Chrome’s consumer interface (UI) code is complicated, and generally has bugs.
Are these bugs safety bugs? Particularly, if a consumer’s clicks and actions lead to reminiscence corruption, is that one thing that an attacker can exploit to hurt that consumer?
Our safety severity pointers say “sure, generally.” For instance, an attacker may very seemingly persuade a consumer to click on an autofill immediate, however it will likely be a lot more durable to persuade the consumer to step via an entire movement of various dialogs.
Even when these bugs aren’t the most simply exploitable, it takes a substantial amount of time for our safety shepherds to make these determinations. Consumer interface bugs are sometimes flakey (that’s, not reliably reproducible). Additionally, even when these bugs aren’t essentially deemed to be exploitable, they might nonetheless be annoying crashes which hassle the consumer.
It could be nice if we may discover these bugs mechanically.
If solely the entire tree of Chrome UI controls have been uncovered, someway, such that we may enumerate and work together with every UI management mechanically.
Aha! Chrome exposes all of the UI controls to assistive expertise. Chrome goes to nice lengths to make sure its whole UI is uncovered to display screen readers, braille units and different such assistive tech. This tree of controls consists of all of the toolbars, menus, and the construction of the web page itself. This structural definition of the browser consumer interface is already generally utilized in different contexts, for instance by some password managers, demonstrating that investing in accessibility has advantages for all customers. We’re now taking that funding and leveraging it to search out safety bugs, too.
Particularly, we’re now “fuzzing” that accessibility tree – that’s, interacting with the completely different UI controls semi-randomly to see if we will make issues crash. This system has a lengthy pedigree.
Display screen reader expertise is a bit completely different on every platform, however on Linux the tree may be explored utilizing Accerciser.
All we have now to do is discover the identical tree of controls with a fuzzer. How laborious can it’s?
“We do that not as a result of it’s straightforward, however as a result of we thought it might be straightforward” – Anon.
Truly we by no means thought this is able to be straightforward, and some completely different bits of tech have needed to fall into place to make this attainable. Particularly,
- There are many combos of how to work together with Chrome. Actually randomly clicking on UI controls in all probability gained’t discover bugs – we wish to leverage coverage-guided fuzzing to assist the fuzzer choose combos of controls that appear to achieve into new code inside Chrome.
- We want any such bugs to be real. We subsequently must fuzz the precise Chrome UI, or one thing very related, moderately than exercising elements of the code in an unrealistic unit-test-like context. That’s the place our InProcessFuzzer framework comes into play – it runs fuzz instances inside a Chrome browser_test; primarily an actual model of Chrome.
- However such browser_tests have a excessive startup value. We have to amortize that value over 1000’s of take a look at instances by operating a batch of them inside every browser invocation. Centipede is designed to do this.
- However every take a look at case gained’t be idempotent. Inside a given invocation of the browser, the UI state could also be successively modified by every take a look at case. We intend so as to add concatenation to centipede to resolve this.
- Chrome is a loud setting with numerous timers, which can nicely confuse coverage-guided fuzzers. Gathering protection for such a big binary is sluggish in itself. So, we don’t know if coverage-guided fuzzing will efficiently discover the UI paths right here.
All of those considerations are frequent to the opposite fuzzers which run within the browser_test context, most notably our new IPC fuzzer (weblog posts to observe). However the UI fuzzer introduced some particular challenges.
Discovering UI bugs is barely helpful in the event that they’re actionable. Ideally, meaning:
- Our fuzzing infrastructure offers a radical set of diagnostics.
- It could actually bisect to search out when the bug was launched and when it was fastened.
- It could actually decrease complicated take a look at instances into the smallest attainable reproducer.
- The take a look at case is descriptive and says which UI controls have been used, so a human could possibly reproduce it.
These necessities collectively imply that the take a look at instances must be secure throughout every Chrome model – if a given take a look at case reproduces a bug with Chrome 125, hopefully it’s going to achieve this in Chrome 124 and Chrome 126 (assuming the bug is current in each). But that is difficult, since Chrome UI controls are deeply nested and sometimes nameless.
Initially, the fuzzer picked controls merely based mostly on their ordinal at every degree of the tree (for example “management 3 nested in management 5 nested in management 0”) however such take a look at instances are unlikely to be secure because the Chrome UI evolves. As a substitute, we settled on an strategy the place the controls are named, when attainable, and in any other case recognized by a mixture of position and ordinal. This yields take a look at instances like this:
motion {
path_to_control {
named {
identify: “Take a look at – Chromium”
}
}
path_to_control {
nameless {
position: “panel”
}
}
path_to_control {
nameless {
position: “panel”
}
}
path_to_control {
nameless {
position: “panel”
}
}
path_to_control {
named {
identify: “Bookmarks”
}
}
take_action {
action_id: 12
}
}
Fuzzers are unlikely to stumble throughout these management names by probability, even with the instrumentation utilized to string comparisons. Actually, this by-name strategy turned out to be solely 20% as efficient as choosing controls by ordinal. To resolve this we added a customized mutator which is wise sufficient to place in place management names and roles that are identified to exist. We randomly use this mutator or the usual libprotobuf-mutator with a view to get the very best of each worlds. This strategy has confirmed to be about 80% as fast as the unique ordinal-based mutator, whereas offering secure take a look at instances.
So, does any of this work?
We don’t know but! – and you’ll observe alongside as we discover out. The fuzzer discovered a few potential bugs (at the moment entry restricted) within the accessibility code itself however hasn’t but explored far sufficient to find bugs in Chrome’s elementary UI. However, on the time of writing, this has solely been operating on our ClusterFuzz infrastructure for a couple of hours, and isn’t but engaged on our protection dashboard. In case you’d wish to observe alongside, control our protection dashboard because it expands to cowl UI code.
