Interesting research :

Shedding Light on CVSS Scoring Inconsistencies: A User-Centric Study on Evaluating Widespread Security Vulnerabilities

Abstract: The Common Vulnerability Scoring System (CVSS) is a popular method for evaluating the severity of vulnerabilities in vulnerability management. In the evaluation process, a numeric score between 0 and 10 is calculated, 10 being the most severe (critical) value. The goal of CVSS is to provide comparable scores across different evaluators. However, previous works indicate that CVSS might not reach this goal: If a vulnerability is evaluated by several analysts, their scores often differ. This raises the following questions: Are CVSS evaluations consistent? Which factors influence CVSS assessments? We systematically investigate these questions in an online survey with 196 CVSS users. We show that specific CVSS metrics are inconsistently evaluated for widespread vulnerability types, including Top 3 vulnerabilities from the ”2022 CWE Top 25 Most Dangerous Software Weaknesses” list. In a follow-up survey with 59 participants, we found that for the same vulnerabilities from the main study, 68% of these users gave different severity ratings. Our study reveals that most evaluators are aware of the problematic aspects of CVSS, but they still see CVSS as a useful tool for vulnerability assessment. Finally, we discuss possible reasons for inconsistent evaluations and provide recommendations on improving the consistency of scoring.

Here’s a summary of the research.

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.

Other news articles . Research paper .

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 .

New research: “ Achilles Heels for AGI/ASI via Decision Theoretic Adversaries “:

As progress in AI continues to advance, it is important to know how advanced systems will make choices and in what ways they may fail. Machines can already outsmart humans in some domains, and understanding how to safely build ones which may have capabilities at or above the human level is of particular concern. One might suspect that artificially generally intelligent (AGI) and artificially superintelligent (ASI) will be systems that humans cannot reliably outsmart. As a challenge to this assumption, this paper presents the Achilles Heel hypothesis which states that even a potentially superintelligent system may nonetheless have stable decision-theoretic delusions which cause them to make irrational decisions in adversarial settings. In a survey of key dilemmas and paradoxes from the decision theory literature, a number of these potential Achilles Heels are discussed in context of this hypothesis. Several novel contributions are made toward understanding the ways in which these weaknesses might be implanted into a system.

Jenny Blessing and Ross Anderson have evaluated the security of systems designed to allow the various Internet messaging platforms to interoperate with each other:

The Digital Markets Act ruled that users on different platforms should be able to exchange messages with each other. This opens up a real Pandora’s box. How will the networks manage keys, authenticate users, and moderate content? How much metadata will have to be shared, and how?

In our latest paper, One Protocol to Rule Them All? On Securing Interoperable Messaging , we explore the security tensions, the conflicts of interest, the usability traps, and the likely consequences for individual and institutional behaviour.

Interoperability will vastly increase the attack surface at every level in the stack ­ from the cryptography up through usability to commercial incentives and the opportunities for government interference.

It’s a good idea in theory, but will likely result in the overall security being the worst of each platform’s security.

A group of Chinese researchers have just published a paper claiming that they can—although they have not yet done so—break 2048-bit RSA. This is something to take seriously. It might not be correct, but it’s not obviously wrong.

We have long known from Shor’s algorithm that factoring with a quantum computer is easy. But it takes a big quantum computer, on the orders of millions of qbits, to factor anything resembling the key sizes we use today. What the researchers have done is combine classical lattice reduction factoring techniques with a quantum approximate optimization algorithm. This means that they only need a quantum computer with 372 qbits, which is well within what’s possible today. (IBM will announce a 1000-qbit quantum computer in a few months. Others are on their way as well.)

The Chinese group didn’t have that large a quantum computer to work with. They were able to factor 48-bit numbers using a 10-qbit quantum computer. And while there are always potential problems when scaling something like this up by a factor of 50, there are no obvious barriers.

Honestly, most of the paper is over my head—both the lattice-reduction math and the quantum physics. And there’s the nagging question of why the Chinese government didn’t classify this research.

But…wow…maybe…and yikes! Or not.

“Factoring integers with sublinear resources on a superconducting quantum processor”

Abstract: Shor’s algorithm has seriously challenged information security based on public key cryptosystems. However, to break the widely used RSA-2048 scheme, one needs millions of physical qubits, which is far beyond current technical capabilities. Here, we report a universal quantum algorithm for integer factorization by combining the classical lattice reduction with a quantum approximate optimization algorithm (QAOA). The number of qubits required is O(logN/loglogN ), which is sublinear in the bit length of the integer N , making it the most qubit-saving factorization algorithm to date. We demonstrate the algorithm experimentally by factoring integers up to 48 bits with 10 superconducting qubits, the largest integer factored on a quantum device. We estimate that a quantum circuit with 372 physical qubits and a depth of thousands is necessary to challenge RSA-2048 using our algorithm. Our study shows great promise in expediting the application of current noisy quantum computers, and paves the way to factor large integers of realistic cryptographic significance.

Machine learning security is extraordinarily difficult because the attacks are so varied—and it seems that each new one is weirder than the next. Here’s the latest: a training-time attack that forces the model to exhibit a point of view: Spinning Language Models: Risks of Propaganda-As-A-Service and Countermeasures .”

Abstract: We investigate a new threat to neural sequence-to-sequence (seq2seq) models: training-time attacks that cause models to “spin” their outputs so as to support an adversary-chosen sentiment or point of view—but only when the input contains adversary-chosen trigger words. For example, a spinned summarization model outputs positive summaries of any text that mentions the name of some individual or organization.

Model spinning introduces a “meta-backdoor” into a model. Whereas conventional backdoors cause models to produce incorrect outputs on inputs with the trigger, outputs of spinned models preserve context and maintain standard accuracy metrics, yet also satisfy a meta-task chosen by the adversary.

Model spinning enables propaganda-as-a-service, where propaganda is defined as biased speech. An adversary can create customized language models that produce desired spins for chosen triggers, then deploy these models to generate disinformation (a platform attack), or else inject them into ML training pipelines (a supply-chain attack), transferring malicious functionality to downstream models trained by victims.

To demonstrate the feasibility of model spinning, we develop a new backdooring technique. It stacks an adversarial meta-task onto a seq2seq model, backpropagates the desired meta-task output to points in the word-embedding space we call “pseudo-words,” and uses pseudo-words to shift the entire output distribution of the seq2seq model. We evaluate this attack on language generation, summarization, and translation models with different triggers and meta-tasks such as sentiment, toxicity, and entailment. Spinned models largely maintain their accuracy metrics (ROUGE and BLEU) while shifting their outputs to satisfy the adversary’s meta-task. We also show that, in the case of a supply-chain attack, the spin functionality transfers to downstream models.

This new attack dovetails with something I’ve been worried about for a while, something Latanya Sweeney has dubbed “persona bots.” This is what I wrote in my upcoming book (to be published in February):

One example of an extension of this technology is the “persona bot,” an AI posing as an individual on social media and other online groups. Persona bots have histories, personalities, and communication styles. They don’t constantly spew propaganda. They hang out in various interest groups: gardening, knitting, model railroading, whatever. They act as normal members of those communities, posting and commenting and discussing. Systems like GPT-3 will make it easy for those AIs to mine previous conversations and related Internet content and to appear knowledgeable. Then, once in a while, the AI might post something relevant to a political issue, maybe an article about a healthcare worker having an allergic reaction to the COVID-19 vaccine, with worried commentary. Or maybe it might offer its developer’s opinions about a recent election, or racial justice, or any other polarizing subject. One persona bot can’t move public opinion, but what if there were thousands of them? Millions?

These are chatbots on a very small scale. They would participate in small forums around the Internet: hobbyist groups, book groups, whatever. In general they would behave normally, participating in discussions like a person does. But occasionally they would say something partisan or political, depending on the desires of their owners. Because they’re all unique and only occasional, it would be hard for existing bot detection techniques to find them. And because they can be replicated by the millions across social media, they could have a greater effect. They would affect what we think, and—just as importantly—what we think others think. What we will see as robust political discussions would be persona bots arguing with other persona bots.

Attacks like these add another wrinkle to that sort of scenario.

A surprising number of websites include JavaScript keyloggers that collect everything you type as you type it, not just when you submit a form.

Researchers from KU Leuven, Radboud University, and University of Lausanne crawled and analyzed the top 100,000 websites, looking at scenarios in which a user is visiting a site while in the European Union and visiting a site from the United States. They found that 1,844 websites gathered an EU user’s email address without their consent, and a staggering 2,950 logged a US user’s email in some form. Many of the sites seemingly do not intend to conduct the data-logging but incorporate third-party marketing and analytics services that cause the behavior.

After specifically crawling sites for password leaks in May 2021, the researchers also found 52 websites in which third parties, including the Russian tech giant Yandex, were incidentally collecting password data before submission. The group disclosed their findings to these sites, and all 52 instances have since been resolved.

“If there’s a Submit button on a form, the reasonable expectation is that it does something — that it will submit your data when you click it,” says Güneş Acar, a professor and researcher in Radboud University’s digital security group and one of the leaders of the study. “We were super surprised by these results. We thought maybe we were going to find a few hundred websites where your email is collected before you submit, but this exceeded our expectations by far.”

Research paper .