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AI and Mass Spying

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/12/ai-and-mass-spying.html

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.

AI and Trust

Post Syndicated from B. Schneier original https://www.schneier.com/blog/archives/2023/12/ai-and-trust.html

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.

Friday Squid Blogging: Strawberry Squid in the Galápagos

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/12/friday-squid-blogging-strawberry-squid-in-the-galapagos.html

Scientists have found Strawberry Squid, “whose mismatched eyes help them simultaneously search for prey above and below them,” among the coral reefs in the Galápagos Islands.

As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.

Read my blog posting guidelines here.

 

AI Decides to Engage in Insider Trading

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/12/ai-decides-to-engage-in-insider-trading.html

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.

More:

“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.

More from the news article:

Though wouldn’t it be funny if this was the limit of AI misalignment? Like, we will program computers that are infinitely smarter than us, and they will look around and decide “you know what we should do is insider trade.” They will make undetectable, very lucrative trades based on inside information, they will get extremely rich and buy yachts and otherwise live a nice artificial life and never bother to enslave or eradicate humanity. Maybe the pinnacle of evil ­—not the most evil form of evil, but the most pleasant form of evil, the form of evil you’d choose if you were all-knowing and all-powerful ­- is some light securities fraud.

Extracting GPT’s Training Data

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/11/extracting-gpts-training-data.html

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.

Breaking Laptop Fingerprint Sensors

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/11/breaking-laptop-fingerprint-sensors.html

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.

Secret White House Warrantless Surveillance Program

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/11/secret-white-house-warrantless-surveillance-program.html

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.

Top announcements of AWS re:Invent 2023

Post Syndicated from AWS Editorial Team original https://aws.amazon.com/blogs/aws/top-announcements-of-aws-reinvent-2023/

Welcome to Las Vegas, where excitement fills the air for the premier AWS event of the year – re:Invent 2023! Happening Nov. 27 – Dec. 1, re:Invent 2023 offers keynotes, training, Innovation Talks, AWS Builder Labs, and much more to inspire you on your cloud journey.

Can’t make it in person? Tune in to the live streams of the keynotes, and check back here as we provide daily updates on the most exciting product launches.

If you don’t want to miss a thing, here are a few more ways to keep in touch:

(This post was last updated: 5:05 p.m. PST, Nov. 26, 2023.)

Use natural language to query Amazon CloudWatch logs and metrics (preview)
To make it easy to interact with your operational data, Amazon CloudWatch is introducing natural language query generation for Logs and Metrics Insights.

Increase collaboration and securely share cloud knowledge with AWS re:Post Private
re:Post Private includes content tailored specifically for your organization’s use cases, along with private discussion and collaboration forums for the members of your organization and your AWS account team.

Use anomaly detection with AWS Glue to improve data quality (preview)
This new feature will help to improve your data quality by using machine learning to detect statistical anomalies and unusual patterns.

Mutual authentication for Application Load Balancer reliably verifies certificate-based client identities
With this new feature, you can now offload client authentication to Application Load Balancer, ensuring only trusted clients communicate with backend applications.

Check your AWS Free Tier usage programmatically with a new API
You can use the API directly with the AWS Command Line Interface or integrate it into an application with the AWS SDKs.

Use Amazon CloudWatch to consolidate hybrid, multicloud, and on-premises metrics
You can now consolidate metrics from your hybrid, multicloud, and on-premises data sources using Amazon CloudWatch and process them in a consistent, unified fashion.

Announcing cross-region data replication for Amazon WorkSpaces
Snapshots are taken every 12 hours, replicated to the desired destination region, and are used to provide a recovery point objective of 12-24 hours.

Amazon Transcribe Call Analytics adds new generative AI-powered call summaries (preview)
Powered by Amazon Bedrock, this feature helps businesses improve customer experience, and agent and supervisor productivity by automatically summarizing customer service calls.

Build generative AI apps using AWS Step Functions and Amazon Bedrock
Step Functions provides two new optimized API actions for Amazon Bedrock: InvokeModel and CreateModelCustomizationJob.

New Cost Optimization Hub centralizes recommended actions to save you money
This new AWS Billing and Cost Management feature makes it easy for you to identify, filter, aggregate, and quantify savings for AWS cost optimization recommendations.

Amazon CloudWatch Logs now offers automated pattern analytics and anomaly detection
Amazon CloudWatch can now automatically recognize and cluster patterns among log records, extract noteworthy content and trends, and notify you of anomalies using advanced machine learning algorithms.

Amazon Managed Service for Prometheus collector provides agentless metric collection for Amazon EKS
This new capability discovers and collects Prometheus metrics from Amazon Elastic Kubernetes Service (Amazon EKS) automatically and without an agent.

Optimize your storage costs for rarely-accessed files with Amazon EFS Archive
We’ve added a new storage class for Amazon Elastic File System optimized for long-lived data that is rarely accessed.

New Amazon CloudWatch log class for infrequent access logs at a reduced price
This new log class offers a tailored set of capabilities at a lower cost for infrequently accessed logs, enabling customers to consolidate all their logs in one place in a cost-effective manner.

Friday Squid Blogging: Squid Nebula

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/11/friday-squid-blogging-squid-nebula.html

Pretty photograph.

The Squid Nebula is shown in blue, indicating doubly ionized oxygen—­which is when you ionize your oxygen once and then ionize it again just to make sure. (In all seriousness, it likely indicates a low-mass star nearing the end of its life).

As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.

Read my blog posting guidelines here.

 

LitterDrifter USB Worm

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/11/litterdrifter-usb-worm.html

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.

Apple to Add Manual Authentication to iMessage

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/11/apple-to-add-manual-authentication-to-imessage.html

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.

Email Security Flaw Found in the Wild

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/11/email-security-flaw-found-in-the-wild.html

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.

Нашенската Коробочка

Post Syndicated from Григор original http://www.gatchev.info/blog/?p=2611

Една статия в BBC (https://www.bbc.com/news/world-europe-67427422) ме върна към спомен, който може би беше по-добре да бях забравил… Но пък може би ще е от полза за някого.

(Ако статията в BBC един ден не е достъпна: разказва за една съвременна руска доносничка, под името Анна Коробкова, и за жертвите ѝ. Топени, вероятно често и клеветени, че не подкрепят „специалната военна операция“ – руската агресия в Украйна. Тъй де, ако агресията не успее, може да се наложи Русия да изплаща репарации. Стандартът и доходите на Коробкова ще спаднат, може дори да ѝ се наложи да мине на пълно работно време. Така че тя не само има тези възгледи, има и интерес…)

Познавах такава „Анна Коробкова“ тук, в България. И за нея донасянето беше и доход, и смисълът на живота ѝ. Очите ѝ блестяха и пръскаше слюнки от кеф, докато разказваше. Как е пращала „където им е мястото“ всякакви „изроди“. „Капиталистически агенти“, разказвали политически вицове. „Безсрамни хлапета“, целували се на обществено място. „Смотаняк“ съсед, който не ѝ позволил да си свърже кухнята към неговия електромер. „Пижони“ с бради или къси полички. „Градска кокона“, която слагала саксии с цветя по стълбището на входа. „Пияница“, който се „скатавал“ от манифестация на 9 септември. „Забравил се“ съсед по вилно място, не разбрал, че мястото му се полага на нея и мъжа ѝ, а не на такива като него…

Тя нямаше компютър и база данни кого е натопила и какво му се е случило, като Коробкова. Имаше картотека с листче в нея за всяка жертва. „Ейййй такава!“, показваше гордо с ръце близо метър дължина. „Когото съм почнала, доде не го свърша, не съм го оставяла! Един няма да съм помилвала! Всичко си пишех там – кога за кого съм писала, какво съм написала, какво кога съм му докарала…“

Ненавистта ѝ към промените нямаше как да бъде предадена с думи. Те ѝ бяха отнели абсолютно всичко. Не, не хубавия апартамент, нито пък вилата. Нито дори бижутата на „градската кокона“, оказали се незнайно как у нея. Заради тези неща тя беше съсипвала и смазвала хора, но всъщност те бяха само израз на истински ценното ѝ – възможността да тъпче и съсипва който поиска. Промените ѝ бяха отнели именно него – отмъщаването на по-свестните от нея за това, че са по-свестни…

Гледах я, стопена от болестта до скелет в кожа, пръскащ ненавист, и изведнъж си я представих като Ам-гъл. Сдобил се с Пръстена на всевластието, но поискал и получил от него по своята нищожна мяра. Да може безнаказано да хапе по краката и да стиска за гушите по-читавите от него…

Беше стигнала дотам в последните си часове да може единствено да сподели, и така преживее отново, това си щастие. С човек, за който не знаеше на практика нищо, освен че се е опитал преди години да спаси мъжа ѝ. Сигурно в изнемощението си приравняваше това до споделяне на „ценностите“ ѝ… Едва се сдържах да не избухна, да си тръгна и да не я видя никога повече. Спираше ме единствено някакво едва доловимо усещане в дъното на ума ми, че дори такъв човек заслужава една последна милост. Някой да го изслуша, преди да си е тръгнал. Да му даде възможността да си припомни и преживее пак каквото е имал за щастие. И заради него, и още повече заради собствената си съвест…

… Дълго време си мислех – какво ли е подходящото определение за такъв човек. Чак когато статията в BBC ми върна спомена, името „Коробкова“ изведнъж намери мястото си в пъзела. Тази жена беше нашенска тоталитарна Коробочка. Изкривена дотам да събира в картотека – материален израз на истинската ѝ същност, точно както заключените шкафове с натрупани неща са истинската същност на Гоголевата Коробочка – единственото щастие, което е могла да има. Ужасяващо с безчовечността си..

Чудех се – какво ли я е направило такава? Може би произход или преживяно – и до днес не зная нищичко и за двете, нито има вече как да науча. Но знам за устройството и работата на мозъка повече от немалко неврофизиолози. Вероятно дефицит в латералния стриатум, по-точно в еферентите на кортико-таламо-стриато-кортикалния кръг. Водещ до неоснователно, но безмилостно усещане за собствена незначимост. Което причинява страдание, силно като това от жестока болка, ако и с различен източник. (За който не знае – усещането за болка и изпитването на страдание от нея са два различни механизма. Подобно страдание може да носи всяко усещане, което говори за някаква опасност, ако е достатъчно силно.)

И тя е търсела как да облекчи това усещане, според каквото е знаела и виждала. А комунистическото общество е имало нужда и е предлагало нужното ѝ облекчение срещу писане на доноси… Но тъй като усещането е болестно, няма как да се задоволи трайно. И в търсене на облекчение тя е слизала все по-надолу по спиралата. Научила се е да вярва в глупости, да мрази каквото всъщност не ѝ вреди. Да се самооправдава за причинявана на другите болка – тоест, отначало вероятно дори е изпитвала угризения… Всъщност тя беше пациент, страдалец, не злодей по същност. Злодей я бяха направили болестта ѝ и болното общество.

Да, сигурно всеки злодей си има причина да е такъв. Да, злодеите трябва да бъдат спирани непоколебимо, преди да причинят зло на други хора и може би да ги превърнат също в злодеи. Но в същността си те имат нужда от лечение, от помощ. Наказанието може би понякога е нужната им помощ, но сигурно не винаги.

Глупости? Може би. Но един лекар е длъжен да мисли така. Юридическото право да бъдеш лекар го дава дипломата, но моралното право го дава този начин на мислене.

Friday Squid Blogging: Unpatched Vulnerabilities in the Squid Caching Proxy

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/11/friday-squid-blogging-unpatched-vulnerabilities-in-the-squid-caching-proxy.html

In a rare squid/security post, here’s an article about unpatched vulnerabilities in the Squid caching proxy.

As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.

Read my blog posting guidelines here.

Ransomware Gang Files SEC Complaint

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/11/ransomware-gang-files-sec-complaint.html

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.

Leaving Authentication Credentials in Public Code

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/11/leaving-authentication-credentials-in-public-code.html

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.