Entries Tagged "cryptography"

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Implementing Cryptography in AI Systems

Interesting research: “How to Securely Implement Cryptography in Deep Neural Networks.”

Abstract: The wide adoption of deep neural networks (DNNs) raises the question of how can we equip them with a desired cryptographic functionality (e.g, to decrypt an encrypted input, to verify that this input is authorized, or to hide a secure watermark in the output). The problem is that cryptographic primitives are typically designed to run on digital computers that use Boolean gates to map sequences of bits to sequences of bits, whereas DNNs are a special type of analog computer that uses linear mappings and ReLUs to map vectors of real numbers to vectors of real numbers. This discrepancy between the discrete and continuous computational models raises the question of what is the best way to implement standard cryptographic primitives as DNNs, and whether DNN implementations of secure cryptosystems remain secure in the new setting, in which an attacker can ask the DNN to process a message whose “bits” are arbitrary real numbers.

In this paper we lay the foundations of this new theory, defining the meaning of correctness and security for implementations of cryptographic primitives as ReLU-based DNNs. We then show that the natural implementations of block ciphers as DNNs can be broken in linear time by using such nonstandard inputs. We tested our attack in the case of full round AES-128, and had success rate in finding randomly chosen keys. Finally, we develop a new method for implementing any desired cryptographic functionality as a standard ReLU-based DNN in a provably secure and correct way. Our protective technique has very low overhead (a constant number of additional layers and a linear number of additional neurons), and is completely practical.

Posted on February 21, 2025 at 10:33 AM • View Comments

Simson Garfinkel on Spooky Cryptographic Action at a Distance

Excellent read. One example:

Consider the case of basic public key cryptography, in which a person’s public and private key are created together in a single operation. These two keys are entangled, not with quantum physics, but with math.

When I create a virtual machine server in the Amazon cloud, I am prompted for an RSA public key that will be used to control access to the machine. Typically, I create the public and private keypair on my laptop and upload the public key to Amazon, which bakes my public key into the server’s administrator account. My laptop and that remove server are thus entangled, in that the only way to log into the server is using the key on my laptop. And because that administrator account can do anything to that server­—read the sensitivity data, hack the web server to install malware on people who visit its web pages, or anything else I might care to do­—the private key on my laptop represents a security risk for that server.

Here’s why it’s impossible to evaluate a server and know if it is secure: as long that private key exists on my laptop, that server has a vulnerability. But if I delete that private key, the vulnerability goes away. By deleting the data, I have removed a security risk from the server and its security has increased. This is true entanglement! And it is spooky: not a single bit has changed on the server, yet it is more secure.

Read it all.

Posted on October 30, 2024 at 10:48 AM • View Comments

Watermark for LLM-Generated Text

Researchers at Google have developed a watermark for LLM-generated text. The basics are pretty obvious: the LLM chooses between tokens partly based on a cryptographic key, and someone with knowledge of the key can detect those choices. What makes this hard is (1) how much text is required for the watermark to work, and (2) how robust the watermark is to post-generation editing. Google’s version looks pretty good: it’s detectable in text as small as 200 tokens.

Posted on October 25, 2024 at 9:56 AM • View Comments

Microsoft Is Adding New Cryptography Algorithms

Microsoft is updating SymCrypt, its core cryptographic library, with new quantum-secure algorithms. Microsoft’s details are here. From a news article:

The first new algorithm Microsoft added to SymCrypt is called ML-KEM. Previously known as CRYSTALS-Kyber, ML-KEM is one of three post-quantum standards formalized last month by the National Institute of Standards and Technology (NIST). The KEM in the new name is short for key encapsulation. KEMs can be used by two parties to negotiate a shared secret over a public channel. Shared secrets generated by a KEM can then be used with symmetric-key cryptographic operations, which aren’t vulnerable to Shor’s algorithm when the keys are of a sufficient size.

The ML in the ML-KEM name refers to Module Learning with Errors, a problem that can’t be cracked with Shor’s algorithm. As explained here, this problem is based on a “core computational assumption of lattice-based cryptography which offers an interesting trade-off between guaranteed security and concrete efficiency.”

ML-KEM, which is formally known as FIPS 203, specifies three parameter sets of varying security strength denoted as ML-KEM-512, ML-KEM-768, and ML-KEM-1024. The stronger the parameter, the more computational resources are required.

The other algorithm added to SymCrypt is the NIST-recommended XMSS. Short for eXtended Merkle Signature Scheme, it’s based on “stateful hash-based signature schemes.” These algorithms are useful in very specific contexts such as firmware signing, but are not suitable for more general uses.

Posted on September 12, 2024 at 11:42 AM • View Comments

NIST Releases First Post-Quantum Encryption Algorithms

From the Federal Register:

After three rounds of evaluation and analysis, NIST selected four algorithms it will standardize as a result of the PQC Standardization Process. The public-key encapsulation mechanism selected was CRYSTALS-KYBER, along with three digital signature schemes: CRYSTALS-Dilithium, FALCON, and SPHINCS+.

These algorithms are part of three NIST standards that have been finalized:

NIST press release. My recent writings on post-quantum cryptographic standards.

EDITED TO ADD: Good article:

One – ML-KEM [PDF] (based on CRYSTALS-Kyber) – is intended for general encryption, which protects data as it moves across public networks. The other two –- ML-DSA [PDF] (originally known as CRYSTALS-Dilithium) and SLH-DSA [PDF] (initially submitted as Sphincs+)—secure digital signatures, which are used to authenticate online identity.

A fourth algorithm – FN-DSA [PDF] (originally called FALCON) – is slated for finalization later this year and is also designed for digital signatures.

NIST continued to evaluate two other sets of algorithms that could potentially serve as backup standards in the future.

One of the sets includes three algorithms designed for general encryption – but the technology is based on a different type of math problem than the ML-KEM general-purpose algorithm in today’s finalized standards.

NIST plans to select one or two of these algorithms by the end of 2024.

IEEE Spectrum article.

Slashdot thread.

Posted on August 15, 2024 at 11:37 AM • View Comments

Compromising the Secure Boot Process

This isn’t good:

On Thursday, researchers from security firm Binarly revealed that Secure Boot is completely compromised on more than 200 device models sold by Acer, Dell, Gigabyte, Intel, and Supermicro. The cause: a cryptographic key underpinning Secure Boot on those models that was compromised in 2022. In a public GitHub repository committed in December of that year, someone working for multiple US-based device manufacturers published what’s known as a platform key, the cryptographic key that forms the root-of-trust anchor between the hardware device and the firmware that runs on it. The repository was located at https://github.com/raywu-aaeon/Ryzen2000_4000.git, and it’s not clear when it was taken down.

The repository included the private portion of the platform key in encrypted form. The encrypted file, however, was protected by a four-character password, a decision that made it trivial for Binarly, and anyone else with even a passing curiosity, to crack the passcode and retrieve the corresponding plain text. The disclosure of the key went largely unnoticed until January 2023, when Binarly researchers found it while investigating a supply-chain incident. Now that the leak has come to light, security experts say it effectively torpedoes the security assurances offered by Secure Boot.

[…]

These keys were created by AMI, one of the three main providers of software developer kits that device makers use to customize their UEFI firmware so it will run on their specific hardware configurations. As the strings suggest, the keys were never intended to be used in production systems. Instead, AMI provided them to customers or prospective customers for testing. For reasons that aren’t clear, the test keys made their way into devices from a nearly inexhaustive roster of makers. In addition to the five makers mentioned earlier, they include Aopen, Foremelife, Fujitsu, HP, Lenovo, and Supermicro.

Posted on July 26, 2024 at 12:21 PM • View Comments

Model Extraction from Neural Networks

A new paper, “Polynomial Time Cryptanalytic Extraction of Neural Network Models,” by Adi Shamir and others, uses ideas from differential cryptanalysis to extract the weights inside a neural network using specific queries and their results. This is much more theoretical than practical, but it’s a really interesting result.

Abstract:

Billions of dollars and countless GPU hours are currently spent on training Deep Neural Networks (DNNs) for a variety of tasks. Thus, it is essential to determine the difficulty of extracting all the parameters of such neural networks when given access to their black-box implementations. Many versions of this problem have been studied over the last 30 years, and the best current attack on ReLU-based deep neural networks was presented at Crypto’20 by Carlini, Jagielski, and Mironov. It resembles a differential chosen plaintext attack on a cryptosystem, which has a secret key embedded in its black-box implementation and requires a polynomial number of queries but an exponential amount of time (as a function of the number of neurons). In this paper, we improve this attack by developing several new techniques that enable us to extract with arbitrarily high precision all the real-valued parameters of a ReLU-based DNN using a polynomial number of queries and a polynomial amount of time. We demonstrate its practical efficiency by applying it to a full-sized neural network for classifying the CIFAR10 dataset, which has 3072 inputs, 8 hidden layers with 256 neurons each, and about 1.2 million neuronal parameters. An attack following the approach by Carlini et al. requires an exhaustive search over 2^256 possibilities. Our attack replaces this with our new techniques, which require only 30 minutes on a 256-core computer.

Posted on July 1, 2024 at 7:05 AM • View Comments

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Sidebar photo of Bruce Schneier by Joe MacInnis.