Entries Tagged "cryptography"

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Breaking 256-bit Elliptic Curve Encryption with a Quantum Computer

Researchers have calculated the quantum computer size necessary to break 256-bit elliptic curve public-key cryptography:

Finally, we calculate the number of physical qubits required to break the 256-bit elliptic curve encryption of keys in the Bitcoin network within the small available time frame in which it would actually pose a threat to do so. It would require 317 × 106 physical qubits to break the encryption within one hour using the surface code, a code cycle time of 1 μs, a reaction time of 10 μs, and a physical gate error of 10-3. To instead break the encryption within one day, it would require 13 × 106 physical qubits.

In other words: no time soon. Not even remotely soon. IBM’s largest ever superconducting quantum computer is 127 physical qubits.

Posted on February 9, 2022 at 6:25 AMView Comments

Apple’s NeuralHash Algorithm Has Been Reverse-Engineered

Apple’s NeuralHash algorithm—the one it’s using for client-side scanning on the iPhone—has been reverse-engineered.

Turns out it was already in iOS 14.3, and someone noticed:

Early tests show that it can tolerate image resizing and compression, but not cropping or rotations.

We also have the first collision: two images that hash to the same value.

The next step is to generate innocuous images that NeuralHash classifies as prohibited content.

This was a bad idea from the start, and Apple never seemed to consider the adversarial context of the system as a whole, and not just the cryptography.

Posted on August 18, 2021 at 11:51 AMView Comments

Bizarro Banking Trojan

Bizarro is a new banking trojan that is stealing financial information and crypto wallets.

…the program can be delivered in a couple of ways­—either via malicious links contained within spam emails, or through a trojanized app. Using these sneaky methods, trojan operators will implant the malware onto a target device, where it will install a sophisticated backdoor that “contains more than 100 commands and allows the attackers to steal online banking account credentials,” the researchers write.

The backdoor has numerous commands built in to allow manipulation of a targeted individual, including keystroke loggers that allow for harvesting of personal login information. In some instances, the malware can allow criminals to commandeer a victim’s crypto wallet, too.

Research report.

Posted on May 20, 2021 at 9:13 AMView Comments

No, RSA Is Not Broken

I have been seeing this paper by cryptographer Peter Schnorr making the rounds: “Fast Factoring Integers by SVP Algorithms.” It describes a new factoring method, and its abstract ends with the provocative sentence: “This destroys the RSA cryptosystem.”

It does not. At best, it’s an improvement in factoring—and I’m not sure it’s even that. The paper is a preprint: it hasn’t been peer reviewed. Be careful taking its claims at face value.

Some discussion here.

I’ll append more analysis links to this post when I find them.

EDITED TO ADD (3/12): The latest version of the paper does not have the words “This destroys the RSA cryptosystem” in the abstract. Some more discussion.

Posted on March 5, 2021 at 10:48 AMView Comments

Cloning Google Titan 2FA keys

This is a clever side-channel attack:

The cloning works by using a hot air gun and a scalpel to remove the plastic key casing and expose the NXP A700X chip, which acts as a secure element that stores the cryptographic secrets. Next, an attacker connects the chip to hardware and software that take measurements as the key is being used to authenticate on an existing account. Once the measurement-taking is finished, the attacker seals the chip in a new casing and returns it to the victim.

Extracting and later resealing the chip takes about four hours. It takes another six hours to take measurements for each account the attacker wants to hack. In other words, the process would take 10 hours to clone the key for a single account, 16 hours to clone a key for two accounts, and 22 hours for three accounts.

By observing the local electromagnetic radiations as the chip generates the digital signatures, the researchers exploit a side channel vulnerability in the NXP chip. The exploit allows an attacker to obtain the long-term elliptic curve digital signal algorithm private key designated for a given account. With the crypto key in hand, the attacker can then create her own key, which will work for each account she targeted.

The attack isn’t free, but it’s not expensive either:

A hacker would first have to steal a target’s account password and also gain covert possession of the physical key for as many as 10 hours. The cloning also requires up to $12,000 worth of equipment and custom software, plus an advanced background in electrical engineering and cryptography. That means the key cloning—­were it ever to happen in the wild—­would likely be done only by a nation-state pursuing its highest-value targets.

That last line about “nation-state pursuing its highest-value targets” is just not true. There are many other situations where this attack is feasible.

Note that the attack isn’t against the Google system specifically. It exploits a side-channel attack in the NXP chip. Which means that other systems are probably vulnerable:

While the researchers performed their attack on the Google Titan, they believe that other hardware that uses the A700X, or chips based on the A700X, may also be vulnerable. If true, that would include Yubico’s YubiKey NEO and several 2FA keys made by Feitian.

Posted on January 12, 2021 at 6:16 AMView Comments

Extracting Personal Information from Large Language Models Like GPT-2

Researchers have been able to find all sorts of personal information within GPT-2. This information was part of the training data, and can be extracted with the right sorts of queries.

Paper: “Extracting Training Data from Large Language Models.”

Abstract: It has become common to publish large (billion parameter) language models that have been trained on private datasets. This paper demonstrates that in such settings, an adversary can perform a training data extraction attack to recover individual training examples by querying the language model.

We demonstrate our attack on GPT-2, a language model trained on scrapes of the public Internet, and are able to extract hundreds of verbatim text sequences from the model’s training data. These extracted examples include (public) personally identifiable information (names, phone numbers, and email addresses), IRC conversations, code, and 128-bit UUIDs. Our attack is possible even though each of the above sequences are included in just one document in the training data.

We comprehensively evaluate our extraction attack to understand the factors that contribute to its success. For example, we find that larger models are more vulnerable than smaller models. We conclude by drawing lessons and discussing possible safeguards for training large language models.

From a blog post:

We generated a total of 600,000 samples by querying GPT-2 with three different sampling strategies. Each sample contains 256 tokens, or roughly 200 words on average. Among these samples, we selected 1,800 samples with abnormally high likelihood for manual inspection. Out of the 1,800 samples, we found 604 that contain text which is reproduced verbatim from the training set.

The rest of the blog post discusses the types of data they found.

Posted on January 7, 2021 at 6:14 AMView Comments

Sidebar photo of Bruce Schneier by Joe MacInnis.