Memory safety vulnerabilities remain a pervasive threat to software security. At Google, we believe the path to eliminating this class of vulnerabilities at scale and building high-assurance software lies in Safe Coding, a secure-by-design approach that prioritizes transitioning to memory-safe languages.
This post demonstrates why focusing on Safe Coding for new code quickly and counterintuitively reduces the overall security risk of a codebase, finally breaking through the stubbornly high plateau of memory safety vulnerabilities and starting an exponential decline, all while being scalable and cost-effective.
We’ll also share updated data on how the percentage of memory safety vulnerabilities in Android dropped from 76% to 24% over 6 years as development shifted to memory safe languages.
Consider a growing codebase primarily written in memory-unsafe languages, experiencing a constant influx of memory safety vulnerabilities. What happens if we gradually transition to memory-safe languages for new features, while leaving existing code mostly untouched except for bug fixes?
We can simulate the results. After some years, the code base has the following makeup1 as new memory unsafe development slows down, and new memory safe development starts to take over:
In the final year of our simulation, despite the growth in memory-unsafe code, the number of memory safety vulnerabilities drops significantly, a seemingly counterintuitive result not seen with other strategies:
This reduction might seem paradoxical: how is this possible when the quantity of new memory unsafe code actually grew?
The answer lies in an important observation: vulnerabilities decay exponentially. They have a half-life. The distribution of vulnerability lifetime follows an exponential distribution given an average vulnerability lifetime λ:
A large-scale study of vulnerability lifetimes2 published in 2022 in Usenix Security confirmed this phenomenon. Researchers found that the vast majority of vulnerabilities reside in new or recently modified code:
This confirms and generalizes our observation, published in 2021, that the density of Android’s memory safety bugs decreased with the age of the code, primarily residing in recent changes.
This leads to two important takeaways:
For example, based on the average vulnerability lifetimes, 5-year-old code has a 3.4x (using lifetimes from the study) to 7.4x (using lifetimes observed in Android and Chromium) lower vulnerability density than new code.
In real life, as with our simulation, when we start to prioritize prevention, the situation starts to rapidly improve.
The Android team began prioritizing transitioning new development to memory safe languages around 2019. This decision was driven by the increasing cost and complexity of managing memory safety vulnerabilities. There’s much left to do, but the results have already been positive. Here’s the big picture in 2024, looking at total code:
Despite the majority of code still being unsafe (but, crucially, getting progressively older), we’re seeing a large and continued decline in memory safety vulnerabilities. The results align with what we simulated above, and are even better, potentially as a result of our parallel efforts to improve the safety of our memory unsafe code. We first reported this decline in 2022, and we continue to see the total number of memory safety vulnerabilities dropping3. Note that the data for 2024 is extrapolated to the full year (represented as 36, but currently at 27 after the September security bulletin).
The percent of vulnerabilities caused by memory safety issues continues to correlate closely with the development language that’s used for new code. Memory safety issues, which accounted for 76% of Android vulnerabilities in 2019, and are currently 24% in 2024, well below the 70% industry norm, and continuing to drop.
As we noted in a previous post, memory safety vulnerabilities tend to be significantly more severe, more likely to be remotely reachable, more versatile, and more likely to be maliciously exploited than other vulnerability types. As the number of memory safety vulnerabilities have dropped, the overall security risk has dropped along with it.
Over the past decades, the industry has pioneered significant advancements to combat memory safety vulnerabilities, with each generation of advancements contributing valuable tools and techniques that have tangibly improved software security. However, with the benefit of hindsight, it’s evident that we have yet to achieve a truly scalable and sustainable solution that achieves an acceptable level of risk:
1st generation: reactive patching. The initial focus was mainly on fixing vulnerabilities reactively. For problems as rampant as memory safety, this incurs ongoing costs on the business and its users. Software manufacturers have to invest significant resources in responding to frequent incidents. This leads to constant security updates, leaving users vulnerable to unknown issues, and frequently albeit temporarily vulnerable to known issues, which are getting exploited ever faster.
2nd generation: proactive mitigating. The next approach consisted of reducing risk in vulnerable software, including a series of exploit mitigation strategies that raised the costs of crafting exploits. However, these mitigations, such as stack canaries and control-flow integrity, typically impose a recurring cost on products and development teams, often putting security and other product requirements in conflict:
3rd generation: proactive vulnerability discovery. The following generation focused on detecting vulnerabilities. This includes sanitizers, often paired with fuzzing like libfuzzer, many of which were built by Google. While helpful, these methods address the symptoms of memory unsafety, not the root cause. They typically require constant pressure to get teams to fuzz, triage, and fix their findings, resulting in low coverage. Even when applied thoroughly, fuzzing does not provide high assurance, as evidenced by vulnerabilities found in extensively fuzzed code.
Products across the industry have been significantly strengthened by these approaches, and we remain committed to responding to, mitigating, and proactively hunting for vulnerabilities. Having said that, it has become increasingly clear that those approaches are not only insufficient for reaching an acceptable level of risk in the memory-safety domain, but incur ongoing and increasing costs to developers, users, businesses, and products. As highlighted by numerous government agencies, including CISA, in their secure-by-design report, "only by incorporating secure by design practices will we break the vicious cycle of constantly creating and applying fixes."
The shift towards memory safe languages represents more than just a change in technology, it is a fundamental shift in how to approach security. This shift is not an unprecedented one, but rather a significant expansion of a proven approach. An approach that has already demonstrated remarkable success in eliminating other vulnerability classes like XSS.
The foundation of this shift is Safe Coding, which enforces security invariants directly into the development platform through language features, static analysis, and API design. The result is a secure by design ecosystem providing continuous assurance at scale, safe from the risk of accidentally introducing vulnerabilities.
The shift from previous generations to Safe Coding can be seen in the quantifiability of the assertions that are made when developing code. Instead of focusing on the interventions applied (mitigations, fuzzing), or attempting to use past performance to predict future security, Safe Coding allows us to make strong assertions about the code's properties and what can or cannot happen based on those properties.
Safe Coding's scalability lies in its ability to reduce costs by:
Based on what we’ve learned, it's become clear that we do not need to throw away or rewrite all our existing memory-unsafe code. Instead, Android is focusing on making interoperability safe and convenient as a primary capability in our memory safety journey. Interoperability offers a practical and incremental approach to adopting memory safe languages, allowing organizations to leverage existing investments in code and systems, while accelerating the development of new features.
We recommend focusing investments on improving interoperability, as we are doing with Rust ↔︎ C++ and Rust ↔︎ Kotlin. To that end, earlier this year, Google provided a $1,000,000 grant to the Rust Foundation, in addition to developing interoperability tooling like Crubit and autocxx.
As Safe Coding continues to drive down risk, what will be the role of mitigations and proactive detection? We don’t have definitive answers in Android, but expect something like the following:
Fighting against the math of vulnerability lifetimes has been a losing battle. Adopting Safe Coding in new code offers a paradigm shift, allowing us to leverage the inherent decay of vulnerabilities to our advantage, even in large existing systems. The concept is simple: once we turn off the tap of new vulnerabilities, they decrease exponentially, making all of our code safer, increasing the effectiveness of security design, and alleviating the scalability challenges associated with existing memory safety strategies such that they can be applied more effectively in a targeted manner.
This approach has proven successful in eliminating entire vulnerability classes and its effectiveness in tackling memory safety is increasingly evident based on more than half a decade of consistent results in Android.
We'll be sharing more about our secure-by-design efforts in the coming months.
Thanks Alice Ryhl for coding up the simulation. Thanks to Emilia Kasper, Adrian Taylor, Manish Goregaokar, Christoph Kern, and Lars Bergstrom for your helpful feedback on this post.
Simulation was based on numbers similar to Android and other Google projects. The code base doubles every 6 years. The average lifetime for vulnerabilities is 2.5 years. It takes 10 years to transition to memory safe languages for new code, and we use a sigmoid function to represent the transition. Note that the use of the sigmoid function is why the second chart doesn’t initially appear to be exponential. ↩
Alexopoulos et al. "How Long Do Vulnerabilities Live in the Code? A Large-Scale Empirical Measurement Study on FOSS Vulnerability Lifetimes". USENIX Security 22. ↩
Unlike our simulation, these are vulnerabilities from a real code base, which comes with higher variance, as you can see in the slight increase in 2023. Vulnerability reports were unusually high that year, but in line with expectations given code growth, so while the percentage of memory safety vulnerabilities continued to drop, the absolute number increased slightly. ↩
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