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Why Italy Sells So Much Spyware

Interesting analysis:

Although much attention is given to sophisticated, zero-click spyware developed by companies like Israel’s NSO Group, the Italian spyware marketplace has been able to operate relatively under the radar by specializing in cheaper tools. According to an Italian Ministry of Justice document, as of December 2022 law enforcement in the country could rent spyware for €150 a day, regardless of which vendor they used, and without the large acquisition costs which would normally be prohibitive.

As a result, thousands of spyware operations have been carried out by Italian authorities in recent years, according to a report from Riccardo Coluccini, a respected Italian journalist who specializes in covering spyware and hacking.

Italian spyware is cheaper and easier to use, which makes it more widely used. And Italian companies have been in this market for a long time.

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Posted on November 19, 2024 at 7:05 AM • View Comments

Most of 2023’s Top Exploited Vulnerabilities Were Zero-Days

Zero-day vulnerabilities are more commonly used, according to the Five Eyes:

Key Findings

In 2023, malicious cyber actors exploited more zero-day vulnerabilities to compromise enterprise networks compared to 2022, allowing them to conduct cyber operations against higher-priority targets. In 2023, the majority of the most frequently exploited vulnerabilities were initially exploited as a zero-day, which is an increase from 2022, when less than half of the top exploited vulnerabilities were exploited as a zero-day.

Malicious cyber actors continue to have the most success exploiting vulnerabilities within two years after public disclosure of the vulnerability. The utility of these vulnerabilities declines over time as more systems are patched or replaced. Malicious cyber actors find less utility from zero-day exploits when international cybersecurity efforts reduce the lifespan of zero-day vulnerabilities.

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Posted on November 18, 2024 at 10:49 AM • View Comments

Good Essay on the History of Bad Password Policies

Stuart Schechter makes some good points on the history of bad password policies:

Morris and Thompson’s work brought much-needed data to highlight a problem that lots of people suspected was bad, but that had not been studied scientifically. Their work was a big step forward, if not for two mistakes that would impede future progress in improving passwords for decades.

First, was Morris and Thompson’s confidence that their solution, a password policy, would fix the underlying problem of weak passwords. They incorrectly assumed that if they prevented the specific categories of weakness that they had noted, that the result would be something strong. After implementing a requirement that password have multiple characters sets or more total characters, they wrote:

These improvements make it exceedingly difficult to find any individual password. The user is warned of the risks and if he cooperates, he is very safe indeed.

As should be obvious now, a user who chooses “p@ssword” to comply with policies such as those proposed by Morris and Thompson is not very safe indeed. Morris and Thompson assumed their intervention would be effective without testing its efficacy, considering its unintended consequences, or even defining a metric of success to test against. Not only did their hunch turn out to be wrong, but their second mistake prevented anyone from proving them wrong.

That second mistake was convincing sysadmins to hash passwords, so there was no way to evaluate how secure anyone’s password actually was. And it wasn’t until hackers started stealing and publishing large troves of actual passwords that we got the data: people are terrible at generating secure passwords, even with rules.

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Posted on November 15, 2024 at 7:05 AM • View Comments

New iOS Security Feature Makes It Harder for Police to Unlock Seized Phones

Everybody is reporting about a new security iPhone security feature with iOS 18: if the phone hasn’t been used for a few days, it automatically goes into its “Before First Unlock” state and has to be rebooted.

This is a really good security feature. But various police departments don’t like it, because it makes it harder for them to unlock suspects’ phones.

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Posted on November 14, 2024 at 7:05 AM • View Comments

Criminals Exploiting FBI Emergency Data Requests

I’ve been writing about the problem with lawful-access backdoors in encryption for decades now: that as soon as you create a mechanism for law enforcement to bypass encryption, the bad guys will use it too.

Turns out the same thing is true for non-technical backdoors:

The advisory said that the cybercriminals were successful in masquerading as law enforcement by using compromised police accounts to send emails to companies requesting user data. In some cases, the requests cited false threats, like claims of human trafficking and, in one case, that an individual would “suffer greatly or die” unless the company in question returns the requested information.

The FBI said the compromised access to law enforcement accounts allowed the hackers to generate legitimate-looking subpoenas that resulted in companies turning over usernames, emails, phone numbers, and other private information about their users.

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Posted on November 12, 2024 at 7:05 AM • View Comments

AI Industry is Trying to Subvert the Definition of “Open Source AI”

The Open Source Initiative has published (news article here) its definition of “open source AI,” and it’s terrible. It allows for secret training data and mechanisms. It allows for development to be done in secret. Since for a neural network, the training data is the source code—it’s how the model gets programmed—the definition makes no sense.

And it’s confusing; most “open source” AI models—like LLAMA—are open source in name only. But the OSI seems to have been co-opted by industry players that want both corporate secrecy and the “open source” label. (Here’s one rebuttal to the definition.)

This is worth fighting for. We need a public AI option, and open source—real open source—is a necessary component of that.

But while open source should mean open source, there are some partially open models that need some sort of definition. There is a big research field of privacy-preserving, federated methods of ML model training and I think that is a good thing. And OSI has a point here:

Why do you allow the exclusion of some training data?

Because we want Open Source AI to exist also in fields where data cannot be legally shared, for example medical AI. Laws that permit training on data often limit the resharing of that same data to protect copyright or other interests. Privacy rules also give a person the rightful ability to control their most sensitive information ­ like decisions about their health. Similarly, much of the world’s Indigenous knowledge is protected through mechanisms that are not compatible with later-developed frameworks for rights exclusivity and sharing.

How about we call this “open weights” and not open source?

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Posted on November 8, 2024 at 7:03 AM • View Comments

Prompt Injection Defenses Against LLM Cyberattacks

Interesting research: “Hacking Back the AI-Hacker: Prompt Injection as a Defense Against LLM-driven Cyberattacks“:

Large language models (LLMs) are increasingly being harnessed to automate cyberattacks, making sophisticated exploits more accessible and scalable. In response, we propose a new defense strategy tailored to counter LLM-driven cyberattacks. We introduce Mantis, a defensive framework that exploits LLMs’ susceptibility to adversarial inputs to undermine malicious operations. Upon detecting an automated cyberattack, Mantis plants carefully crafted inputs into system responses, leading the attacker’s LLM to disrupt their own operations (passive defense) or even compromise the attacker’s machine (active defense). By deploying purposefully vulnerable decoy services to attract the attacker and using dynamic prompt injections for the attacker’s LLM, Mantis can autonomously hack back the attacker. In our experiments, Mantis consistently achieved over 95% effectiveness against automated LLM-driven attacks. To foster further research and collaboration, Mantis is available as an open-source tool: this https URL.

This isn’t the solution, of course. But this sort of thing could be part of a solution.

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Posted on November 7, 2024 at 11:13 AM • View Comments

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Sidebar photo of Bruce Schneier by Joe MacInnis.