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Is your product’s AI annoying people?

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Artificial intelligence is allowing us all to consider surprising new ways to simplify the lives of our customers. As a product developer, your central focus is always on the customer. But new problems can arise when the specific solution under development helps one customer while alienating others.

We tend to think of AI as an incredible dream assistant to our lives and business operations, when that’s not always the case. Designers of new AI services should consider in what ways and for whom might these services be annoying, burdensome or problematic, and whether it involves the direct customer or others who are intertwined with the customer. When we apply AI services to make tasks easier for our customers that end up making things more difficult for others, that outcome can ultimately cause real harm to our brand perception.

Let’s consider one personal example taken from my own use of Amy.ai, a service (from x.ai) that provides AI assistants named Amy and Andrew Ingram. Amy and Andrew are AI assistants that help schedule meetings for up to four people. This service solves the very relatable problem of scheduling meetings over email, at least for the person who is trying to do the scheduling.

After all, who doesn’t want a personal assistant to whom you can simply say, “Amy, please find the time next week to meet with Tom, Mary, Anushya and Shiveesh.” In this way, you don’t have to arrange a meeting room, send the email, and go back and forth managing everyone’s replies. My own experience showed that while it was easier for me to use Amy to find a good time to meet with my four colleagues, it soon became a headache for those other four people. They resented me for it after being bombarded by countless emails trying to find some mutually agreeable time and place for everyone involved.

Automotive designers are another group that’s incorporating all kinds of new AI systems to enhance the driving experience. For instance, Tesla recently updated its autopilot software to allow a car to change lanes automatically when it sees fit, presumably when the system interprets that the next lane’s traffic is going faster.

In concept, this idea seems advantageous to the driver who can make a safe entrance into faster traffic, while relieving any cognitive burden of having to change lanes manually. Furthermore, by allowing the Tesla system to change lanes, it takes away the desire to play Speed Racer or edge toward competitiveness that one may feel on the highway.

However, for the drivers in other lanes who are forced to react to the Tesla autopilot, they may be annoyed if the Tesla jerks, slows down or behaves outside the normal realm of what people expect on the freeway. Moreover, if they are driving very fast and the autopilot did not recognize they were operating at a high rate of speed when the car decided to make the lane change, then that other driver can get annoyed. We can all relate to driving 75 mph in the fast lane, only to have someone suddenly pull in front of us at 70 as if they were clueless that the lane was moving at 75.

For two-lane traffic highways that are not busy, the Tesla software might work reasonably well. However, in my experience of driving around the congested freeways of the Bay Area, the system performed horribly whenever I changed crowded lanes, and I knew that it was angering other drivers most of the time. Even without knowing those irate drivers personally, I care enough about driving etiquette to politely change lanes without getting the finger from them for doing so.

Post Intelligence robot

Another example from the internet world involves Google Duplex, a clever feature for Android phone users that allows AI to make restaurant reservations. From the consumer point of view, having an automated system to make a dinner reservation on one’s behalf sounds excellent. It is advantageous to the person making the reservation because, theoretically, it will save the burden of calling when the restaurant is open and the hassle of dealing with busy signals and callbacks.

However, this tool is also potentially problematic for the restaurant worker who answers the phone. Even though the system may introduce itself as artificial, the burden shifts to the restaurant employee to adapt and master a new and more limited interaction to achieve the same goal — making a simple reservation.

On the one hand, Duplex is bringing customers to the restaurant, but on the other hand, the system is narrowing the scope of interaction between the restaurant and its customer. The restaurant may have other tables on different days, or it may be able to squeeze you in if you leave early, but the system might not handle exceptions like this. Even the idea of an AI bot bothering the host who answers the phone doesn’t seem quite right.

As you think about making the lives of your customers easier, consider how the assistance you are dreaming about might be more of a nightmare for everyone else associated with your primary customer. If there is a question regarding the negative experience of anyone related to your AI product, explore that experience further to determine if there is another better way to still delight them without angering their neighbors.

From a user-experience perspective, developing a customer journey map can be a helpful way to explore the actions, thoughts and emotional experiences of your primary customer or “buyer persona.” Identify the touchpoints in which your system interacts with innocent bystanders who are not your direct customers. For those people unaware of your product, explore their interaction with your buyer persona, specifically their emotional experience.

An aspirational goal should be to delight this adjacent group of people enough that they would move toward being prospects and, eventually, becoming your customers as well. Also, you can use participant ethnography to analyze the innocent bystander in relation to your product. This is a research method that combines the observations of people as they interact with processes and the product.

A guiding design inspiration for this research could be, “How can our AI system behave in such a way that everyone who might come into contact with our product is enchanted and wants to know more?”

That’s just human intelligence, and it’s not artificial.

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3 iOS 0-days, a cellular network compromise, and HTTP used to infect an iPhone

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Apple has patched a potent chain of iOS zero-days that were used to infect the iPhone of an Egyptian presidential candidate with sophisticated spyware developed by a commercial exploit seller, Google and researchers from Citizen Lab said Friday.

The previously unknown vulnerabilities, which Apple patched on Thursday, were exploited in clickless attacks, meaning they didn’t require a target to take any steps other than to visit a website that used the HTTP protocol rather than the safer HTTPS alternative. A packet inspection device sitting on a cellular network in Egypt kept an eye out for connections from the phone of the targeted candidate and, when spotted, redirected it to a site that delivered the exploit chain, according to Citizen Lab, a research group at the University of Toronto’s Munk School.

A cast of villains, 3 0-days, and a compromised cell network

Citizen Lab said the attack was made possible by participation from the Egyptian government, spyware known as Predator sold by a company known as Cytrox, and hardware sold by Egypt-based Sandvine. The campaign targeted Ahmed Eltantawy, a former member of the Egyptian Parliament who announced he was running for president in March. Citizen Lab said the recent attacks were at least the third time Eltantawy’s iPhone has been attacked. One of them, in 2021, was successful and also installed Predator.

“The use of mercenary spyware to target a senior member of a country’s democratic opposition after they had announced their intention to run for president is a clear interference in free and fair elections and violates the rights to freedom of expression, assembly, and privacy,” Citizen Lab researchers Bill Marczak, John Scott-Railton, Daniel Roethlisberger, Bahr Abdul Razzak, Siena Anstis, and Ron Deibert wrote in a 4,200-word report. “It also directly contradicts how mercenary spyware firms publicly justify their sales.”

The vulnerabilities, which are patched in iOS versions 16.7 and iOS 17.0.1, are tracked as:

  • CVE-2023-41993: Initial remote code execution in Safari
  • CVE-2023-41991: PAC bypass
  • CVE-2023-41992: Local privilege escalation in the XNU Kernel

According to research published Friday by members of Google’s Threat Analysis Group, the attackers who exploited the iOS vulnerabilities also had a separate exploit for installing the same Predator spyware on Android devices. Google patched the flaws on September 5 after receiving a report by a research group calling itself DarkNavy.

“TAG observed these exploits delivered in two different ways: the MITM injection and via one-time links sent directly to the target,” Maddie Stone, a researcher with the Google Threat Analysis Group wrote. “We were only able to obtain the initial renderer remote code execution vulnerability for Chrome, which was exploiting CVE-2023-4762.”

The attack was complex. Besides leveraging three separate iOS vulnerabilities, it also relied on hardware made by a manufacturer known as Sandvine. Sold under the brand umbrella PacketLogic, the hardware sat on the cellular network the targeted iPhone accessed and monitored traffic passing over it for his phone. Despite the precision, Citizen Lab said that the attack is blocked when users turn on a feature known as Lockdown, which Apple added to iOS last year. More about that later.

There’s little information about the iOS exploit chain other than it automatically triggered when a target visited a site hosting the malicious code. Once there, the exploits installed Predator with no further user action required.

To surreptitiously direct the iPhone to the attack site, it only needed to visit any HTTP site. Over the past five years or so, HTTPS has become the dominant means of connecting to websites because the encryption it uses prevents adversary-in-the-middle attackers from monitoring or manipulating data sent between the site and the visitor. HTTP sites still exist, and sometimes HTTPS connections can be downgraded to unencrypted HTTP ones.

Once Eltantawy visited an HTTP site, the PacketLogic device injected data into the traffic that surreptitiously connected the Apple device to a site that triggered the exploit chain.

Network diagram showing the Spyware Injection Middlebox located on a link between Telecom Egypt and Vodafone Egypt.
Enlarge / Network diagram showing the Spyware Injection Middlebox located on a link between Telecom Egypt and Vodafone Egypt.

Predator, the payload installed in the attack, is sold to a wide array of governments, including those of Armenia, Egypt, Greece, Indonesia, Madagascar, Oman, Saudi Arabia, and Serbia. Citizen Lab has said that Predator was used to target Ayman Nour, a member of the Egyptian political opposition living in exile in Turkey, and an Egyptian exiled journalist who hosts a popular news program and wishes to remain anonymous. Last year researchers from Cisco’s Talo security team exposed the inner workings of the malware after obtaining a binary of it.

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Incomplete disclosures by Apple and Google create “huge blindspot” for 0-day hunters

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Incomplete information included in recent disclosures by Apple and Google reporting critical zero-day vulnerabilities under active exploitation in their products has created a “huge blindspot” that’s causing a large number of offerings from other developers to go unpatched, researchers said Thursday.

Two weeks ago, Apple reported that threat actors were actively exploiting a critical vulnerability in iOS so they could install espionage spyware known as Pegasus. The attacks used a zero-click method, meaning they required no interaction on the part of targets. Simply receiving a call or text on an iPhone was enough to become infected by the Pegasus, which is among the world’s most advanced pieces of known malware.

“Huge blindspot”

Apple said the vulnerability, tracked as CVE-2023-41064, stemmed from a buffer overflow bug in ImageIO, a proprietary framework that allows applications to read and write most image file formats, which include one known as WebP. Apple credited the discovery of the zero-day to Citizen Lab, a research group at the University of Toronto’s Munk School that follows attacks by nation-states targeting dissidents and other at-risk groups.

Four days later, Google reported a critical vulnerability in its Chrome browser. The company said the vulnerability was what’s known as a heap buffer overflow that was present in WebP. Google went on to warn that an exploit for the vulnerability existed in the wild. Google said that the vulnerability, designated as CVE-2023-4863, was reported by the Apple Security Engineering and Architecture team and Citizen Lab.

Speculation, including from me, quickly arose that a large number of similarities strongly suggested that the underlying bug for both vulnerabilities was the same. On Thursday, researchers from security firm Rezillion published evidence that they said made it “highly likely” both indeed stemmed from the same bug, specifically in libwebp, the code library that apps, operating systems, and other code libraries incorporate to process WebP images.

Rather than Apple, Google, and Citizen Lab coordinating and accurately reporting the common origin of the vulnerability, they chose to use a separate CVE designation, the researchers said. The researchers concluded that “millions of different applications” would remain vulnerable until they, too, incorporated the libwebp fix. That, in turn, they said, was preventing automated systems developers use to track known vulnerabilities in their offerings from detecting a critical vulnerability that’s under active exploitation.

“Since the vulnerability is scoped under the overarching product containing the vulnerable dependency, the vulnerability will only be flagged by vulnerability scanners for these specific products,” Rezillion researchers Ofri Ouzan and Yotam Perkal wrote. “This creates a HUGE blindspot for organizations blindly relying on the output of their vulnerability scanner.”

Google has further come under criticism for limiting the scope of CVE-2023-4863 to Chrome rather than in libwebp. Further, the official description describes the vulnerability as a heap buffer overflow in WebP in Google Chrome.

In an email, a Google representative wrote: “Many platforms implement WebP differently. We do not have any details about how the bug impacts other products. Our focus was getting a fix out to the Chromium community and affected Chromium users as soon as possible. It is best practice for software products to track upstream libraries they depend on in order to pick up security fixes and improvements.”

The representative noted that the WebP image format is mentioned in its disclosure and the official CVE page. The representative didn’t explain why the official CVE and Google’s disclosure did not mention the widely used libwebp library or the likelihood that other software was also likely to be vulnerable.

The Google representative didn’t answer a question asking if CVE-2023-4863 and CVE-2023-41064 stemmed from the same vulnerability. Citizen Lab and Apple didn’t respond to emailed questions before this story went live.

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Signal preps its encryption engine for the quantum doomsday inevitability

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The Signal Foundation, maker of the Signal Protocol that encrypts messages sent by more than a billion people, has rolled out an update designed to prepare for a very real prospect that’s never far from the thoughts of just about every security engineer on the planet: the catastrophic fall of cryptographic protocols that secure some of the most sensitive secrets today.

The Signal Protocol is a key ingredient in the Signal, Google RCS, and WhatsApp messengers, which collectively have more than 1 billion users. It’s the engine that provides end-to-end encryption, meaning messages encrypted with the apps can be decrypted only by the recipients and no one else, including the platforms enabling the service. Until now, the Signal Protocol encrypted messages and voice calls with X3DH, a specification based on a form of cryptography known as Elliptic Curve Diffie-Hellman.

A brief detour: WTF is ECDH?

Often abbreviated as ECDH, Elliptic Curve Diffie-Hellman is a protocol unto its own. It combines two main building blocks. The first part involves the use of elliptic curves to form asymmetric key pairs, each of which is unique to each user. One key in the pair is public and available to anyone to use for encrypting messages sent to the person who owns it. The corresponding private key is closely guarded by the user. It allows the user to decrypt the messages. Cryptography relying on a public-private key pair is often known as asymmetric encryption.

The security of asymmetric encryption is based on mathematical one-way functions. Also known as trapdoor functions, these problems are easy to compute in one direction and substantially harder to compute in reverse. In elliptic curve cryptography, this one-way function is based on the Discrete Logarithm problem in mathematics. The key parameters are based on specific points in an elliptic curve, which is defined as the field of integers modulo prime P.

When someone knows the starting point (A) in the above image showing an elliptic curve and the number of hops required to get to the endpoint (E), it’s easy to know where (E) is. But when all someone knows is the starting and end points, it’s next to impossible to deduce how many hops are required.

As explained in an Ars article from 2013:

Let’s imagine this curve as the setting for a bizarre game of billiards. Take any two points on the curve and draw a line through them; the line will intersect the curve at exactly one more place. In this game of billiards, you take a ball at point A and shoot it toward point B. When it hits the curve, the ball bounces either straight up (if it’s below the x-axis) or straight down (if it’s above the x-axis) to the other side of the curve.

We can call this billiards move on two points “dot.” Any two points on a curve can be dotted together to get a new point.

A dot B = C

We can also string moves together to “dot” a point with itself over and over.

A dot A = B

A dot B = C

A dot C = D

It turns out that if you have two points, an initial point “dotted” with itself n times to arrive at a final point, finding out n when you only know the final point and the first point is hard. To continue our bizarro billiards metaphor, imagine that one person plays our game alone in a room for a random period of time. It is easy for him to hit the ball over and over following the rules described above. If someone walks into the room later and sees where the ball has ended up, even if they know all the rules of the game and where the ball started, they cannot determine the number of times the ball was struck to get there without running through the whole game again until the ball gets to the same point. Easy to do, hard to undo. This is the basis for a very good trapdoor function.

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