Everyone is writing about an interagency and international report on Chinese hacking of US critical infrastructure.
Lots of interesting details about how the group, called Volt Typhoon, accesses target networks and evades detection.
It’s neither hard nor expensive:
Unlike password authentication, which requires a direct match between what is inputted and what’s stored in a database, fingerprint authentication determines a match using a reference threshold. As a result, a successful fingerprint brute-force attack requires only that an inputted image provides an acceptable approximation of an image in the fingerprint database. BrutePrint manipulates the false acceptance rate (FAR) to increase the threshold so fewer approximate images are accepted.
BrutePrint acts as an adversary in the middle between the fingerprint sensor and the trusted execution environment and exploits vulnerabilities that allow for unlimited guesses.
In a BrutePrint attack, the adversary removes the back cover of the device and attaches the $15 circuit board that has the fingerprint database loaded in the flash storage. The adversary then must convert the database into a fingerprint dictionary that’s formatted to work with the specific sensor used by the targeted phone. The process uses a neural-style transfer when converting the database into the usable dictionary. This process increases the chances of a match.
With the fingerprint dictionary in place, the adversary device is now in a position to input each entry into the targeted phone. Normally, a protection known as attempt limiting effectively locks a phone after a set number of failed login attempts are reached. BrutePrint can fully bypass this limit in the eight tested Android models, meaning the adversary device can try an infinite number of guesses. (On the two iPhones, the attack can expand the number of guesses to 15, three times higher than the five permitted.)
The bypasses result from exploiting what the researchers said are two zero-day vulnerabilities in the smartphone fingerprint authentication framework of virtually all smartphones. The vulnerabilities—one known as CAMF (cancel-after-match fail) and the other MAL (match-after-lock)—result from logic bugs in the authentication framework. CAMF exploits invalidate the checksum of transmitted fingerprint data, and MAL exploits infer matching results through side-channel attacks.
Depending on the model, the attack takes between 40 minutes and 14 hours.
Also:
The ability of BrutePrint to successfully hijack fingerprints stored on Android devices but not iPhones is the result of one simple design difference: iOS encrypts the data, and Android does not.
Atlas Obscura has a five-part online course on cephalopods, taught by squid biologist Dr. Sarah McAnulty.
As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.
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Cyberspace operations now officially has a physical dimension, meaning that the United States has official military doctrine about cyberattacks that also involve an actual human gaining physical access to a piece of computing infrastructure.
A revised version of Joint Publication 3-12 Cyberspace Operations—published in December 2022 and while unclassified, is only available to those with DoD common access cards, according to a Joint Staff spokesperson—officially provides a definition for “expeditionary cyberspace operations,” which are “[c]yberspace operations that require the deployment of cyberspace forces within the physical domains.”
[…]
“Developing access to targets in or through cyberspace follows a process that can often take significant time. In some cases, remote access is not possible or preferable, and close proximity may be required, using expeditionary [cyber operations],” the joint publication states. “Such operations are key to addressing the challenge of closed networks and other systems that are virtually isolated. Expeditionary CO are often more regionally and tactically focused and can include units of the CMF or special operations forces … If direct access to the target is unavailable or undesired, sometimes a similar or partial effect can be created by indirect access using a related target that has higher-order effects on the desired target.”
[…]
“Allowing them to support [combatant commands] in this way permits faster adaptation to rapidly changing needs and allows threats that initially manifest only in one [area of responsibility] to be mitigated globally in near real time. Likewise, while synchronizing CO missions related to achieving [combatant commander] objectives, some cyberspace capabilities that support this activity may need to be forward-deployed; used in multiple AORs simultaneously; or, for speed in time-critical situations, made available via reachback,” it states. “This might involve augmentation or deployment of cyberspace capabilities to forces already forward or require expeditionary CO by deployment of a fully equipped team of personnel and capabilities.”
Interesting essay on the poisoning of LLMs—ChatGPT in particular:
Given that we’ve known about model poisoning for years, and given the strong incentives the black-hat SEO crowd has to manipulate results, it’s entirely possible that bad actors have been poisoning ChatGPT for months. We don’t know because OpenAI doesn’t talk about their processes, how they validate the prompts they use for training, how they vet their training data set, or how they fine-tune ChatGPT. Their secrecy means we don’t know if ChatGPT has been safely managed.
They’ll also have to update their training data set at some point. They can’t leave their models stuck in 2021 forever.
Once they do update it, we only have their word—pinky-swear promises—that they’ve done a good enough job of filtering out keyword manipulations and other training data attacks, something that the AI researcher El Mahdi El Mhamdi posited is mathematically impossible in a paper he worked on while he was at Google.
It’s been a big month for US data privacy. Indiana, Iowa, and Tennessee all passed state privacy laws, bringing the total number of states with a privacy law up to eight. No private right of action in any of those, which means it’s up to the states to enforce the laws.
In case you don’t have enough to worry about, someone has built a credible handwriting machine:
This is still a work in progress, but the project seeks to solve one of the biggest problems with other homework machines, such as this one that I covered a few months ago after it blew up on social media. The problem with most homework machines is that they’re too perfect. Not only is their content output too well-written for most students, but they also have perfect grammar and punctuation something even we professional writers fail to consistently achieve. Most importantly, the machine’s “handwriting” is too consistent. Humans always include small variations in their writing, no matter how honed their penmanship.
Devadath is on a quest to fix the issue with perfect penmanship by making his machine mimic human handwriting. Even better, it will reflect the handwriting of its specific user so that AI-written submissions match those written by the student themselves.
Like other machines, this starts with asking ChatGPT to write an essay based on the assignment prompt. That generates a chunk of text, which would normally be stylized with a script-style font and then output as g-code for a pen plotter. But instead, Devadeth created custom software that records examples of the user’s own handwriting. The software then uses that as a font, with small random variations, to create a document image that looks like it was actually handwritten.
Watch the video.
My guess is that this is another detection/detection avoidance arms race.
Google has backtracked on its plan to delete inactive YouTube videos—at least for now. Of course, it could change its mind anytime it wants.
It would be nice if this would get people to think about the vulnerabilities inherent in letting a for-profit monopoly decide what of human creativity is worth saving.
A Peruvian oversight law has the opposite effect:
Peru in 2020 began requiring any foreign fishing boat entering its ports to use a vessel monitoring system allowing its activities to be tracked in real time 24 hours a day. The equipment, which tracks a vessel’s geographic position and fishing activity through a proprietary satellite communication system, sought to provide authorities with visibility into several hundred Chinese squid vessels that every year amass off the west coast of South America.
[…]
Instead of increasing oversight, the new Peruvian regulations appear to have driven Chinese ships away from the country’s ports—and kept crews made up of impoverished Filipinos and Indonesians at sea for longer periods, exposing them to abuse, according to new research published by Peruvian fishing consultancy Artisonal.
Two things to note here. One is that the Peruvian law was easy to hack, which China promptly did. The second is that no nation-state has the proper regulatory footprint to manage the world’s oceans. These are global issues, and need global solutions. Of course, our current society is terrible at global solutions—to anything.
As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.
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Researchers are worried about Google’s .zip and .mov domains, because they are confusing. Mistaking a URL for a filename could be a security vulnerability.
Sidebar photo of Bruce Schneier by Joe MacInnis.