Michael suspects personal information he submitted to rent application platforms was leaked online. And analysis shows millions of documents may also be at risk
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Michael* has spent the past two months trying to get his digital identity back.
The 47-year-old Victorian schoolteacher was in the process of moving to a new town and applying for rental properties online. Around this time – and unbeknown to him – his mobile phone number was transferred to someone else.
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© Composite: Getty Images
© Composite: Getty Images
© Composite: Getty Images
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”.
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.
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.
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.
Look at the reflections and glares in the eyes in the real photo (left) and the generated image (right) — although similar, specular highlights in the eyes in the deepfake are different. Source
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.
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.
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.
Ask a deepfake to wave a hand in front of its face, and the hand may appear to dissolve. Source
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.
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.
To protect yourself and loved ones from being scammed, learn more about how scammers deploy deepfakes:
In this post we analyze the multi-vector threat landscape of the 2026 Winter Olympics, examining how the Games’ dispersed geographic footprint and high digital complexity create unique potential for cyber sabotage and physical disruptions.
Table Of ContentsThe Milano-Cortina 2026 Winter Olympics represent a historic milestone as the first Games co-hosted by two major cities. However, the event’s expansive geographic footprint—covering 22,000 square kilometers across northern Italy—presents a complex security environment. From the metropolitan centers of Milan to the alpine peaks of Cortina d’Ampezzo, security forces are contending with a multi-vector threat landscape.
The geographically dispersed nature of the Milano-Cortina 2026 Winter Games also creates unique physical security challenges. Because venues are spread across thousands of square kilometers of the Alps, securing transit corridors and ensuring rapid emergency response across different Italian regions—including Lombardy, Veneto, and Trentino—is an incredible logistical hurdle. New tunnels, increased train services, and extended bus routes have been welcomed but create new potential targets for physical disruption by threat actors or protestors.
Flashpoint has not identified any terrorist or extremist threats to the Winter Olympic Games. However, lone threat actors in support of international terrorist organizations or domestic violence extremists remain a persistent threat due to the large number of attendees expected and the media attention that this event will attract.
Authorities in northern Italy are investigating a series of sabotage attacks on the national railway network that coincided with the opening of the 2026 Winter Olympic Games. The coordinated incidents—which included arson at a track switch, severed electrical cables, and the discovery of a rudimentary explosive device—caused delays of over two hours and temporarily disabled the vital transport hub of Bologna.
Flashpoint analysts identified several protests targeting the 2026 Winter Olympics:
On February 7, a massive protest of approximately 10,000 people near the Olympic Village in Milan descended into violence as a peaceful march against the Winter Games ended in clashes with Italian police. While the majority of demonstrators initially focused on the environmental destruction caused by Olympic infrastructure, a smaller group of masked protestors engaged security forces with flares, stones, and firecrackers.
The Milano-Cortina 2026 Winter Olympics will be among the most digitally complex global events, making it a prime target for cyberattacks. The greatest risks stem from familiar tactics such as phishing, spoofed websites, and business email compromise, which exploit human trust rather than technical flaws. With billions of viewers and a vast network of cloud services, vendors, and connected systems, the games create an expansive attack surface under intense operational pressure.
Italy blocked a series of cyberattacks targeting its foreign ministry offices, including one in Washington, as well as Winter Olympics websites and hotels in Cortina d’Ampezzo, with officials attributing the attempts to Russian sources. Foreign Minister Antonio Tajani confirmed the attacks were prevented just days before the Games’ official opening, which began with curling matches on February 4.
Past Olympic Games show a clear pattern of heightened cyber activity, including phishing campaigns, distributed denial-of-service (DDoS) attacks, ransomware, and online scams targeting both organizers and the public. A mix of cybercriminals, advanced persistent threats, and hacktivists is expected to exploit the event for financial gain, espionage, or publicity. Experts emphasize that improving security awareness, verifying digital interactions, and strengthening supply chain defenses are critical, as the most damaging incidents often arise from ordinary threats amplified by scale and urgency.
The security success of Milano-Cortina 2026 relies on the integration of real-time intelligence, advanced technological safeguards, and public vigilance. As the Games proceed, the intersection of cyber-sabotage and physical protest remains the most likely source of operational disruption.
To stay safe at this year’s Games, participants should:
While there are no current indications of imminent threats of extreme violence targeting the Milano-Cortina 2026 Winter Olympics, the event’s vast geographic footprint and digital complexity demand constant vigilance. Securing an event that spans 22,000 square kilometers requires more than just a physical presence; it necessitates a multi-faceted approach that bridges the gap between digital and kinetic risks.
To effectively navigate the intersection of cyber-sabotage, civil unrest, and logistical challenges, organizations and attendees must adopt a comprehensive strategy that integrates real-time intelligence with proactive security measures. Download Flashpoint’s Physical Safety Event Checklist to learn more.
The post Cyber and Physical Risks Targeting the 2026 Winter Olympics appeared first on Flashpoint.
VS Code-integrated configuration files are automatically executed in Codespaces when the user opens a repository or pull request.
The post VS Code Configs Expose GitHub Codespaces to Attacks appeared first on SecurityWeek.
Overview This report documents a large-scale phishing campaign in which attackers abused legitimate software-as-a-service (SaaS) platforms to deliver phone-based scam lures that appeared authentic and trustworthy. Rather than spoofing domains or compromising services, the attackers deliberately misused native platform functionality to generate and distribute emails that closely resembled routine service notifications, inheriting the trust, reputation, and authentication posture of well-known SaaS providers. The campaign generated approximately 133,260 phishing emails, impacting 20,049 organizations. It is part of a broader and rapidly escalating trend in which attackers weaponize trusted brands and native cloud workflows to maximize delivery, credibility, and reach. Observed brands […]
The post SaaS Abuse at Scale: Phone-Based Scam Campaign Leveraging Trusted Platforms appeared first on Check Point Blog.
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