A previously unknown compression side channel in GPUs can expose images thought to be private.

GPUs from all six of the major suppliers are vulnerable to a newly discovered attack that allows malicious websites to read the usernames, passwords, and other sensitive visual data displayed by other websites, researchers have demonstrated in a paper published Tuesday.

The cross-origin attack allows a malicious website from one domain—say, example.com—to effectively read the pixels displayed by a website from example.org, or another different domain. Attackers can then reconstruct them in a way that allows them to view the words or images displayed by the latter site. This leakage violates a critical security principle that forms one of the most fundamental security boundaries safeguarding the Internet. Known as the same origin policy, it mandates that content hosted on one website domain be isolated from all other website domains.

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The security threats that can result when HTML is embedded in iframes on malicious websites have been well-known for more than a decade. Most websites restrict the cross-origin embedding of pages displaying user names, passwords, or other sensitive content through X-Frame-Options or Content-Security-Policy headers. Not all, however, do. One example is Wikipedia, which shows the usernames of people who log in to their accounts. A person who wants to remain anonymous while visiting a site they don’t trust could be outed if it contained an iframe containing a link to https://en.wikipedia.org/wiki/Main_Page.

Pixel stealing PoC for deanonymizing a user, run with other tabs open playing video. “Ground Truth” is the victim iframe (Wikipedia logged in as “Yingchenw”). “AMD” is the attack result on a Ryzen 7 4800U after 30 minutes, with 97 percent accuracy. “Intel” is the attack result for an i7-8700 after 215 minutes with 98 percent accuracy.

The researchers showed how GPU.zip allows a malicious website they created for their PoC to steal pixels one by one for a user’s Wikipedia username. The attack works on GPUs provided by Apple, Intel, AMD, Qualcomm, Arm, and Nvidia. On AMD’s Ryzen 7 4800U, GPU.zip took about 30 minutes to render the targeted pixels with 97 percent accuracy. The attack required 215 minutes to reconstruct the pixels when displayed on a system running an Intel i7-8700.

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  • geosoco@kbin.socialOP
    9·
    1 year ago

    The problem is that so many browsers leverage hardware acceleration and offer access to the GPUs. So yes, the browsers could fix the issue, but the underlying cause is the way GPUs handle data that the attack is leveraging. Fixing it would likely involve not using hardware acceleration.

    As these patterns are processed by the iGPU, their varying degrees of redundancy cause the lossless compression output to depend on the secret pixel. The data-dependent compression output directly translates to data-dependent DRAM traffic and data-dependent cache occupancy. Consequently, we show that, even under the most passive threat model—where an attacker can only observe coarse-grained redundancy information of a pattern using a coarse-grained timer in the browser and lacks the ability to adaptively select input—individual pixels can be leaked. Our proof-of-concept attack succeeds on a range of devices (including computers, phones) from a variety of hardware vendors with distinct GPU architectures (Intel, AMD, Apple, Nvidia). Surprisingly, our attack also succeeds on discrete GPUs, and we have preliminary results indicating the presence of software-transparent compression on those architectures as well.

    It sounds distantly similar to some of the canvas issues where the acceleration creates different artifacts which makes it possible to identify GPUs and fingerprint the browsers.