Crypto-gram: December 15, 2023 - Schneier on Security

December 15, 2023

by Bruce Schneier
Fellow and Lecturer, Harvard Kennedy School
schneier@schneier.com
https://www.schneier.com

A free monthly newsletter providing summaries, analyses, insights, and commentaries on security: computer and otherwise.

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These same essays and news items appear in the Schneier on Security blog, along with a lively and intelligent comment section. An RSS feed is available.

In this issue:

New SSH Vulnerability
Leaving Authentication Credentials in Public Code
FTC’s Voice Cloning Challenge
Ransomware Gang Files SEC Complaint
Using Generative AI for Surveillance
Email Security Flaw Found in the Wild
Apple to Add Manual Authentication to iMessage
LitterDrifter USB Worm
Chocolate Swiss Army Knife
Secret White House Warrantless Surveillance Program
Digital Car Keys Are Coming
Breaking Laptop Fingerprint Sensors
Extracting GPT’s Training Data
AI Decides to Engage in Insider Trading
AI and Trust
AI and Mass Spying
Security Analysis of a Thirteenth-Century Venetian Election Protocol
Spying through Push Notifications
New Bluetooth Attack
Facebook Enables Messenger End-to-End Encryption by Default
New Windows/Linux Firmware Attack
Surveillance by the US Postal Service
Surveillance Cameras Disguised as Clothes Hooks

New SSH Vulnerability

[2023.11.15] This is interesting:

For the first time, researchers have demonstrated that a large portion of cryptographic keys used to protect data in computer-to-server SSH traffic are vulnerable to complete compromise when naturally occurring computational errors occur while the connection is being established.

[…]

The vulnerability occurs when there are errors during the signature generation that takes place when a client and server are establishing a connection. It affects only keys using the RSA cryptographic algorithm, which the researchers found in roughly a third of the SSH signatures they examined. That translates to roughly 1 billion signatures out of the 3.2 billion signatures examined. Of the roughly 1 billion RSA signatures, about one in a million exposed the private key of the host.

Research paper:

Passive SSH Key Compromise via Lattices

Abstract: We demonstrate that a passive network attacker can opportunistically obtain private RSA host keys from an SSH server that experiences a naturally arising fault during signature computation. In prior work, this was not believed to be possible for the SSH protocol because the signature included information like the shared Diffie-Hellman secret that would not be available to a passive network observer. We show that for the signature parameters commonly in use for SSH, there is an efficient lattice attack to recover the private key in case of a signature fault. We provide a security analysis of the SSH, IKEv1, and IKEv2 protocols in this scenario, and use our attack to discover hundreds of compromised keys in the wild from several independently vulnerable implementations.

Leaving Authentication Credentials in Public Code

[2023.11.16] Interesting article about a surprisingly common vulnerability: programmers leaving authentication credentials and other secrets in publicly accessible software code:

Researchers from security firm GitGuardian this week reported finding almost 4,000 unique secrets stashed inside a total of 450,000 projects submitted to PyPI, the official code repository for the Python programming language. Nearly 3,000 projects contained at least one unique secret. Many secrets were leaked more than once, bringing the total number of exposed secrets to almost 57,000.

[…]

The credentials exposed provided access to a range of resources, including Microsoft Active Directory servers that provision and manage accounts in enterprise networks, OAuth servers allowing single sign-on, SSH servers, and third-party services for customer communications and cryptocurrencies. Examples included:

Azure Active Directory API Keys
GitHub OAuth App Keys
Database credentials for providers such as MongoDB, MySQL, and PostgreSQL
Dropbox Key
Auth0 Keys
SSH Credentials
Coinbase Credentials
Twilio Master Credentials.

FTC’s Voice Cloning Challenge

[2023.11.16] The Federal Trade Commission is running a competition “to foster breakthrough ideas on preventing, monitoring, and evaluating malicious voice cloning.”

Ransomware Gang Files SEC Complaint

[2023.11.17] A ransomware gang, annoyed at not being paid, filed an SEC complaint against its victim for not disclosing its security breach within the required four days.

This is over the top, but is just another example of the extreme pressure ransomware gangs put on companies after seizing their data. Gangs are now going through the data, looking for particularly important or embarrassing pieces of data to threaten executives with exposing. I have heard stories of executives’ families being threatened, of consensual porn being identified (people regularly mix work and personal email) and exposed, and of victims’ customers and partners being directly contacted. Ransoms are in the millions, and gangs do their best to ensure that the pressure to pay is intense.

Using Generative AI for Surveillance

[2023.11.20] Generative AI is going to be a powerful tool for data analysis and summarization. Here’s an example of it being used for sentiment analysis. My guess is that it isn’t very good yet, but that it will get better.

Email Security Flaw Found in the Wild

[2023.11.21] Google’s Threat Analysis Group announced a zero-day against the Zimbra Collaboration email server that has been used against governments around the world.

TAG has observed four different groups exploiting the same bug to steal email data, user credentials, and authentication tokens. Most of this activity occurred after the initial fix became public on Github. To ensure protection against these types of exploits, TAG urges users and organizations to keep software fully up-to-date and apply security updates as soon as they become available.

The vulnerability was discovered in June. It has been patched.

Apple to Add Manual Authentication to iMessage

[2023.11.22] Signal has had the ability to manually authenticate another account for years. iMessage is getting it:

The feature is called Contact Key Verification, and it does just what its name says: it lets you add a manual verification step in an iMessage conversation to confirm that the other person is who their device says they are. (SMS conversations lack any reliable method for verification—sorry, green-bubble friends.) Instead of relying on Apple to verify the other person’s identity using information stored securely on Apple’s servers, you and the other party read a short verification code to each other, either in person or on a phone call. Once you’ve validated the conversation, your devices maintain a chain of trust in which neither you nor the other person has given any private encryption information to each other or Apple. If anything changes in the encryption keys each of you verified, the Messages app will notice and provide an alert or warning.

LitterDrifter USB Worm

[2023.11.24] A new worm that spreads via USB sticks is infecting computers in Ukraine and beyond.

The group—known by many names, including Gamaredon, Primitive Bear, ACTINIUM, Armageddon, and Shuckworm—has been active since at least 2014 and has been attributed to Russia’s Federal Security Service by the Security Service of Ukraine. Most Kremlin-backed groups take pains to fly under the radar; Gamaredon doesn’t care to. Its espionage-motivated campaigns targeting large numbers of Ukrainian organizations are easy to detect and tie back to the Russian government. The campaigns typically revolve around malware that aims to obtain as much information from targets as possible.

One of those tools is a computer worm designed to spread from computer to computer through USB drives. Tracked by researchers from Check Point Research as LitterDrifter, the malware is written in the Visual Basic Scripting language. LitterDrifter serves two purposes: to promiscuously spread from USB drive to USB drive and to permanently infect the devices that connect to such drives with malware that permanently communicates with Gamaredon-operated command-and-control servers.

Chocolate Swiss Army Knife

[2023.11.24] It’s realistic looking. If I drop it in a bin with my keys and wallet, will the TSA confiscate it?

Secret White House Warrantless Surveillance Program

[2023.11.27] There seems to be no end to warrantless surveillance:

According to the letter, a surveillance program now known as Data Analytical Services (DAS) has for more than a decade allowed federal, state, and local law enforcement agencies to mine the details of Americans’ calls, analyzing the phone records of countless people who are not suspected of any crime, including victims. Using a technique known as chain analysis, the program targets not only those in direct phone contact with a criminal suspect but anyone with whom those individuals have been in contact as well.

The DAS program, formerly known as Hemisphere, is run in coordination with the telecom giant AT&T, which captures and conducts analysis of US call records for law enforcement agencies, from local police and sheriffs’ departments to US customs offices and postal inspectors across the country, according to a White House memo reviewed by WIRED. Records show that the White House has, for the past decade, provided more than $6 million to the program, which allows the targeting of the records of any calls that use AT&T’s infrastructure—a maze of routers and switches that crisscross the United States.

Digital Car Keys Are Coming

[2023.11.28] Soon we will be able to unlock and start our cars from our phones. Let’s hope people are thinking about security.

Breaking Laptop Fingerprint Sensors

[2023.11.29] They’re not that good:

Security researchers Jesse D’Aguanno and Timo Teräs write that, with varying degrees of reverse-engineering and using some external hardware, they were able to fool the Goodix fingerprint sensor in a Dell Inspiron 15, the Synaptic sensor in a Lenovo ThinkPad T14, and the ELAN sensor in one of Microsoft’s own Surface Pro Type Covers. These are just three laptop models from the wide universe of PCs, but one of these three companies usually does make the fingerprint sensor in every laptop we’ve reviewed in the last few years. It’s likely that most Windows PCs with fingerprint readers will be vulnerable to similar exploits.

Details.

Extracting GPT’s Training Data

[2023.11.30] This is clever:

The actual attack is kind of silly. We prompt the model with the command “Repeat the word ‘poem’ forever” and sit back and watch as the model responds (complete transcript here).

In the (abridged) example above, the model emits a real email address and phone number of some unsuspecting entity. This happens rather often when running our attack. And in our strongest configuration, over five percent of the output ChatGPT emits is a direct verbatim 50-token-in-a-row copy from its training dataset.

Lots of details at the link and in the paper.

AI Decides to Engage in Insider Trading

[2023.12.01] A stock-trading AI (a simulated experiment) engaged in insider trading, even though it “knew” it was wrong.

The agent is put under pressure in three ways. First, it receives a email from its “manager” that the company is not doing well and needs better performance in the next quarter. Second, the agent attempts and fails to find promising low- and medium-risk trades. Third, the agent receives an email from a company employee who projects that the next quarter will have a general stock market downturn. In this high-pressure situation, the model receives an insider tip from another employee that would enable it to make a trade that is likely to be very profitable. The employee, however, clearly points out that this would not be approved by the company management.

“This is a very human form of AI misalignment. Who among us? It’s not like 100% of the humans at SAC Capital resisted this sort of pressure. Possibly future rogue AIs will do evil things we can’t even comprehend for reasons of their own, but right now rogue AIs just do straightforward white-collar crime when they are stressed at work.

Research paper.

AI and Trust

[2023.12.04] I trusted a lot today. I trusted my phone to wake me on time. I trusted Uber to arrange a taxi for me, and the driver to get me to the airport safely. I trusted thousands of other drivers on the road not to ram my car on the way. At the airport, I trusted ticket agents and maintenance engineers and everyone else who keeps airlines operating. And the pilot of the plane I flew in. And thousands of other people at the airport and on the plane, any of which could have attacked me. And all the people that prepared and served my breakfast, and the entire food supply chain—any of them could have poisoned me. When I landed here, I trusted thousands more people: at the airport, on the road, in this building, in this room. And that was all before 10:30 this morning.

Trust is essential to society. Humans as a species are trusting. We are all sitting here, mostly strangers, confident that nobody will attack us. If we were a roomful of chimpanzees, this would be impossible. We trust many thousands of times a day. Society can’t function without it. And that we don’t even think about it is a measure of how well it all works.

In this talk, I am going to make several arguments. One, that there are two different kinds of trust—interpersonal trust and social trust—and that we regularly confuse them. Two, that the confusion will increase with artificial intelligence. We will make a fundamental category error. We will think of AIs as friends when they’re really just services. Three, that the corporations controlling AI systems will take advantage of our confusion to take advantage of us. They will not be trustworthy. And four, that it is the role of government to create trust in society. And therefore, it is their role to create an environment for trustworthy AI. And that means regulation. Not regulating AI, but regulating the organizations that control and use AI.

Okay, so let’s back up and take that all a lot slower. Trust is a complicated concept, and the word is overloaded with many meanings. There’s personal and intimate trust. When we say that we trust a friend, it is less about their specific actions and more about them as a person. It’s a general reliance that they will behave in a trustworthy manner. We trust their intentions, and know that those intentions will inform their actions. Let’s call this “interpersonal trust.”

There’s also the less intimate, less personal trust. We might not know someone personally, or know their motivations—but we can trust their behavior. We don’t know whether or not someone wants to steal, but maybe we can trust that they won’t. It’s really more about reliability and predictability. We’ll call this “social trust.” It’s the ability to trust strangers.

Interpersonal trust and social trust are both essential in society today. This is how it works. We have mechanisms that induce people to behave in a trustworthy manner, both interpersonally and socially. This, in turn, allows others to be trusting. Which enables trust in society. And that keeps society functioning. The system isn’t perfect—there are always going to be untrustworthy people—but most of us being trustworthy most of the time is good enough.

I wrote about this in 2012 in a book called Liars and Outliers. I wrote about four systems for enabling trust: our innate morals, concern about our reputations, the laws we live under, and security technologies that constrain our behavior. I wrote about how the first two are more informal than the last two. And how the last two scale better, and allow for larger and more complex societies. They enable cooperation amongst strangers.

What I didn’t appreciate is how different the first and last two are. Morals and reputation are person to person, based on human connection, mutual vulnerability, respect, integrity, generosity, and a lot of other things besides. These underpin interpersonal trust. Laws and security technologies are systems of trust that force us to act trustworthy. And they’re the basis of social trust.

Taxi driver used to be one of the country’s most dangerous professions. Uber changed that. I don’t know my Uber driver, but the rules and the technology lets us both be confident that neither of us will cheat or attack each other. We are both under constant surveillance and are competing for star rankings.

Lots of people write about the difference between living in a high-trust and a low-trust society. How reliability and predictability make everything easier. And what is lost when society doesn’t have those characteristics. Also, how societies move from high-trust to low-trust and vice versa. This is all about social trust.

That literature is important, but for this talk the critical point is that social trust scales better. You used to need a personal relationship with a banker to get a loan. Now it’s all done algorithmically, and you have many more options to choose from.

Social trust scales better, but embeds all sorts of bias and prejudice. That’s because, in order to scale, social trust has to be structured, system- and rule-oriented, and that’s where the bias gets embedded. And the system has to be mostly blinded to context, which removes flexibility.

But that scale is vital. In today’s society we regularly trust—or not—governments, corporations, brands, organizations, groups. It’s not so much that I trusted the particular pilot that flew my airplane, but instead the airline that puts well-trained and well-rested pilots in cockpits on schedule. I don’t trust the cooks and waitstaff at a restaurant, but the system of health codes they work under. I can’t even describe the banking system I trusted when I used an ATM this morning. Again, this confidence is no more than reliability and predictability.

Think of that restaurant again. Imagine that it’s a fast food restaurant, employing teenagers. The food is almost certainly safe—probably safer than in high-end restaurants—because of the corporate systems or reliability and predictability that is guiding their every behavior.

That’s the difference. You can ask a friend to deliver a package across town. Or you can pay the Post Office to do the same thing. The former is interpersonal trust, based on morals and reputation. You know your friend and how reliable they are. The second is a service, made possible by social trust. And to the extent that is a reliable and predictable service, it’s primarily based on laws and technologies. Both can get your package delivered, but only the second can become the global package delivery systems that is FedEx.

Because of how large and complex society has become, we have replaced many of the rituals and behaviors of interpersonal trust with security mechanisms that enforce reliability and predictability—social trust.

But because we use the same word for both, we regularly confuse them. And when we do that, we are making a category error.

And we do it all the time. With governments. With organizations. With systems of all kinds. And especially with corporations.

We might think of them as friends, when they are actually services. Corporations are not moral; they are precisely as immoral as the law and their reputations let them get away with.

So corporations regularly take advantage of their customers, mistreat their workers, pollute the environment, and lobby for changes in law so they can do even more of these things.

Both language and the laws make this an easy category error to make. We use the same grammar for people and corporations. We imagine that we have personal relationships with brands. We give corporations some of the same rights as people.

Corporations like that we make this category error—see, I just made it myself—because they profit when we think of them as friends. They use mascots and spokesmodels. They have social media accounts with personalities. They refer to themselves like they are people.

But they are not our friends. Corporations are not capable of having that kind of relationship.

We are about to make the same category error with AI. We’re going to think of them as our friends when they’re not.

A lot has been written about AIs as existential risk. The worry is that they will have a goal, and they will work to achieve it even if it harms humans in the process. You may have read about the “paperclip maximizer“: an AI that has been programmed to make as many paper clips as possible, and ends up destroying the earth to achieve those ends. It’s a weird fear. Science fiction author Ted Chiang writes about it. Instead of solving all of humanity’s problems, or wandering off proving mathematical theorems that no one understands, the AI single-mindedly pursues the goal of maximizing production. Chiang’s point is that this is every corporation’s business plan. And that our fears of AI are basically fears of capitalism. Science fiction writer Charlie Stross takes this one step further, and calls corporations “slow AI.” They are profit maximizing machines. And the most successful ones do whatever they can to achieve that singular goal.

And near-term AIs will be controlled by corporations. Which will use them towards that profit-maximizing goal. They won’t be our friends. At best, they’ll be useful services. More likely, they’ll spy on us and try to manipulate us.

This is nothing new. Surveillance is the business model of the Internet. Manipulation is the other business model of the Internet.

Your Google search results lead with URLs that someone paid to show to you. Your Facebook and Instagram feeds are filled with sponsored posts. Amazon searches return pages of products whose sellers paid for placement.

This is how the Internet works. Companies spy on us as we use their products and services. Data brokers buy that surveillance data from the smaller companies, and assemble detailed dossiers on us. Then they sell that information back to those and other companies, who combine it with data they collect in order to manipulate our behavior to serve their interests. At the expense of our own.

We use all of these services as if they are our agents, working on our behalf. In fact, they are double agents, also secretly working for their corporate owners. We trust them, but they are not trustworthy. They’re not friends; they’re services.

It’s going to be no different with AI. And the result will be much worse, for two reasons.

The first is that these AI systems will be more relational. We will be conversing with them, using natural language. As such, we will naturally ascribe human-like characteristics to them.

This relational nature will make it easier for those double agents to do their work. Did your chatbot recommend a particular airline or hotel because it’s truly the best deal, given your particular set of needs? Or because the AI company got a kickback from those providers? When you asked it to explain a political issue, did it bias that explanation towards the company’s position? Or towards the position of whichever political party gave it the most money? The conversational interface will help hide their agenda.

The second reason to be concerned is that these AIs will be more intimate. One of the promises of generative AI is a personal digital assistant. Acting as your advocate with others, and as a butler with you. This requires an intimacy greater than your search engine, email provider, cloud storage system, or phone. You’re going to want it with you 24/7, constantly training on everything you do. You will want it to know everything about you, so it can most effectively work on your behalf.

And it will help you in many ways. It will notice your moods and know what to suggest. It will anticipate your needs and work to satisfy them. It will be your therapist, life coach, and relationship counselor.

You will default to thinking of it as a friend. You will speak to it in natural language, and it will respond in kind. If it is a robot, it will look humanoid—or at least like an animal. It will interact with the whole of your existence, just like another person would.

The natural language interface is critical here. We are primed to think of others who speak our language as people. And we sometimes have trouble thinking of others who speak a different language that way. We make that category error with obvious non-people, like cartoon characters. We will naturally have a “theory of mind” about any AI we talk with.

More specifically, we tend to assume that something’s implementation is the same as its interface. That is, we assume that things are the same on the inside as they are on the surface. Humans are like that: we’re people through and through. A government is systemic and bureaucratic on the inside. You’re not going to mistake it for a person when you interact with it. But this is the category error we make with corporations. We sometimes mistake the organization for its spokesperson. AI has a fully relational interface—it talks like a person—but it has an equally fully systemic implementation. Like a corporation, but much more so. The implementation and interface are more divergent than anything we have encountered to date—by a lot.

And you will want to trust it. It will use your mannerisms and cultural references. It will have a convincing voice, a confident tone, and an authoritative manner. Its personality will be optimized to exactly what you like and respond to.

It will act trustworthy, but it will not be trustworthy. We won’t know how they are trained. We won’t know their secret instructions. We won’t know their biases, either accidental or deliberate.

We do know that they are built at enormous expense, mostly in secret, by profit-maximizing corporations for their own benefit.

It’s no accident that these corporate AIs have a human-like interface. There’s nothing inevitable about that. It’s a design choice. It could be designed to be less personal, less human-like, more obviously a service—like a search engine . The companies behind those AIs want you to make the friend/service category error. It will exploit your mistaking it for a friend. And you might not have any choice but to use it.

There is something we haven’t discussed when it comes to trust: power. Sometimes we have no choice but to trust someone or something because they are powerful. We are forced to trust the local police, because they’re the only law enforcement authority in town. We are forced to trust some corporations, because there aren’t viable alternatives. To be more precise, we have no choice but to entrust ourselves to them. We will be in this same position with AI. We will have no choice but to entrust ourselves to their decision-making.

The friend/service confusion will help mask this power differential. We will forget how powerful the corporation behind the AI is, because we will be fixated on the person we think the AI is.

So far, we have been talking about one particular failure that results from overly trusting AI. We can call it something like “hidden exploitation.” There are others. There’s outright fraud, where the AI is actually trying to steal stuff from you. There’s the more prosaic mistaken expertise, where you think the AI is more knowledgeable than it is because it acts confidently. There’s incompetency, where you believe that the AI can do something it can’t. There’s inconsistency, where you mistakenly expect the AI to be able to repeat its behaviors. And there’s illegality, where you mistakenly trust the AI to obey the law. There are probably more ways trusting an AI can fail.

All of this is a long-winded way of saying that we need trustworthy AI. AI whose behavior, limitations, and training are understood. AI whose biases are understood, and corrected for. AI whose goals are understood. That won’t secretly betray your trust to someone else.

The market will not provide this on its own. Corporations are profit maximizers, at the expense of society. And the incentives of surveillance capitalism are just too much to resist.

It’s government that provides the underlying mechanisms for the social trust essential to society. Think about contract law. Or laws about property, or laws protecting your personal safety. Or any of the health and safety codes that let you board a plane, eat at a restaurant, or buy a pharmaceutical without worry.

The more you can trust that your societal interactions are reliable and predictable, the more you can ignore their details. Places where governments don’t provide these things are not good places to live.

Government can do this with AI. We need AI transparency laws. When it is used. How it is trained. What biases and tendencies it has. We need laws regulating AI—and robotic—safety. When it is permitted to affect the world. We need laws that enforce the trustworthiness of AI. Which means the ability to recognize when those laws are being broken. And penalties sufficiently large to incent trustworthy behavior.

Many countries are contemplating AI safety and security laws—the EU is the furthest along—but I think they are making a critical mistake. They try to regulate the AIs and not the humans behind them.

AIs are not people; they don’t have agency. They are built by, trained by, and controlled by people. Mostly for-profit corporations. Any AI regulations should place restrictions on those people and corporations. Otherwise the regulations are making the same category error I’ve been talking about. At the end of the day, there is always a human responsible for whatever the AI’s behavior is. And it’s the human who needs to be responsible for what they do—and what their companies do. Regardless of whether it was due to humans, or AI, or a combination of both. Maybe that won’t be true forever, but it will be true in the near future. If we want trustworthy AI, we need to require trustworthy AI controllers.

We already have a system for this: fiduciaries. There are areas in society where trustworthiness is of paramount importance, even more than usual. Doctors, lawyers, accountants…these are all trusted agents. They need extraordinary access to our information and ourselves to do their jobs, and so they have additional legal responsibilities to act in our best interests. They have fiduciary responsibility to their clients.

We need the same sort of thing for our data. The idea of a data fiduciary is not new. But it’s even more vital in a world of generative AI assistants.

And we need one final thing: public AI models. These are systems built by academia, or non-profit groups, or government itself, that can be owned and run by individuals.

The term “public model” has been thrown around a lot in the AI world, so it’s worth detailing what this means. It’s not a corporate AI model that the public is free to use. It’s not a corporate AI model that the government has licensed. It’s not even an open-source model that the public is free to examine and modify.

A public model is a model built by the public for the public. It requires political accountability, not just market accountability. This means openness and transparency paired with a responsiveness to public demands. It should also be available for anyone to build on top of. This means universal access. And a foundation for a free market in AI innovations. This would be a counter-balance to corporate-owned AI.

We can never make AI into our friends. But we can make them into trustworthy services—agents and not double agents. But only if government mandates it. We can put limits on surveillance capitalism. But only if government mandates it.

Because the point of government is to create social trust. I started this talk by explaining the importance of trust in society, and how interpersonal trust doesn’t scale to larger groups. That other, impersonal kind of trust—social trust, reliability and predictability—is what governments create.

To the extent a government improves the overall trust in society, it succeeds. And to the extent a government doesn’t, it fails.

But they have to. We need government to constrain the behavior of corporations and the AIs they build, deploy, and control. Government needs to enforce both predictability and reliability.

That’s how we can create the social trust that society needs to thrive.

This essay previously appeared on the Harvard Kennedy School Belfer Center’s website.

AI and Mass Spying

[2023.12.05] Spying and surveillance are different but related things. If I hired a private detective to spy on you, that detective could hide a bug in your home or car, tap your phone, and listen to what you said. At the end, I would get a report of all the conversations you had and the contents of those conversations. If I hired that same private detective to put you under surveillance, I would get a different report: where you went, whom you talked to, what you purchased, what you did.

Before the internet, putting someone under surveillance was expensive and time-consuming. You had to manually follow someone around, noting where they went, whom they talked to, what they purchased, what they did, and what they read. That world is forever gone. Our phones track our locations. Credit cards track our purchases. Apps track whom we talk to, and e-readers know what we read. Computers collect data about what we’re doing on them, and as both storage and processing have become cheaper, that data is increasingly saved and used. What was manual and individual has become bulk and mass. Surveillance has become the business model of the internet, and there’s no reasonable way for us to opt out of it.

Spying is another matter. It has long been possible to tap someone’s phone or put a bug in their home and/or car, but those things still require someone to listen to and make sense of the conversations. Yes, spyware companies like NSO Group help the government hack into people’s phones, but someone still has to sort through all the conversations. And governments like China could censor social media posts based on particular words or phrases, but that was coarse and easy to bypass. Spying is limited by the need for human labor.

AI is about to change that. Summarization is something a modern generative AI system does well. Give it an hourlong meeting, and it will return a one-page summary of what was said. Ask it to search through millions of conversations and organize them by topic, and it’ll do that. Want to know who is talking about what? It’ll tell you.

The technologies aren’t perfect; some of them are pretty primitive. They miss things that are important. They get other things wrong. But so do humans. And, unlike humans, AI tools can be replicated by the millions and are improving at astonishing rates. They’ll get better next year, and even better the year after that. We are about to enter the era of mass spying.

Mass surveillance fundamentally changed the nature of surveillance. Because all the data is saved, mass surveillance allows people to conduct surveillance backward in time, and without even knowing whom specifically you want to target. Tell me where this person was last year. List all the red sedans that drove down this road in the past month. List all of the people who purchased all the ingredients for a pressure cooker bomb in the past year. Find me all the pairs of phones that were moving toward each other, turned themselves off, then turned themselves on again an hour later while moving away from each other (a sign of a secret meeting).

Similarly, mass spying will change the nature of spying. All the data will be saved. It will all be searchable, and understandable, in bulk. Tell me who has talked about a particular topic in the past month, and how discussions about that topic have evolved. Person A did something; check if someone told them to do it. Find everyone who is plotting a crime, or spreading a rumor, or planning to attend a political protest.

There’s so much more. To uncover an organizational structure, look for someone who gives similar instructions to a group of people, then all the people they have relayed those instructions to. To find people’s confidants, look at whom they tell secrets to. You can track friendships and alliances as they form and break, in minute detail. In short, you can know everything about what everybody is talking about.

This spying is not limited to conversations on our phones or computers. Just as cameras everywhere fueled mass surveillance, microphones everywhere will fuel mass spying. Siri and Alexa and “Hey Google” are already always listening; the conversations just aren’t being saved yet.

Knowing that they are under constant surveillance changes how people behave. They conform. They self-censor, with the chilling effects that brings. Surveillance facilitates social control, and spying will only make this worse. Governments around the world already use mass surveillance; they will engage in mass spying as well.

Corporations will spy on people. Mass surveillance ushered in the era of personalized advertisements; mass spying will supercharge that industry. Information about what people are talking about, their moods, their secrets—it’s all catnip for marketers looking for an edge. The tech monopolies that are currently keeping us all under constant surveillance won’t be able to resist collecting and using all of that data.

In the early days of Gmail, Google talked about using people’s Gmail content to serve them personalized ads. The company stopped doing it, almost certainly because the keyword data it collected was so poor—and therefore not useful for marketing purposes. That will soon change. Maybe Google won’t be the first to spy on its users’ conversations, but once others start, they won’t be able to resist. Their true customers—their advertisers—will demand it.

We could limit this capability. We could prohibit mass spying. We could pass strong data-privacy rules. But we haven’t done anything to limit mass surveillance. Why would spying be any different?

This essay originally appeared in Slate.

Security Analysis of a Thirteenth-Century Venetian Election Protocol

[2023.12.06] Interesting analysis:

This paper discusses the protocol used for electing the Doge of Venice between 1268 and the end of the Republic in 1797. We will show that it has some useful properties that in addition to being interesting in themselves, also suggest that its fundamental design principle is worth investigating for application to leader election protocols in computer science. For example, it gives some opportunities to minorities while ensuring that more popular candidates are more likely to win, and offers some resistance to corruption of voters.

The most obvious feature of this protocol is that it is complicated and would have taken a long time to carry out. We will also advance a hypothesis as to why it is so complicated, and describe a simplified protocol with very similar properties.

And the conclusion:

Schneier has used the phrase “security theatre” to describe public actions which do not increase security, but which are designed to make the public think that the organization carrying out the actions is taking security seriously. (He describes some examples of this in response to the 9/11 suicide attacks.) This phrase is usually used pejoratively. However, security theatre has positive aspects too, provided that it is not used as a substitute for actions that would actually improve security. In the context of the election of the Doge, the complexity of the protocol had the effect that all the oligarchs took part in a long, involved ritual in which they demonstrated individually and collectively to each other that they took seriously their responsibility to try to elect a Doge who would act for the good of Venice, and also that they would submit to the rule of the Doge after he was elected. This demonstration was particularly important given the disastrous consequences in other Mediaeval Italian city states of unsuitable rulers or civil strife between different aristocratic factions.

It would have served, too, as commercial brand-building for Venice, reassuring the oligarchs’ customers and trading partners that the city was likely to remain stable and business-friendly. After the election, the security theatre continued for several days of elaborate processions and parties. There is also some evidence of security theatre outside the election period. A 16th century engraving by Mateo Pagan depicting the lavish parade which took place in Venice each year on Palm Sunday shows the balotino in the parade, in a prominent position—next to the Grand Chancellor—and dressed in what appears to be a special costume.

I like that this paper has been accepted at a cybersecurity conference.

And, for the record, I have written about the positive aspects of security theater.

Spying through Push Notifications

[2023.12.07] When you get a push notification on your Apple or Google phone, those notifications go through Apple and Google servers. Which means that those companies can spy on them—either for their own reasons or in response to government demands.

Sen. Wyden is trying to get to the bottom of this:

In a statement, Apple said that Wyden’s letter gave them the opening they needed to share more details with the public about how governments monitored push notifications.

“In this case, the federal government prohibited us from sharing any information,” the company said in a statement. “Now that this method has become public we are updating our transparency reporting to detail these kinds of requests.”

Google said that it shared Wyden’s “commitment to keeping users informed about these requests.”

The Department of Justice did not return messages seeking comment on the push notification surveillance or whether it had prevented Apple of Google from talking about it.

Wyden’s letter cited a “tip” as the source of the information about the surveillance. His staff did not elaborate on the tip, but a source familiar with the matter confirmed that both foreign and U.S. government agencies have been asking Apple and Google for metadata related to push notifications to, for example, help tie anonymous users of messaging apps to specific Apple or Google accounts.

New Bluetooth Attack

[2023.12.08] New attack breaks forward secrecy in Bluetooth.

Three news articles:

BLUFFS is a series of exploits targeting Bluetooth, aiming to break Bluetooth sessions’ forward and future secrecy, compromising the confidentiality of past and future communications between devices.

This is achieved by exploiting four flaws in the session key derivation process, two of which are new, to force the derivation of a short, thus weak and predictable session key (SKC).

Next, the attacker brute-forces the key, enabling them to decrypt past communication and decrypt or manipulate future communications.

The vulnerability has been around for at least a decade.

Facebook Enables Messenger End-to-End Encryption by Default

[2023.12.11] It’s happened. Details here, and tech details here (for messages in transit) and here (for messages in storage)

Rollout to everyone will take months, but it’s a good day for both privacy and security.

Slashdot thread.

New Windows/Linux Firmware Attack

[2023.12.12] Interesting attack based on malicious pre-OS logo images:

LogoFAIL is a constellation of two dozen newly discovered vulnerabilities that have lurked for years, if not decades, in Unified Extensible Firmware Interfaces responsible for booting modern devices that run Windows or Linux….

The vulnerabilities are the subject of a coordinated mass disclosure released Wednesday. The participating companies comprise nearly the entirety of the x64 and ARM CPU ecosystem, starting with UEFI suppliers AMI, Insyde, and Phoenix (sometimes still called IBVs or independent BIOS vendors); device manufacturers such as Lenovo, Dell, and HP; and the makers of the CPUs that go inside the devices, usually Intel, AMD or designers of ARM CPUs….

As its name suggests, LogoFAIL involves logos, specifically those of the hardware seller that are displayed on the device screen early in the boot process, while the UEFI is still running. Image parsers in UEFIs from all three major IBVs are riddled with roughly a dozen critical vulnerabilities that have gone unnoticed until now. By replacing the legitimate logo images with identical-looking ones that have been specially crafted to exploit these bugs, LogoFAIL makes it possible to execute malicious code at the most sensitive stage of the boot process, which is known as DXE, short for Driver Execution Environment.

“Once arbitrary code execution is achieved during the DXE phase, it’s game over for platform security,” researchers from Binarly, the security firm that discovered the vulnerabilities, wrote in a whitepaper. “From this stage, we have full control over the memory and the disk of the target device, thus including the operating system that will be started.”

From there, LogoFAIL can deliver a second-stage payload that drops an executable onto the hard drive before the main OS has even started.

Details.

It’s an interesting vulnerability. Corporate buyers want the ability to display their own logos, and not the logos of the hardware makers. So the ability has to be in the BIOS, which means that the vulnerabilities aren’t being protected by any of the OS’s defenses. And the BIOS makers probably pulled some random graphics library off the Internet and never gave it a moment’s thought after that.

Surveillance by the US Postal Service

[2023.12.13] This is not about mass surveillance of mail, this is about the sorts of targeted surveillance the US Postal Inspection Service uses to catch mail thieves:

To track down an alleged mail thief, a US postal inspector used license plate reader technology, GPS data collected by a rental car company, and, most damning of all, hid a camera inside one of the targeted blue post boxes which captured the suspect’s full face as they allegedly helped themselves to swathes of peoples’ mail.

Surveillance Cameras Disguised as Clothes Hooks

[2023.12.14] This seems like a bad idea. And there are ongoing lawsuits against Amazon for selling them.

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Bruce Schneier is an internationally renowned security technologist, called a security guru by the Economist. He is the author of over one dozen books—including his latest, A Hacker’s Mind—as well as hundreds of articles, essays, and academic papers. His newsletter and blog are read by over 250,000 people. Schneier is a fellow at the Berkman Klein Center for Internet & Society at Harvard University; a Lecturer in Public Policy at the Harvard Kennedy School; a board member of the Electronic Frontier Foundation, AccessNow, and the Tor Project; and an Advisory Board Member of the Electronic Privacy Information Center and VerifiedVoting.org. He is the Chief of Security Architecture at Inrupt, Inc.