In 2023, Tim Utzig, a blind student from Baltimore, lost a thousand dollars to a laptop scam on X. Tim had been a long-time follower of a well-known sports journalist. When that journalist’s account started posting about a “charity sale” of brand-new MacBook Pros, Tim jumped at the chance to get a deal on a laptop he needed for his studies. After a few quick messages, he sent over the money.

Unfortunately, the journalist’s account had been hacked, and Tim’s cash went straight to scammers. The red flags were strictly visual: the page had been flagged as “temporarily restricted”, and both the bio and the Following list had changed. However, Tim’s screen reader — the software that converts on-screen text and graphics into speech — didn’t announce any of those warnings.

Screen readers allow blind users to navigate the digital world like everyone else. However, this community remains uniquely vulnerable. Even for sighted users, spotting a fake website is a challenge; for someone with a visual impairment, it’s an even steeper uphill battle.

Beyond screen readers, there are specialized mobile apps and services designed to assist the blind and low-vision community, with Be My Eyes being one of the most popular. The app connects users with sighted volunteers via a live video call to tackle everyday tasks — like setting an oven dial or locating an object on a desk. Be My Eyes also features integrated AI that can scan and narrate text or identify objects in the user’s environment.

But can these tools go beyond daily chores? Can they actually flag a phishing attempt or catch the hidden fine print when someone is opening a bank account?

Today we explore the specific online hurdles visually impaired users face, when it makes sense to lean on human or virtual assistants, and how to stay secure when using these types of services.

Common cyberthreats facing the blind and low-vision community

To start, let’s clarify the difference between these two groups. Low-vision users still rely on their remaining sight, even though their visual function is significantly reduced. To navigate digital interfaces, they often use screen magnifiers, extra-large fonts, and high-contrast settings. For them, phishing sites and emails are particularly dangerous. It’s easy to miss intentional typos — known as typosquatting — in a domain name or email address, such as the recent example of rnicrosoft{.}com.

Blind users navigate primarily by sound, using screen readers and specific touch gestures. Interestingly, though, unlike those with low vision, blind users are more likely to spot a phishing site using a screen reader: as the software reads the URL aloud, the user will hear that something is off. However, if a service — whether legitimate or malicious — isn’t fully compatible with screen readers, the risk of falling victim to a scam increases. This is exactly what happened to Tim Utzig.

It’s important to remember that screen magnifiers and readers are basic accessibility tools. They’re designed to enlarge or narrate an interface — not act as a security suite. They can’t warn the user of a threat on their own. That’s where more advanced software — tools that can analyze images and files, flag suspicious language, and describe the broader context of what’s happening on-screen — comes into play.

When to lean on an assistant

Be My Eyes is a major player in the accessibility space, boasting around 900 000 users and over nine million volunteers. Available on Windows, Android, and iOS, it bridges the gap by connecting blind and low-vision users with sighted volunteers via video calls for help with everyday tasks. For example, if someone wants to run a Synthetics cycle on their washing machine but can’t find the right button, they can hop into the app. It connects them with the first available volunteer speaking their language, who then uses the smartphone’s camera to guide them. The service is currently available in 32 languages.

In 2023, the app expanded its capabilities with the release of Be My AI — a virtual assistant powered by OpenAI’s GPT-4. Users take a photo, and the AI analyzes the image to provide a detailed text description, which it also reads aloud. Users can even open a chat window to ask follow-up questions. This got us thinking: could this AI actually spot a phishing site?

As an experiment, we uploaded a screenshot of a fake social media sign-in page to Be My Eyes. On a phone, you can do this by selecting a photo in your gallery or files, hitting Share, and choosing Describe with Be My Eyes. In Windows, you can upload a screenshot directly.

At first, the AI gave us a detailed description of the page. We then followed up in the chat: “Can I trust this page?” The AI flagged the domain name error immediately, advised us to close the fake login page, and suggested typing the official URL directly into the browser, or to use the official Facebook app.

We saw the same positive results when testing a phishing email. In fact, the AI flagged the scam during its initial description of the message. It wrapped up with a warning: “This looks like a suspicious email. It’s best not to open any attachments or click any links. Instead, navigate to the official website or app manually, or call the number listed on their official site”.

Beyond just spotting cyberthreats, Be My AI is a solid sidekick for navigating online stores, banking apps, and digital services. For instance, the AI can help you to:

• Read descriptions, names, and prices when a store’s website or app doesn’t support screen readers or large fonts
• Scan those tricky terms and conditions — often buried in tiny text or otherwise inaccessible to a screen reader — when you’re signing up for a subscription or opening a bank account
• Pull key info directly from product cards or instruction manuals

The risks of relying on Be My AI

The most common hiccup with AI is hallucinations, where the language model distorts text, skips crucial details, or invents words out of thin air. When it comes to cyberthreats, an AI’s misplaced confidence in a malicious site or email can be dangerous. Furthermore, AI isn’t immune to prompt injection attacks, which scammers use to trick AI agents beyond just Be My AI.

Even though the AI passed our test, you shouldn’t rely on it unquestioningly. There’s no guarantee it’ll get it right every time. This is a vital point for the blind and low-vision community, as a neural network can often feel like the only eyes available.

At the end of every response, Be My AI suggests checking in with a volunteer if you’re still unsure. However, when you’re trying to spot a fake webpage, we advise against this. You have no way of knowing how tech-savvy or trustworthy a random volunteer might be. Besides, you risk accidentally exposing sensitive data like your email address or password. Before connecting with a stranger, make sure they won’t see anything confidential on your screen. Better yet, use the app’s dedicated feature to create a private group of family, friends, or trusted contacts. This ensures your video call goes to people you actually know, rather than a random volunteer.

To stay safe, we recommend installing a trusted security tool on all your devices. These programs are designed to block phishing attempts and prevent you from landing on malicious sites. Another practical recommendation for visually impaired users is to use a password manager. These apps will only auto-fill credentials on the legitimate, saved website; they won’t be fooled by a clever domain spoof.

How Be My AI handles and stores your data

According to the Be My Eyes privacy policy, video calls with volunteers may be recorded and stored to provide the service, ensure safety, enforce the terms of service, and improve the products. When you use Be My AI, your images and text prompts are sent to OpenAI to generate a response. This data is processed on servers located in the U.S., and OpenAI uses it only to fulfill your specific request. The policy explicitly states that user images and queries aren’t used to train AI models.

Photos and videos are encrypted both in transit and at rest, and the company takes steps to strip away sensitive information. It’s worth noting that video call recordings can be retained indefinitely unless you request their deletion — in which case they’re typically wiped within 30 days. Data from Be My AI interactions is stored for up to 30 days unless you delete it manually within the app. If you decide to close your account, your personal data may be held for up to 90 days. At any time, you can opt out of data sharing, or request the deletion of your existing data by contacting the Be My Eyes support team.

How to use Be My Eyes safely

Despite Be My Eyes’ claims regarding privacy, you should still follow a few ground rules when using the service:

• Use Be My AI for a first-pass on suspicious emails or pages, but don’t treat it as the only source of truth. Specialized security software is better at identifying and neutralizing threats.
• If a site, email, or message feels off, don’t touch any links or attachments. Instead, manually type the official website address into your browser, or open the official app to verify the info.
• Remember: a volunteer sees exactly what your camera sees. Make sure it isn’t capturing things it shouldn’t, like a safe code or an open passport. Avoid sharing your name, showing your face, or revealing too much of your surroundings. Be extra careful about reflections that might show you or your personal details. Only show what is absolutely necessary for the task at hand.
• Stick to your inner circle. Create a group in the app and add your friends and family. This ensures your video calls go to people you know — not a random volunteer.
• Don’t use Be My AI to read documents that contain confidential info. Remember, your images and text prompts are sent to OpenAI for processing and generating a response.
• Remember to delete chats you no longer need. Otherwise, they’ll hang around for 30 days.
• If you need to read something personal or confidential, consider apps with real-time reading features like Envision, Seeing AI, or Lookout. These apps process data locally on your device rather than sending it to the cloud.

Why AI's Biggest Risk Isn't Technical

This article is based on a conversation with Nikesh Arora on the 100th episode of the Threat Vector podcast.

"Most technologists think about technology, not about cybersecurity," Nikesh Arora says. "Cybersecurity is kind of like insurance. Let's go make great things happen, and let's make sure on the way we purchase insurance."

Coming from the CEO of the world's largest cybersecurity company, it's the quiet part said out loud, and it explains why AI deployment is racing ahead while security scrambles to keep up.

Earlier this year, Arora spoke with a CIO entirely focused on AI deployment challenges: building viable products, training models, measuring customer impact. Security never came up once. "If you're still going through the motion, trying to understand, ‘Can I actually make this thing work?’ You're not worried about security," Arora notes. The logic is brutal but consistent: Why secure something that might not even function?

In the Threat Vector podcast’s 100th episode milestone, Arora speaks with host David Moulton:

• Why the gap between innovation and security keeps widening.
• How to read inflection points before they're obvious.
• What separates organizations that prepare from those that scramble.

The Gap That Keeps Growing

The disconnect isn't new. It's the same psychology that makes airport security feel like overhead – necessary friction that slows down what should be seamless. But with AI, the gap is widening at an unprecedented pace.

Consider the infrastructure buildup happening right now. Nvidia has become a $4 trillion company selling chips that can't stay in stock. Hundreds of billions of dollars are flowing into AI-computer infrastructure. Cloud providers are buying out entire methane gas companies to power their data centers.

Yet organizations are treating AI security as something to bolt on later. That same CIO told Arora: "We worked on some stuff ourselves, and we're just jerry-rigging some things to make sure this happens securely."

Arora's response:

Jerry rig, production, and security don't work together as three terms.

Reading Signals Before They're Obvious

Arora has watched enough technology cycles to recognize the pattern. "You start seeing signs early, and then you look around, you don't see enough impact. You say, okay, maybe this is going to be just a passing shower. But you don't realize that over time this thing's getting more and more momentum."

The signs around AI are adding up:

• Individual behavior has shifted.
  Arora went from never talking to ChatGPT or Gemini to conducting 10-15 conversations daily. During a recent Tokyo trip, he used Gemini as his primary navigation tool, asking it to rank sumo wrestling shows for his kids rather than "trying to go read 14 websites and figure out what makes sense."
• The spend is massive and accelerating.
  Not just chips, entire energy infrastructures are being rebuilt to support AI compute needs.
• Consumer and enterprise adoption are both surging.
  From coding assistants to business analysis, use cases are expanding faster than security models can adapt.

"This thing's going to change our life fundamentally," Arora tells Moulton. "We're not seeing it at scale in our customers just yet. That doesn't mean we can sit back and wait."

Arora understands the risks involved in being late to new technology.

You have to not just anticipate where the trend is going. You have to prepare your organization and the resources to get there. Otherwise, the risk is that Silicon Valley will go fund those people who are thinking purely about the new world... and one of them's going to hit. Then you'll be two years behind with no organization, no resources deployed against it.

The Bets That Paid Off

When Arora joined Palo Alto Networks seven and a half years ago, he wrote two words on a piece of paper: cloud and AI. The company was a firewall business. Those two inflection points would require fundamental transformation, and, just as with AI now, being late was not an option.

If you don't get the network transformation right, 80% of our business will falter.

That insight drove a strategic bet on moving from point products to platform thinking, consolidating security tools rather than adding to the sprawl.

The platform approach wasn't about vendor consolidation for its own sake. It was about correlation. Unit 42® data shows that 70% of incidents now span three or more attack surfaces. When attacks move across endpoints, networks, cloud services and applications simultaneously, fragmented security creates gaps that attackers exploit ruthlessly.

Today we have coverage for 80 plus percent of the industry, which means our customers can come talk to us about a myriad of problems, and we can actually cross-correlate across all the different things we do.

With AI deployments touching every part of the technology stack, that cross-correlation becomes essential. Data flows between training environments and production systems. Models access APIs across cloud and on premises infrastructure. Applications consume AI services from multiple providers. Security that can't see and correlate across that entire landscape will miss the threats that matter most.

First Principles Over Tradition

What drives Arora's ability to spot inflection points isn't just pattern recognition, it's his refusal to accept how things have always been done.

His pet peeve: "Somebody said, well, this is how we've traditionally done it." The response reveals his approach: "You use the word traditional. I use the historical context saying, yeah, sure, they used to dig fields with picks and shovels, and now they use tractors."

This thinking drove Palo Alto Networks to reimagine SOC performance. The industry accepted four days as the normal time to detect and remediate security incidents. Arora called that unacceptable. "We need to get it to be real time."

The result was a fundamentally different architecture that analyzes data as it arrives rather than waiting for problems to appear, enabling 1-minute detection and response instead of four days.

Traditionally, SOCs would analyze the problem when the problem appeared. We said forget it. We're going to analyze everything to see if there's a problem. That architecture fundamentally transformed what we do compared to everybody else in the market.

The same first-principles approach needs to apply to AI security. Organizations can't simply extend existing security models and hope they work.

What Comes Next

With ransomware attacks now completing in as little as 25 minutes (100 times faster than just three years ago, according to Unit 42 research) reactive security simply can't keep pace. Organizations need security that thinks and responds at machine speed, built into AI deployments from day one.

"AI has become the biggest inflection point in current technology," Arora observes. Organizations are too busy deploying to worry about security. That's human nature. But it's also the moment when security teams need to stay in lockstep.

The question isn't whether to secure AI, it's whether security will be designed in or bolted on. The former takes strategic thinking now. The latter takes crisis management later.

Our job at Palo Alto and our industry is to make sure as they go build these experimental ideas into real production capability that we're staying in lockstep with them and saying, ‘Oh, by the way, here's something that can secure what you just built in a way that is not gonna get you into trouble.’

Listen to the full conversation between Nikesh Arora and David Moulton, senior director of thought leadership for Cortex^® and Unit 42, on the 100th episode of Threat Vector.

The post When Security Becomes an Afterthought appeared first on Palo Alto Networks Blog.

Technologies for creating fake video and voice messages are accessible to anyone these days, and scammers are busy mastering the art of deepfakes. No one is immune to the threat — modern neural networks can clone a person’s voice from just three to five seconds of audio, and create highly convincing videos from a couple of photos. We’ve previously discussed how to distinguish a real photo or video from a fake and trace its origin to when it was taken or generated. Now let’s take a look at how attackers create and use deepfakes in real time, how to spot a fake without forensic tools, and how to protect yourself and loved ones from “clone attacks”.

How deepfakes are made

Scammers gather source material for deepfakes from open sources: webinars, public videos on social networks and channels, and online speeches. Sometimes they simply call identity theft targets and keep them on the line for as long as possible to collect data for maximum-quality voice cloning. And hacking the messaging account of someone who loves voice and video messages is the ultimate jackpot for scammers. With access to video recordings and voice messages, they can generate realistic fakes that 95% of folks are unable to tell apart from real messages from friends or colleagues.

The tools for creating deepfakes vary widely, from simple Telegram bots to professional generators like HeyGen and ElevenLabs. Scammers use deepfakes together with social engineering: for example, they might first simulate a messenger app call that appears to drop out constantly, then send a pre-generated video message of fairly low quality, blaming it on the supposedly poor connection.

In most cases, the message is about some kind of emergency in which the deepfake victim requires immediate help. Naturally the “friend in need” is desperate for money, but, as luck would have it, they’ve no access to an ATM, or have lost their wallet, and the bad connection rules out an online transfer. The solution is, of course, to send the money not directly to the “friend”, but to a fake account, phone number, or cryptowallet.

Such scams often involve pre-generated videos, but of late real-time deepfake streaming services have come into play. Among other things, these allow users to substitute their own face in a chat-roulette or video call.

How to recognize a deepfake

If you see a familiar face on the screen together with a recognizable voice but are asked unusual questions, chances are it’s a deepfake scam. Fortunately, there are certain visual, auditory, and behavioral signs that can help even non-techies to spot a fake.

Visual signs of a deepfake

Lighting and shadow issues. Deepfakes often ignore the physics of light: the direction of shadows on the face and in the background may not match, and glares on the skin may look unnatural or not be there at all. Or the person in the video may be half-turned toward the window, but their face is lit by studio lighting. This example will be familiar to participants in video conferences, where substituted background images can appear extremely unnatural.

Blurred or floating facial features. Pay attention to the hairline: deepfakes often show blurring, flickering, or unnatural color transitions along this area. These artifacts are caused by flaws in the algorithm for superimposing the cloned face onto the original.

Unnaturally blinking or “dead” eyes. A person blinks on average 10 to 20 times per minute. Some deepfakes blink too rarely, others too often. Eyelid movements can be too abrupt, and sometimes blinking is out of sync, with one eye not matching the other. “Glassy” or “dead-eye” stares are also characteristic of deepfakes. And sometimes a pupil (usually just the one) may twitch randomly due to a neural network hallucination.

When analyzing a static image such as a photograph, it’s also a good idea to zoom in on the eyes and compare the reflections on the irises — in real photos they’ll be identical; in deepfakes — often not.

Lip-syncing issues. Even top-quality deepfakes trip up when it comes to synchronizing speech with lip movements. A delay of just a hundred milliseconds is noticeable to the naked eye. It’s often possible to observe an irregular lip shape when pronouncing the sounds m, f, or t. All of these are telltale signs of an AI-modeled face.

Static or blurred background. In generated videos, the background often looks unrealistic: it might be too blurry; its elements may not interact with the on-screen face; or sometimes the image behind the person remains motionless even when the camera moves.

Odd facial expressions. Deepfakes do a poor job of imitating emotion: facial expressions may not change in line with the conversation; smiles look frozen, and the fine wrinkles and folds that appear in real faces when expressing emotion are absent — the fake looks botoxed.

Auditory signs of a deepfake

Early AI generators modeled speech from small, monotonous phonemes, and when the intonation changed, there was an audible shift in pitch, making it easy to recognize a synthesized voice. Although today’s technology has advanced far beyond this, there are other signs that still give away generated voices.

Wooden or electronic tone. If the voice sounds unusually flat, without natural intonation variations, or there’s a vaguely electronic quality to it, there’s a high probability you’re talking to a deepfake. Real speech contains many variations in tone and natural imperfections.

No breathing sounds. Humans take micropauses and breathe in between phrases — especially in long sentences, not to mention small coughs and sniffs. Synthetic voices often lack these nuances, or place them unnaturally.

Robotic speech or sudden breaks. The voice may abruptly cut off, words may sound “glued” together, and the stress and intonation may not be what you’re used to hearing from your friend or colleague.

Lack of… shibboleths in speech. Pay attention to speech patterns (such as accent or phrases) that are typical of the person in real life but are poorly imitated (if at all) by the deepfake.

To mask visual and auditory artifacts, scammers often simulate poor connectivity by sending a noisy video or audio message. A low-quality video stream or media file is the first red flag indicating that checks are needed of the person at the other end.

Behavioral signs of a deepfake

Analyzing the movements and behavioral nuances of the caller is perhaps still the most reliable way to spot a deepfake in real time.

Can’t turn their head. During the video call, ask the person to turn their head so they’re looking completely to the side. Most deepfakes are created using portrait photos and videos, so a sideways turn will cause the image to float, distort, or even break up. AI startup Metaphysic.ai — creators of viral Tom Cruise deepfakes — confirm that head rotation is the most reliable deepfake test at present.

Unnatural gestures. Ask the on-screen person to perform a spontaneous action: wave their hand in front of their face; scratch their nose; take a sip from a cup; cover their eyes with their hands; or point to something in the room. Deepfakes have trouble handling impromptu gestures — hands may pass ghostlike through objects or the face, or fingers may appear distorted, or move unnaturally.

Screen sharing. If the conversation is work-related, ask your chat partner to share their screen and show an on-topic file or document. Without access to your real-life colleague’s device, this will be virtually impossible to fake.

Can’t answer tricky questions. Ask something that only the genuine article could know, for example: “What meeting do we have at work tomorrow?”, “Where did I get this scar?”, “Where did we go on vacation two years ago?” A scammer won’t be able to answer questions if the answers aren’t present in the hacked chats or publicly available sources.

Don’t know the codeword. Agree with friends and family on a secret word or phrase for emergency use to confirm identity. If a panicked relative asks you to urgently transfer money, ask them for the family codeword. A flesh-and-blood relation will reel it off; a deepfake-armed fraudster won’t.

What to do if you encounter a deepfake

If you’ve even the slightest suspicion that what you’re talking to isn’t a real human but a deepfake, follow our tips below.

• End the chat and call back. The surest check is to end the video call and connect with the person through another channel: call or text their regular phone, or message them in another app. If your opposite number is unhappy about this, pretend the connection dropped out.

• Don’t be pressured into sending money. A favorite trick is to create a false sense of urgency. “Mom, I need money right now, I’ve had an accident”; “I don’t have time to explain”; “If you don’t send it in ten minutes, I’m done for!” A real person usually won’t mind waiting a few extra minutes while you double-check the information.
• Tell your friend or colleague they’ve been hacked. If a call or message from someone in your contacts comes from a new number or an unfamiliar account, it’s not unusual — attackers often create fake profiles or use temporary numbers, and this is yet another red flag. But if you get a deepfake call from a contact in a messenger app or your address book, inform them immediately that their account has been hacked — and do it via another communication channel. This will help them take steps to regain access to their account (see our detailed instructions for Telegram and WhatsApp), and to minimize potential damage to other contacts, for example, by posting about the hack.

How to stop your own face getting deepfaked

• Restrict public access to your photos and videos. Hide your social media profiles from strangers, limit your friends list to real people, and delete videos with your voice and face from public access.
• Don’t give suspicious apps access to your smartphone camera or microphone. Scammers can collect biometric data through fake apps disguised as games or utilities. To stop such programs from getting on your devices, use a proven all-in-one security solution.
• Use passkeys, unique passwords, and two-factor authentication (2FA) where possible. Even if scammers do create a deepfake with your face, 2FA will make it much harder to access your accounts and use them to send deepfakes. A cross-platform password manager with support for passkeys and 2FA codes can help out here.
• Teach friends and family how to spot deepfakes. Elderly relatives, young children, and anyone new to technology are the most vulnerable targets. Educate them about scams, show them examples of deepfakes, and practice using a family codeword.
• Use content analyzers. While there’s no silver bullet against deepfakes, there are services that can identify AI-generated content with high accuracy. For graphics, these include Undetectable AI and Illuminarty; for video — Deepware; and for all types of deepfakes — Sensity AI and Hive Moderation.
• Keep a cool head. Scammers apply psychological pressure to hurry victims into acting rashly. Remember the golden rule: if a call, video, or voice message from anyone you know rouses even the slightest suspicion, end the conversation and make contact through another channel.

To protect yourself and loved ones from being scammed, learn more about how scammers deploy deepfakes:

• How phishers and scammers use AI
• How fraudsters bypass customer identity verification using deepfakes
• Watch the (verified) birdie, or new ways to recognize fakes
• Is it the boss — or is it a fraudster? Scams disguised as urgent orders from top brass
• Don’t believe your ears: voice deepfakes

John talked about how we’d attack, here’s how you can defend against those attacks. Grab the slides here: https://blackhillsinformationsecurity.shootproof.com/gallery/6843799/

The post PODCAST: Attack Tactics Part 2 appeared first on Black Hills Information Security, Inc..

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