Memory security vulnerabilities stay a pervasive risk to software program safety. At Google, we imagine the trail to eliminating this class of vulnerabilities at scale and constructing high-assurance software program lies in Safe Coding, a secure-by-design method that prioritizes transitioning to memory-safe languages.
This publish demonstrates why specializing in Safe Coding for brand new code rapidly and counterintuitively reduces the general safety threat of a codebase, lastly breaking by the stubbornly excessive plateau of reminiscence security vulnerabilities and beginning an exponential decline, all whereas being scalable and cost-effective.
We’ll additionally share up to date knowledge on how the share of reminiscence security vulnerabilities in Android dropped from 76% to 24% over 6 years as improvement shifted to reminiscence secure languages.
Consider a rising codebase primarily written in memory-unsafe languages, experiencing a relentless inflow of reminiscence security vulnerabilities. What occurs if we regularly transition to memory-safe languages for brand new options, whereas leaving present code principally untouched apart from bug fixes?
We can simulate the outcomes. After some years, the code base has the next make-up1 as new reminiscence unsafe improvement slows down, and new reminiscence secure improvement begins to take over:
In the ultimate yr of our simulation, regardless of the expansion in memory-unsafe code, the variety of reminiscence security vulnerabilities drops considerably, a seemingly counterintuitive end result not seen with different methods:
This discount might sound paradoxical: how is that this doable when the amount of recent reminiscence unsafe code truly grew?
The reply lies in an vital statement: vulnerabilities decay exponentially. They have a half-life. The distribution of vulnerability lifetime follows an exponential distribution given a median vulnerability lifetime λ:
A big-scale examine of vulnerability lifetimes2 revealed in 2022 in Usenix Security confirmed this phenomenon. Researchers discovered that the overwhelming majority of vulnerabilities reside in new or just lately modified code:
This confirms and generalizes our observation, revealed in 2021, that the density of Android’s reminiscence security bugs decreased with the age of the code, primarily residing in latest modifications.
This results in two vital takeaways:
- The drawback is overwhelmingly with new code, necessitating a basic change in how we develop code.
- Code matures and will get safer with time, exponentially, making the returns on investments like rewrites diminish over time as code will get older.
For instance, primarily based on the common vulnerability lifetimes, 5-year-old code has a 3.4x (utilizing lifetimes from the examine) to 7.4x (utilizing lifetimes noticed in Android and Chromium) decrease vulnerability density than new code.
In actual life, as with our simulation, after we begin to prioritize prevention, the scenario begins to quickly enhance.
The Android group started prioritizing transitioning new improvement to reminiscence secure languages round 2019. This choice was pushed by the rising value and complexity of managing reminiscence security vulnerabilities. There’s a lot left to do, however the outcomes have already been optimistic. Here’s the large image in 2024, complete code:
Despite nearly all of code nonetheless being unsafe (however, crucially, getting progressively older), we’re seeing a big and continued decline in reminiscence security vulnerabilities. The outcomes align with what we simulated above, and are even higher, probably because of our parallel efforts to enhance the security of our reminiscence unsafe code. We first reported this decline in 2022, and we proceed to see the entire variety of reminiscence security vulnerabilities dropping3. Note that the info for 2024 is extrapolated to the complete yr (represented as 36, however presently at 27 after the September safety bulletin).
The % of vulnerabilities brought on by reminiscence questions of safety continues to correlate carefully with the event language that’s used for brand new code. Memory questions of safety, which accounted for 76% of Android vulnerabilities in 2019, and are presently 24% in 2024, nicely beneath the 70% trade norm, and persevering with to drop.
As we famous in a earlier publish, reminiscence security vulnerabilities are typically considerably extra extreme, extra more likely to be remotely reachable, extra versatile, and extra more likely to be maliciously exploited than different vulnerability sorts. As the variety of reminiscence security vulnerabilities have dropped, the general safety threat has dropped together with it.
Over the previous many years, the trade has pioneered important developments to fight reminiscence security vulnerabilities, with every technology of developments contributing helpful instruments and methods which have tangibly improved software program safety. However, with the advantage of hindsight, it’s evident that we’ve got but to realize a very scalable and sustainable resolution that achieves a suitable stage of threat:
1st technology: reactive patching. The preliminary focus was primarily on fixing vulnerabilities reactively. For issues as rampant as reminiscence security, this incurs ongoing prices on the enterprise and its customers. Software producers have to speculate important sources in responding to frequent incidents. This results in fixed safety updates, leaving customers susceptible to unknown points, and regularly albeit briefly susceptible to identified points, that are getting exploited ever sooner.
2nd technology: proactive mitigating. The subsequent method consisted of decreasing threat in susceptible software program, together with a collection of exploit mitigation methods that raised the prices of crafting exploits. However, these mitigations, corresponding to stack canaries and control-flow integrity, sometimes impose a recurring value on merchandise and improvement groups, usually placing safety and different product necessities in battle:
- They include efficiency overhead, impacting execution velocity, battery life, tail latencies, and reminiscence utilization, typically stopping their deployment.
- Attackers are seemingly infinitely artistic, leading to a cat-and-mouse sport with defenders. In addition, the bar to develop and weaponize an exploit is repeatedly being lowered by higher tooling and different advancements.
third technology: proactive vulnerability discovery. The following technology centered on detecting vulnerabilities. This contains sanitizers, usually paired with fuzzing like libfuzzer, lots of which had been constructed by Google. While useful, these strategies tackle the signs of reminiscence unsafety, not the basis trigger. They sometimes require fixed stress to get groups to fuzz, triage, and repair their findings, leading to low protection. Even when utilized completely, fuzzing doesn’t present excessive assurance, as evidenced by vulnerabilities present in extensively fuzzed code.
Products throughout the trade have been considerably strengthened by these approaches, and we stay dedicated to responding to, mitigating, and proactively attempting to find vulnerabilities. Having mentioned that, it has grow to be more and more clear that these approaches usually are not solely inadequate for reaching a suitable stage of threat within the memory-safety area, however incur ongoing and rising prices to builders, customers, companies, and merchandise. As highlighted by quite a few authorities companies, together with CISA, of their secure-by-design report, “solely by incorporating safe by design practices will we break the vicious cycle of continually creating and making use of fixes.”
The shift in the direction of reminiscence secure languages represents greater than only a change in know-how, it’s a basic shift in how one can method safety. This shift will not be an unprecedented one, however slightly a big enlargement of a confirmed method. An method that has already demonstrated outstanding success in eliminating different vulnerability courses like XSS.
The basis of this shift is Safe Coding, which enforces safety invariants instantly into the event platform by language options, static evaluation, and API design. The result’s a safe by design ecosystem offering steady assurance at scale, secure from the danger of unintentionally introducing vulnerabilities.
The shift from earlier generations to Safe Coding could be seen within the quantifiability of the assertions which are made when growing code. Instead of specializing in the interventions utilized (mitigations, fuzzing), or making an attempt to make use of previous efficiency to foretell future safety, Safe Coding permits us to make sturdy assertions concerning the code’s properties and what can or can’t occur primarily based on these properties.
Safe Coding’s scalability lies in its capacity to cut back prices by:
- Breaking the arms race: Instead of an countless arms race of defenders making an attempt to boost attackers’ prices by additionally elevating their very own, Safe Coding leverages our management of developer ecosystems to interrupt this cycle by specializing in proactively constructing safe software program from the beginning.
- Commoditizing excessive assurance reminiscence security: Rather than exactly tailoring interventions to every asset’s assessed threat, all whereas managing the price and overhead of reassessing evolving dangers and making use of disparate interventions, Safe Coding establishes a excessive baseline of commoditized safety, like memory-safe languages, that affordably reduces vulnerability density throughout the board. Modern memory-safe languages (particularly Rust) prolong these rules past reminiscence security to different bug courses.
- Increasing productiveness: Safe Coding improves code correctness and developer productiveness by shifting bug discovering additional left, earlier than the code is even checked in. We see this shift exhibiting up in vital metrics corresponding to rollback charges (emergency code revert as a result of an unanticipated bug). The Android group has noticed that the rollback charge of Rust modifications is lower than half that of C++.
Interoperability is the brand new rewrite
Based on what we’ve realized, it is grow to be clear that we don’t must throw away or rewrite all our present memory-unsafe code. Instead, Android is specializing in making interoperability secure and handy as a main functionality in our reminiscence security journey. Interoperability provides a sensible and incremental method to adopting reminiscence secure languages, permitting organizations to leverage present investments in code and programs, whereas accelerating the event of recent options.
We suggest focusing investments on enhancing interoperability, as we’re doing with Rust ↔︎ C++ and Rust ↔︎ Kotlin. To that finish, earlier this yr, Google supplied a $1,000,000 grant to the Rust Foundation, along with growing interoperability tooling like Crubit and autocxx.
Role of earlier generations
As Safe Coding continues to drive down threat, what would be the function of mitigations and proactive detection? We don’t have definitive solutions in Android, however anticipate one thing like the next:
- More selective use of proactive mitigations: We anticipate much less reliance on exploit mitigations as we transition to memory-safe code, resulting in not solely safer software program, but additionally extra environment friendly software program. For occasion, after eradicating the now pointless sandbox, Chromium’s Rust QR code generator is 20 occasions sooner.
- Decreased use, however elevated effectiveness of proactive detection: We anticipate a decreased reliance on proactive detection approaches like fuzzing, however elevated effectiveness, as attaining complete protection over small well-encapsulated code snippets turns into extra possible.
Fighting in opposition to the mathematics of vulnerability lifetimes has been a dropping battle. Adopting Safe Coding in new code provides a paradigm shift, permitting us to leverage the inherent decay of vulnerabilities to our benefit, even in massive present programs. The idea is straightforward: as soon as we flip off the faucet of recent vulnerabilities, they lower exponentially, making all of our code safer, rising the effectiveness of safety design, and assuaging the scalability challenges related to present reminiscence security methods such that they are often utilized extra successfully in a focused method.
This method has confirmed profitable in eliminating complete vulnerability courses and its effectiveness in tackling reminiscence security is more and more evident primarily based on greater than half a decade of constant ends in Android.
We’ll be sharing extra about our secure-by-design efforts within the coming months.
Thanks Alice Ryhl for coding up the simulation. Thanks to Emilia Kasper, Adrian Taylor, Manish Goregaokar, Christoph Kern, and Lars Bergstrom in your useful suggestions on this publish.
Notes