring the expected capabilities of a package, and extending to other programming languages. We are working to apply Capslock at scale and make capability information for open source packages broadly available in various community tools like deps.dev. 

You can try Capslock now, and we hope you find it useful for auditing your external dependencies and making informed decisions on your code’s capabilities.

We’ll be at Gophercon in San Diego on Sept 27th, 2023—come and chat with us! 

Since 2016, OSS-Fuzz has been at the forefront of automated vulnerability discovery for open source projects. Vulnerability discovery is an important part of keeping software supply chains secure, so our team is constantly working to improve OSS-Fuzz. For the last few months, we’ve tested whether we could boost OSS-Fuzz’s performance using Google’s Large Language Models (LLM). 

This blog post shares our experience of successfully applying the generative power of LLMs to improve the automated vulnerability detection technique known as fuzz testing (“fuzzing”). By using LLMs, we’re able to increase the code coverage for critical projects using our OSS-Fuzz service without manually writing additional code. Using LLMs is a promising new way to scale security improvements across the over 1,000 projects currently fuzzed by OSS-Fuzz and to remove barriers to future projects adopting fuzzing. 

LLM-aided fuzzing

We created the OSS-Fuzz service to help open source developers find bugs in their code at scale—especially bugs that indicate security vulnerabilities. After more than six years of running OSS-Fuzz, we now support over 1,000 open source projects with continuous fuzzing, free of charge. As the Heartbleed vulnerability showed us, bugs that could be easily found with automated fuzzing can have devastating effects. For most open source developers, setting up their own fuzzing solution could cost time and resources. With OSS-Fuzz, developers are able to integrate their project for free, automated bug discovery at scale.  

Since 2016, we’ve found and verified a fix for over 10,000 security vulnerabilities. We also believe that OSS-Fuzz could likely find even more bugs with increased code coverage. The fuzzing service covers only around 30% of an open source project’s code on average, meaning that a large portion of our users’ code remains untouched by fuzzing. Recent research suggests that the most effective way to increase this is by adding additional fuzz targets for every project—one of the few parts of the fuzzing workflow that isn’t yet automated.

When an open source project onboards to OSS-Fuzz, maintainers make an initial time investment to integrate their projects into the infrastructure and then add fuzz targets. The fuzz targets are functions that use randomized input to test the targeted code. Writing fuzz targets is a project-specific and manual process that is similar to writing unit tests. The ongoing security benefits from fuzzing make this initial investment of time worth it for maintainers, but writing a comprehensive set of fuzz targets is an tough expectation for project maintainers, who are often volunteers. 

But what if LLMs could write additional fuzz targets for maintainers?

“Hey LLM, fuzz this project for me”

To discover whether an LLM could successfully write new fuzz targets, we built an evaluation framework that connects OSS-Fuzz to the LLM, conducts the experiment, and evaluates the results. The steps look like this:  

1. OSS-Fuzz’s Fuzz Introspector tool identifies an under-fuzzed, high-potential portion of the sample project’s code and passes the code to the evaluation framework. 

2. The evaluation framework creates a prompt that the LLM will use to write the new fuzz target. The prompt includes project-specific information.

3. The evaluation framework takes the fuzz target generated by the LLM and runs the new target. 

4. The evaluation framework observes the run for any change in code coverage.

5. In the event that the fuzz target fails to compile, the evaluation framework prompts the LLM to write a revised fuzz target that addresses the compilation errors.

Experiment overview: The experiment pictured above is a fully automated process, from identifying target code to evaluating the change in code coverage.

At first, the code generated from our prompts wouldn’t compile, however after several rounds of  prompt engineering and trying out the new fuzz targets, we saw projects gain between 1.5% and 31% code coverage. One of our sample projects, tinyxml2, went from 38% line coverage to 69% without any interventions from our team. The case of tinyxml2 taught us: when LLM-generated fuzz targets are added, tinyxml2 has the majority of its code covered. 

Example fuzz targets for tinyxml2: Each of the five fuzz targets shown is associated with a different part of the code and adds to the overall coverage improvement. 

To replicate tinyxml2’s results manually would have required at least a day’s worth of work—which would mean several years of work to manually cover all OSS-Fuzz projects. Given tinyxml2’s promising results, we want to implement them in production and to extend similar, automatic coverage to other OSS-Fuzz projects. 

Additionally, in the OpenSSL project, our LLM was able to automatically generate a working target that rediscovered CVE-2022-3602, which was in an area of code that previously did not have fuzzing coverage. Though this is not a new vulnerability, it suggests that as code coverage increases, we will find more vulnerabilities that are currently missed by fuzzing. 

Learn more about our results through our example prompts and outputs or through our experiment report. 

The goal: fully automated fuzzing

In the next few months, we’ll open source our evaluation framework to allow researchers to test their own automatic fuzz target generation. We’ll continue to optimize our use of LLMs for fuzzing target generation through more model finetuning, prompt engineering, and improvements to our infrastructure. We’re also collaborating closely with the Assured OSS team on this research in order to secure even more open source software used by Google Cloud customers.   

Our longer term goals include:

• Adding LLM fuzz target generation as a fully integrated feature in OSS-Fuzz, with continuous generation of new targets for OSS-fuzz projects and zero manual involvement.

• Extending support from C/C++ projects to additional language ecosystems, like Python and Java. 

• Automating the process of onboarding a project into OSS-Fuzz to eliminate any need to write even initial fuzz targets. 

We’re working towards a future of personalized vulnerability detection with little manual effort from developers. With the addition of LLM generated fuzz targets, OSS-Fuzz can help improve open source security for everyone. 

Since 2016, OSS-Fuzz has been at the forefront of automated vulnerability discovery for open source projects. Vulnerability discovery is an important part of keeping software supply chains secure, so our team is constantly working to improve OSS-Fuzz. For the last few months, we’ve tested whether we could boost OSS-Fuzz’s performance using Google’s Large Language Models (LLM). 

This blog post shares our experience of successfully applying the generative power of LLMs to improve the automated vulnerability detection technique known as fuzz testing (“fuzzing”). By using LLMs, we’re able to increase the code coverage for critical projects using our OSS-Fuzz service without manually writing additional code. Using LLMs is a promising new way to scale security improvements across the over 1,000 projects currently fuzzed by OSS-Fuzz and to remove barriers to future projects adopting fuzzing. 

LLM-aided fuzzing

We created the OSS-Fuzz service to help open source developers find bugs in their code at scale—especially bugs that indicate security vulnerabilities. After more than six years of running OSS-Fuzz, we now support over 1,000 open source projects with continuous fuzzing, free of charge. As the Heartbleed vulnerability showed us, bugs that could be easily found with automated fuzzing can have devastating effects. For most open source developers, setting up their own fuzzing solution could cost time and resources. With OSS-Fuzz, developers are able to integrate their project for free, automated bug discovery at scale.  

Since 2016, we’ve found and verified a fix for over 10,000 security vulnerabilities. We also believe that OSS-Fuzz could likely find even more bugs with increased code coverage. The fuzzing service covers only around 30% of an open source project’s code on average, meaning that a large portion of our users’ code remains untouched by fuzzing. Recent research suggests that the most effective way to increase this is by adding additional fuzz targets for every project—one of the few parts of the fuzzing workflow that isn’t yet automated.

When an open source project onboards to OSS-Fuzz, maintainers make an initial time investment to integrate their projects into the infrastructure and then add fuzz targets. The fuzz targets are functions that use randomized input to test the targeted code. Writing fuzz targets is a project-specific and manual process that is similar to writing unit tests. The ongoing security benefits from fuzzing make this initial investment of time worth it for maintainers, but writing a comprehensive set of fuzz targets is an tough expectation for project maintainers, who are often volunteers. 

But what if LLMs could write additional fuzz targets for maintainers?

“Hey LLM, fuzz this project for me”

To discover whether an LLM could successfully write new fuzz targets, we built an evaluation framework that connects OSS-Fuzz to the LLM, conducts the experiment, and evaluates the results. The steps look like this:  

1. OSS-Fuzz’s Fuzz Introspector tool identifies an under-fuzzed, high-potential portion of the sample project’s code and passes the code to the evaluation framework. 

2. The evaluation framework creates a prompt that the LLM will use to write the new fuzz target. The prompt includes project-specific information.

3. The evaluation framework takes the fuzz target generated by the LLM and runs the new target. 

4. The evaluation framework observes the run for any change in code coverage.

5. In the event that the fuzz target fails to compile, the evaluation framework prompts the LLM to write a revised fuzz target that addresses the compilation errors.

Experiment overview: The experiment pictured above is a fully automated process, from identifying target code to evaluating the change in code coverage.

At first, the code generated from our prompts wouldn’t compile, however after several rounds of  prompt engineering and trying out the new fuzz targets, we saw projects gain between 1.5% and 31% code coverage. One of our sample projects, tinyxml2, went from 38% line coverage to 69% without any interventions from our team. The case of tinyxml2 taught us: when LLM-generated fuzz targets are added, tinyxml2 has the majority of its code covered. 

Example fuzz targets for tinyxml2: Each of the five fuzz targets shown is associated with a different part of the code and adds to the overall coverage improvement. 

To replicate tinyxml2’s results manually would have required at least a day’s worth of work—which would mean several years of work to manually cover all OSS-Fuzz projects. Given tinyxml2’s promising results, we want to implement them in production and to extend similar, automatic coverage to other OSS-Fuzz projects. 

Additionally, in the OpenSSL project, our LLM was able to automatically generate a working target that rediscovered CVE-2022-3602, which was in an area of code that previously did not have fuzzing coverage. Though this is not a new vulnerability, it suggests that as code coverage increases, we will find more vulnerabilities that are currently missed by fuzzing. 

Learn more about our results through our example prompts and outputs or through our experiment report. 

The goal: fully automated fuzzing

In the next few months, we’ll open source our evaluation framework to allow researchers to test their own automatic fuzz target generation. We’ll continue to optimize our use of LLMs for fuzzing target generation through more model finetuning, prompt engineering, and improvements to our infrastructure. We’re also collaborating closely with the Assured OSS team on this research in order to secure even more open source software used by Google Cloud customers.   

Our longer term goals include:

• Adding LLM fuzz target generation as a fully integrated feature in OSS-Fuzz, with continuous generation of new targets for OSS-fuzz projects and zero manual involvement.

• Extending support from C/C++ projects to additional language ecosystems, like Python and Java. 

• Automating the process of onboarding a project into OSS-Fuzz to eliminate any need to write even initial fuzz targets. 

We’re working towards a future of personalized vulnerability detection with little manual effort from developers. With the addition of LLM generated fuzz targets, OSS-Fuzz can help improve open source security for everyone. 

Since 2016, OSS-Fuzz has been at the forefront of automated vulnerability discovery for open source projects. Vulnerability discovery is an important part of keeping software supply chains secure, so our team is constantly working to improve OSS-Fuzz. For the last few months, we’ve tested whether we could boost OSS-Fuzz’s performance using Google’s Large Language Models (LLM). 

This blog post shares our experience of successfully applying the generative power of LLMs to improve the automated vulnerability detection technique known as fuzz testing (“fuzzing”). By using LLMs, we’re able to increase the code coverage for critical projects using our OSS-Fuzz service without manually writing additional code. Using LLMs is a promising new way to scale security improvements across the over 1,000 projects currently fuzzed by OSS-Fuzz and to remove barriers to future projects adopting fuzzing. 

LLM-aided fuzzing

We created the OSS-Fuzz service to help open source developers find bugs in their code at scale—especially bugs that indicate security vulnerabilities. After more than six years of running OSS-Fuzz, we now support over 1,000 open source projects with continuous fuzzing, free of charge. As the Heartbleed vulnerability showed us, bugs that could be easily found with automated fuzzing can have devastating effects. For most open source developers, setting up their own fuzzing solution could cost time and resources. With OSS-Fuzz, developers are able to integrate their project for free, automated bug discovery at scale.  

Since 2016, we’ve found and verified a fix for over 10,000 security vulnerabilities. We also believe that OSS-Fuzz could likely find even more bugs with increased code coverage. The fuzzing service covers only around 30% of an open source project’s code on average, meaning that a large portion of our users’ code remains untouched by fuzzing. Recent research suggests that the most effective way to increase this is by adding additional fuzz targets for every project—one of the few parts of the fuzzing workflow that isn’t yet automated.

When an open source project onboards to OSS-Fuzz, maintainers make an initial time investment to integrate their projects into the infrastructure and then add fuzz targets. The fuzz targets are functions that use randomized input to test the targeted code. Writing fuzz targets is a project-specific and manual process that is similar to writing unit tests. The ongoing security benefits from fuzzing make this initial investment of time worth it for maintainers, but writing a comprehensive set of fuzz targets is an tough expectation for project maintainers, who are often volunteers. 

But what if LLMs could write additional fuzz targets for maintainers?

“Hey LLM, fuzz this project for me”

To discover whether an LLM could successfully write new fuzz targets, we built an evaluation framework that connects OSS-Fuzz to the LLM, conducts the experiment, and evaluates the results. The steps look like this:  

1. OSS-Fuzz’s Fuzz Introspector tool identifies an under-fuzzed, high-potential portion of the sample project’s code and passes the code to the evaluation framework. 

2. The evaluation framework creates a prompt that the LLM will use to write the new fuzz target. The prompt includes project-specific information.

3. The evaluation framework takes the fuzz target generated by the LLM and runs the new target. 

4. The evaluation framework observes the run for any change in code coverage.

5. In the event that the fuzz target fails to compile, the evaluation framework prompts the LLM to write a revised fuzz target that addresses the compilation errors.

Experiment overview: The experiment pictured above is a fully automated process, from identifying target code to evaluating the change in code coverage.

At first, the code generated from our prompts wouldn’t compile, however after several rounds of  prompt engineering and trying out the new fuzz targets, we saw projects gain between 1.5% and 31% code coverage. One of our sample projects, tinyxml2, went from 38% line coverage to 69% without any interventions from our team. The case of tinyxml2 taught us: when LLM-generated fuzz targets are added, tinyxml2 has the majority of its code covered. 

Example fuzz targets for tinyxml2: Each of the five fuzz targets shown is associated with a different part of the code and adds to the overall coverage improvement. 

To replicate tinyxml2’s results manually would have required at least a day’s worth of work—which would mean several years of work to manually cover all OSS-Fuzz projects. Given tinyxml2’s promising results, we want to implement them in production and to extend similar, automatic coverage to other OSS-Fuzz projects. 

Additionally, in the OpenSSL project, our LLM was able to automatically generate a working target that rediscovered CVE-2022-3602, which was in an area of code that previously did not have fuzzing coverage. Though this is not a new vulnerability, it suggests that as code coverage increases, we will find more vulnerabilities that are currently missed by fuzzing. 

Learn more about our results through our example prompts and outputs or through our experiment report. 

The goal: fully automated fuzzing

In the next few months, we’ll open source our evaluation framework to allow researchers to test their own automatic fuzz target generation. We’ll continue to optimize our use of LLMs for fuzzing target generation through more model finetuning, prompt engineering, and improvements to our infrastructure. We’re also collaborating closely with the Assured OSS team on this research in order to secure even more open source software used by Google Cloud customers.   

Our longer term goals include:

• Adding LLM fuzz target generation as a fully integrated feature in OSS-Fuzz, with continuous generation of new targets for OSS-fuzz projects and zero manual involvement.

• Extending support from C/C++ projects to additional language ecosystems, like Python and Java. 

• Automating the process of onboarding a project into OSS-Fuzz to eliminate any need to write even initial fuzz targets. 

We’re working towards a future of personalized vulnerability detection with little manual effort from developers. With the addition of LLM generated fuzz targets, OSS-Fuzz can help improve open source security for everyone. 

Since 2016, OSS-Fuzz has been at the forefront of automated vulnerability discovery for open source projects. Vulnerability discovery is an important part of keeping software supply chains secure, so our team is constantly working to improve OSS-Fuzz. For the last few months, we’ve tested whether we could boost OSS-Fuzz’s performance using Google’s Large Language Models (LLM). 

This blog post shares our experience of successfully applying the generative power of LLMs to improve the automated vulnerability detection technique known as fuzz testing (“fuzzing”). By using LLMs, we’re able to increase the code coverage for critical projects using our OSS-Fuzz service without manually writing additional code. Using LLMs is a promising new way to scale security improvements across the over 1,000 projects currently fuzzed by OSS-Fuzz and to remove barriers to future projects adopting fuzzing. 

LLM-aided fuzzing

We created the OSS-Fuzz service to help open source developers find bugs in their code at scale—especially bugs that indicate security vulnerabilities. After more than six years of running OSS-Fuzz, we now support over 1,000 open source projects with continuous fuzzing, free of charge. As the Heartbleed vulnerability showed us, bugs that could be easily found with automated fuzzing can have devastating effects. For most open source developers, setting up their own fuzzing solution could cost time and resources. With OSS-Fuzz, developers are able to integrate their project for free, automated bug discovery at scale.  

Since 2016, we’ve found and verified a fix for over 10,000 security vulnerabilities. We also believe that OSS-Fuzz could likely find even more bugs with increased code coverage. The fuzzing service covers only around 30% of an open source project’s code on average, meaning that a large portion of our users’ code remains untouched by fuzzing. Recent research suggests that the most effective way to increase this is by adding additional fuzz targets for every project—one of the few parts of the fuzzing workflow that isn’t yet automated.

When an open source project onboards to OSS-Fuzz, maintainers make an initial time investment to integrate their projects into the infrastructure and then add fuzz targets. The fuzz targets are functions that use randomized input to test the targeted code. Writing fuzz targets is a project-specific and manual process that is similar to writing unit tests. The ongoing security benefits from fuzzing make this initial investment of time worth it for maintainers, but writing a comprehensive set of fuzz targets is an tough expectation for project maintainers, who are often volunteers. 

But what if LLMs could write additional fuzz targets for maintainers?

“Hey LLM, fuzz this project for me”

To discover whether an LLM could successfully write new fuzz targets, we built an evaluation framework that connects OSS-Fuzz to the LLM, conducts the experiment, and evaluates the results. The steps look like this:  

1. OSS-Fuzz’s Fuzz Introspector tool identifies an under-fuzzed, high-potential portion of the sample project’s code and passes the code to the evaluation framework. 

2. The evaluation framework creates a prompt that the LLM will use to write the new fuzz target. The prompt includes project-specific information.

3. The evaluation framework takes the fuzz target generated by the LLM and runs the new target. 

4. The evaluation framework observes the run for any change in code coverage.

5. In the event that the fuzz target fails to compile, the evaluation framework prompts the LLM to write a revised fuzz target that addresses the compilation errors.

Experiment overview: The experiment pictured above is a fully automated process, from identifying target code to evaluating the change in code coverage.

At first, the code generated from our prompts wouldn’t compile, however after several rounds of  prompt engineering and trying out the new fuzz targets, we saw projects gain between 1.5% and 31% code coverage. One of our sample projects, tinyxml2, went from 38% line coverage to 69% without any interventions from our team. The case of tinyxml2 taught us: when LLM-generated fuzz targets are added, tinyxml2 has the majority of its code covered. 

Example fuzz targets for tinyxml2: Each of the five fuzz targets shown is associated with a different part of the code and adds to the overall coverage improvement. 

To replicate tinyxml2’s results manually would have required at least a day’s worth of work—which would mean several years of work to manually cover all OSS-Fuzz projects. Given tinyxml2’s promising results, we want to implement them in production and to extend similar, automatic coverage to other OSS-Fuzz projects. 

Additionally, in the OpenSSL project, our LLM was able to automatically generate a working target that rediscovered CVE-2022-3602, which was in an area of code that previously did not have fuzzing coverage. Though this is not a new vulnerability, it suggests that as code coverage increases, we will find more vulnerabilities that are currently missed by fuzzing. 

Learn more about our results through our example prompts and outputs or through our experiment report. 

The goal: fully automated fuzzing

In the next few months, we’ll open source our evaluation framework to allow researchers to test their own automatic fuzz target generation. We’ll continue to optimize our use of LLMs for fuzzing target generation through more model finetuning, prompt engineering, and improvements to our infrastructure. We’re also collaborating closely with the Assured OSS team on this research in order to secure even more open source software used by Google Cloud customers.   

Our longer term goals include:

• Adding LLM fuzz target generation as a fully integrated feature in OSS-Fuzz, with continuous generation of new targets for OSS-fuzz projects and zero manual involvement.

• Extending support from C/C++ projects to additional language ecosystems, like Python and Java. 

• Automating the process of onboarding a project into OSS-Fuzz to eliminate any need to write even initial fuzz targets. 

We’re working towards a future of personalized vulnerability detection with little manual effort from developers. With the addition of LLM generated fuzz targets, OSS-Fuzz can help improve open source security for everyone. 

As part of our effort to deploy quantum resistant cryptography, we are happy to announce the release of the first quantum resilient FIDO2 security key implementation as part of OpenSK, our open source security key firmware. This open-source hardware optimized implementation uses a novel ECC/Dilithium hybrid signature schema that benefits from the security of ECC against standard attacks and Dilithium’s resilience against quantum attacks. This schema was co-developed in partnership with the ETH Zürich and won the ACNS secure cryptographic implementation workshop best paper.

Quantum processor

As progress toward practical quantum computers is accelerating, preparing for their advent is becoming a more pressing issue as time passes. In particular, standard public key cryptography which was designed to protect against traditional computers, will not be able to withstand quantum attacks. Fortunately, with the recent standardization of public key quantum resilient cryptography including the Dilithium algorithm, we now have a clear path to secure security keys against quantum attacks.

While quantum attacks are still in the distant future, deploying cryptography at Internet scale is a massive undertaking which is why doing it as early as possible is vital. In particular, for security keys this process is expected to be gradual as users will have to acquire new ones once FIDO has standardized post quantum cryptography resilient cryptography and this new standard is supported by major browser vendors.

Hybrid signature: Strong nesting with classical and PQC scheme

Our proposed implementation relies on a hybrid approach that combines the battle tested ECDSA signature algorithm and the recently standardized quantum resistant signature algorithm, Dilithium. In collaboration with ETH, we developed this novel hybrid signature schema that offers the best of both worlds. Relying on a hybrid signature is critical as the security of Dilithium and other recently standardized quantum resistant algorithms haven’t yet stood the test of time and recent attacks on Rainbow (another quantum resilient algorithm) demonstrate the need for caution. This cautiousness is particularly warranted for security keys as most can’t be upgraded – although we are working toward it for OpenSK. The hybrid approach is also used in other post-quantum efforts like Chrome’s support for TLS.

On the technical side, a large challenge was to create a Dilithium implementation small enough to run on security keys’ constrained hardware. Through careful optimization, we were able to develop a Rust memory optimized implementation that only required 20 KB of memory, which was sufficiently small enough. We also spent time ensuring that our implementation signature speed was well within the expected security keys specification. That said, we believe improving signature speed further by leveraging hardware acceleration would allow for keys to be more responsive.

Moving forward, we are hoping  to see this implementation (or a variant of it), being standardized as part of the FIDO2 key specification and supported by major web browsers so that users' credentials can be protected against quantum attacks. If you are interested in testing this algorithm or contributing to security key research, head to our open source implementation OpenSK.

Finding and mitigating security vulnerabilities is critical to keeping Internet users safe.  However, the more complex a system becomes, the harder it is to secure—and that is also the case with computing hardware and processors, which have developed highly advanced capabilities over the years. This post will detail this trend by exploring Downfall and Zenbleed, two new security vulnerabilities (one of which was disclosed today) that prior to mitigation had the potential to affect billions of personal and cloud computers, signifying the importance of vulnerability research and cross-industry collaboration. Had these vulnerabilities not been discovered by Google researchers, and instead by adversaries, they would have enabled attackers to compromise Internet users. For both vulnerabilities, Google worked closely with our partners in the industry to develop fixes, deploy mitigations and gather details to share widely and better secure the ecosystem.

What are Downfall and Zenbleed?

Downfall (CVE-2022-40982) and Zenbleed (CVE-2023-20593) are two different vulnerabilities affecting CPUs - Intel Core (6th - 11th generation) and AMD Zen2, respectively. They allow an attacker to violate the software-hardware boundary established in modern processors. This could allow an attacker to access data in internal hardware registers that hold information belonging to other users of the system (both across different virtual machines and different processes). 

These vulnerabilities arise from complex optimizations in modern CPUs that speed up applications: 

1. Preemptive multitasking and simultaneous multithreading enable users and applications to share CPU cores, while the CPU enforces security boundaries at the architecture level to stop a malicious user accessing data from other users. 

2. Speculative execution allows the CPU core to execute instructions from a single execution thread without waiting for prior instructions to be completed.

3. SIMD enables data-level parallelism where an instruction computes the same function multiple times with different data.

Downfall, affecting Intel CPUs, exploits the speculative forwarding of data from the SIMD Gather instruction. The Gather instruction helps the software access scattered data in memory quickly, which is crucial for high-performance computing workloads performing data encoding and processing. Downfall shows that this instruction forwards stale data from the internal physical hardware registers to succeeding instructions. Although this data is not directly exposed to software registers, it can trivially be extracted via similar exploitation techniques as Meltdown. Since these physical hardware register files are shared across multiple users sharing the same CPU core, an attacker can ultimately extract data from other users. 

Zenbleed, affecting AMD CPUs, shows that incorrectly implemented speculative execution of the SIMD Zeroupper instruction leaks stale data from physical hardware registers to software registers. Zeroupper instructions should clear the data in the upper-half of SIMD registers (e.g., 256-bit register YMM) which on Zen2 processors is done by just setting a flag that marks the upper half of the register as zero. However, if on the same cycle as a register to register move the Zeroupper instruction is mis-speculated, the zero flag doesn’t get rolled back properly, leading to the upper-half of the YMM register to hold stale data rather than the value of zero. Similar to Downfall, leaking stale data from physical hardware registers expose the data from other users who share the same CPU core and its internal physical registers. 

Comparison

How did we protect our users?

Vulnerability research continues to be at the heart of our security work at Google. We invest in not only vulnerability research, but in the community as a whole in order to encourage further research that keeps all users safe. These vulnerabilities were no exception, and we worked closely with our industry partners to make them aware of the vulnerabilities, coordinate on mitigations, align on disclosure timelines and a plan to get details out to the ecosystem. 

Upon disclosures, we immediately published Security Bulletins for both Downfall and Zenbleed that detailed how Google responded to each vulnerability, and provided guidance for the industry. In addition to our bulletins, we posted technical details for insights on both Downfall and Zenbleed. It’s imperative that vulnerability research continues to be supported by the industry, and we’re dedicated to doing our part to helping protect those that do this important work.

Lessons learned 

These long existing vulnerabilities, their discovery and the mitigations that followed have provided several lessons learned that will help the industry move forward in vulnerability research, including: 

• There are fundamental challenges in designing secure hardware that requires further research and understanding.

• There are gaps in automated testing and verification of hardware for vulnerabilities. 

• Optimization features that are supposed to make computation faster are closely related to security and can introduce new vulnerabilities, if not implemented properly.

As Downfall and Zenbleed, suggest, computer hardware is only becoming more complex everyday, and so we will see more vulnerabilities, which is why Google is investing in CPU/hardware security research. We look forward to continuing to share our insights and encourage the wider industry to join us in helping to expand on this work. 

Want to learn more?

Downfall will be presented at Blackhat USA 2023 on August 9 at 1:30pm. You can also read more about Zenbleed on this advisory.