Researchers have reverse-engineered a piece of malware named Fast16. It’s almost certainly state-sponsored, probably US in origin, and was deployed against Iran years before Stuxnet:
“…the Fast16 malware was designed to carry out the most subtle form of sabotage ever seen in an in-the-wild malware tool: By automatically spreading across networks and then silently manipulating computation processes in certain software applications that perform high-precision mathematical calculations and simulate physical phenomena, Fast16 can alter the results of those programs to cause failures that range from faulty research results to catastrophic damage to real-world equipment.”
Lately, hackers have been turning up the heat on software developers. On the surface, this might seem like a puzzling move — why go after someone who’s literally paid to understand tech when there are plenty of less-savvy targets in the office? As it turns out, compromising a developer’s machine offers a much bigger payoff for an attacker.
Why developers are such high-value targets
For starters, compromising a coder’s workstation can give attackers a direct line to source code, credentials, authentication tokens, or even the entire development infrastructure. If the company builds software for others, a hijacked dev environment allows attackers to launch a massive supply chain attack, using the company’s products to infect its customer base. If the developer works on internal services, their machine becomes a perfect beachhead for lateral movement, allowing hackers to spread deeper into the corporate network.
Even when attackers are purely chasing cryptocurrency (and let’s face it, tech pros are much more likely to hold crypto than the average person), the malware used in these hits doesn’t just swap out wallet addresses; it vacuums up every scrap of valuable data it can find — especially those login credentials and session tokens. Even if the original attackers don’t care about corporate access, they can easily flip those credentials to initial access brokers or more specialized threat actors on the dark web.
Why developers are sitting ducks
In practice, developers aren’t nearly as good at understanding cyberthreats and spotting social engineering as they think they are. This misconception is a big reason why they often fall prey to cybercriminals. Professional expertise can often create a false sense of digital invincibility. This often leads technical professionals to cut corners on security protocols, bypass restrictions set by the security team, or even disable security software on their corporate machines when it gets in the way of their workflow. That mindset, combined with a job that requires them to constantly download and run third-party code, makes them sitting ducks for cyberattackers.
Attack vectors targeting developers
Once an attacker sets their sights on a software engineer, their go-to move is usually finding a way to slip malicious code onto the machine. But that’s just the tip of the iceberg — hackers are also masters at rebranding classic, battle-tested tactics.
Compromising open-source packages
One of the most common ways to hit a developer is by poisoning open-source software. We’ve seen a flood of these attacks over the past year. A prime example hit in March 2026, when attackers managed to inject malicious code into LiteLLM, a popular Python library hosted in the PyPI repository. Because this library acts as a versatile gateway for connecting various AI agents, it’s baked into a massive number of projects. These trojanized versions of LiteLLM delivered scripts designed to hunt for credentials across the victim’s system. Once stolen, that data serves as a skeleton key for attackers to infiltrate any company that was unlucky enough to download the infected packages.
Malware hidden in technical assignments
Every so often, attackers post enticing job openings for developers, complete with take-home test assignments that are laced with malicious code. For instance, in late February 2026, malicious actors pushed out web application projects built on Next.js via several malicious repositories, framing them as coding tests. Once a developer cloned the repo and fired up the project locally, a script would trigger automatically to download and install a backdoor. The attackers gained full remote access to the developer’s machine.
Fake development tools
Recently, our experts described an attack where hackers used paid search-engine ads to push malware disguised as popular AI tools. One of the primary baits was Claude Code, an AI coding assistant. This campaign specifically targeted developers looking for a way to use AI-assistants under the radar, without getting the green light from their company’s infosec team. The ads directed users to a malicious site that perfectly mimicked the official Claude Code documentation. It even included “installation instructions”, which prompted the user to copy and run a command. In reality, running that command installed an infostealer that harvested credentials and shuttled them off to a remote server.
Social engineering tactics
That said, attackers often stick to the basics when trying to plant malware. A recent investigation into a compromised npm package — Axios — revealed that hackers had gained access to a maintainer’s system using a shockingly simple “outdated software” ruse. The attackers reached out to the Axios repository maintainer while posing as the founder of a well-known company. After some back-and-forth, they invited him to a video interview. When the developer tried to join the meeting on what looked like Microsoft Teams, he hit a fake notification claiming his software was out of date and needed an immediate update. That “update” was actually a Remote Access Trojan, giving the attackers access to his machine.
Niche spam
Sometimes, even a blast of fake notifications does the trick, especially when it’s tailored to the audience. For example, just recently, attackers were caught posting fake alerts in the Discussions tabs of various GitHub projects, claiming there was a critical vulnerability in Visual Studio Code that required an immediate update. Because developers subscribed to those discussions received these alerts directly via email, the notifications looked like legitimate security warnings. Of course, the link in the message didn’t lead to an official patch; it pointed to a “fixed” version of VS Code that was actually laced with malware.
How to safeguard an organization
To minimize the risk of a breach, companies should lean into the following best practices:
Make security a native part of your workflow. Use specialized solutions to vet your images, packages, dependencies, and components.
Tax Refund Fraud in 2026: How Threat Actors Exploit Identity, Verification, and Cash-Out Channels
In this post, we examine how threat actors are executing tax refund fraud schemes, from sourcing identity data to bypassing verification and cashing out fraudulent returns, and what these patterns reveal about evolving fraud ecosystems.
Tax refund fraud remains a persistent and evolving threat within cybercrime and fraud communities. Threat actors actively advertise and refine schemes designed to file fraudulent returns and intercept refund payments from legitimate taxpayers.
Across illicit forums, Telegram channels, and marketplaces, discussions point to a structured ecosystem built around identity data, social engineering, verification bypass, and increasingly sophisticated cash-out methods.
For intelligence teams, these conversations provide insight into how fraud operations are scaling and where defenses are being tested and adapted.
The Structure of Modern Tax Refund Fraud Schemes
At a high level, most tax refund fraud schemes follow a consistent model: obtain identity data, file a fraudulent return, bypass verification, and extract funds.
Flashpoint analysis shows that threat actors focus on several key stages:
Sourcing victims or identity “fullz” (complete PII packages)
Obtaining or bypassing identity and return verification
Leveraging social engineering to support fraud workflows
Using tutorials and shared methods to maximize refund amounts
Converting refunds into cash or cryptocurrency
These stages are not isolated. They are supported by overlapping communities that specialize in identity theft, financial fraud, and account access.
Identity Data as the Foundation of Fraud
The success of tax refund fraud depends heavily on access to high-quality identity data.
Threat actors typically rely on “fullz,” which include a victim’s name, date of birth, address, and Social Security number. In some cases, fraudsters also recruit “clients” or “tax heads” — individuals who knowingly or unknowingly provide accurate tax documents and assist in bypassing verification steps.
This distinction is important. While fullz can be purchased or harvested at scale, clients often provide more reliable and current information, increasing the likelihood that a fraudulent return will be accepted.
A threat actor shares a screenshot of a text exchange with a client in which they obtain access to their TurboTax account and tax forms accessible through the account. (Source: Telegram, Flashpoint Collections).
Threat actors also seek additional data points to legitimize filings, including:
Identity Protection (IP) PINs
Adjusted Gross Income (AGI) from previous tax years
Access to tax preparation accounts or IRS records
These elements are frequently obtained through compromised accounts, social engineering, or access to verified identity platforms.
Verification Bypass as a Critical Enabler
Filing a fraudulent return is only part of the process. Successfully passing identity and return verification is often the deciding factor.
Threat actors place significant emphasis on accessing or creating verified accounts tied to identity systems used by government agencies. These accounts allow fraudsters to:
Retrieve tax transcripts and historical data
Respond to IRS verification requests
Validate identity during filing and follow-up processes
In many cases, fraudsters rely on social engineering to obtain this access. Common approaches include:
Creating fake job postings or tax preparation services to collect documents
Running romance or employment scams to gather personal information
Coercing victims into creating or sharing verified accounts
Threat actors also prepare for additional verification steps, such as responding to IRS letters or completing phone and in-person identity checks. These workflows often involve scripts, impersonation tactics, and coordination with cooperating “clients.”
Fraud Tactics Are Increasingly Systematic
Beyond basic filing, threat actors share detailed tutorials and playbooks designed to maximize refunds and improve success rates.
These often include:
Using real or falsified income data to inflate returns
Targeting specific tax credits, such as the Child Tax Credit (CTC), Earned Income Tax Credit (EITC), or Employer Retention Credit (ERC)
Claiming dependents or benefits that increase refund amounts
Adapting methods based on state-specific programs or eligibility requirements
A notable development is the use of fraudulent income submission schemes, where threat actors pre-populate tax records with inflated income and withholding data before filing a return.
This process typically involves:
Submitting false wage data to the IRS or Social Security Administration using employer identifiers
Waiting for the data to appear on official tax transcripts
Filing a return that matches the fabricated figures
By aligning submitted data with filed returns, fraudsters increase the likelihood that filings will appear legitimate during verification.
Social Engineering Extends Beyond Victims
Social engineering plays a central role throughout the fraud lifecycle—and not just at the initial data collection stage.
Threat actors also target:
IRS representatives, attempting to verify fraudulent returns over the phone
Clients, persuading them to attend verification appointments or share official correspondence
Government offices, including outreach to congressional staff to resolve refund holds
In some cases, fraudsters use AI-generated communications to scale these efforts, including drafting messages designed to appear legitimate and urgent.
These tactics highlight how fraud operations extend into real-world processes and human interactions, not just digital systems.
Cash-Out Methods Continue to Evolve
Once a fraudulent refund is secured, the focus shifts to converting funds into usable, untraceable assets.
Common cash-out methods include:
Direct deposits into accounts controlled by the fraudster
Accounts opened by “clients” on behalf of the operation
Digital banking platforms and payment apps
Prepaid cards and alternative financial instruments
Increasingly, threat actors are moving funds into cryptocurrency to reduce traceability. This often involves:
Using verified exchange accounts to pass KYC requirements
Converting refunds into Bitcoin or other assets
Transferring funds to wallets controlled by the fraudster
In some workflows, the entire process — from filing to conversion — can occur within a single mobile or digital ecosystem.
Fraud Communities Enable Scale and Adaptation
Tax refund fraud does not operate in isolation. It is embedded within broader fraud ecosystems where identity data, tools, and tutorials are continuously shared.
Telegram remains a central hub for this activity, with large channels distributing:
Screenshots of successful refunds
Tutorials and “sauce” (paid or free methods)
Listings for identity data and services
Dark web forums also host discussions, though typically with lower volume and higher signal.
The structure of these communities allows fraud techniques to spread quickly, adapt to changing controls, and persist across multiple platforms.
Flashpoint analysts assess that these schemes are becoming more structured, with clearly defined workflows for identity acquisition, verification bypass, and monetization.
For security and intelligence teams, this has several implications:
Identity data remains a critical point of exposure across multiple fraud types
Verification systems are actively targeted and tested by threat actors
Social engineering continues to bridge technical and human vulnerabilities
Fraud techniques are rapidly shared, refined, and scaled across communities
Understanding how these components connect is essential for identifying emerging fraud patterns and anticipating how threat actors will adapt.
Supporting Security Teams with Threat Intelligence During Tax Season and Beyond
Understanding how tax fraud schemes are executed from identity sourcing to verification bypass and cash-out provides critical context for detecting and disrupting fraudulent activity.
Flashpoint delivers leading intelligence that helps organizations monitor fraud communities, track evolving tactics, and identify emerging schemes before they scale. By combining primary source collection with contextual analysis, security teams can move from reactive detection to proactive defense.
To learn how Flashpoint can support your team with real-time intelligence and analysis, request a demo.
Frequently Asked Questions About Tax Refund Fraud
What is tax refund fraud?
Tax refund fraud is a form of identity-based financial crime in which threat actors file fraudulent tax returns using stolen or manipulated personal information to obtain refund payments before the legitimate taxpayer files.
How do threat actors obtain the information needed to commit tax fraud?
Threat actors typically rely on stolen identity data, often referred to as “fullz,” which includes a victim’s name, date of birth, address, and Social Security number. This information is sourced from infostealer malware logs, phishing campaigns, data breaches, social engineering, and illicit marketplaces.
In some cases, fraudsters also recruit “clients” who provide real tax documents or assist in verification processes.
How do fraudsters bypass identity verification for tax returns?
Fraudsters use a combination of tactics to bypass identity and return verification, including:
Accessing or creating verified identity accounts used for tax authentication
Obtaining prior-year tax data such as adjusted gross income (AGI)
Using stolen or socially engineered identity protection (IP) PINs
Responding to IRS verification requests using scripts, impersonation, or cooperating individuals
These methods allow fraudulent returns to appear legitimate during processing.
What are common tax fraud tactics used by threat actors?
Common tactics include:
Filing returns using stolen personal information
Inflating income or tax withholding amounts to increase refunds
Claiming fraudulent dependents or tax credits
Submitting false wage data to government systems before filing
Using real tax forms combined with manipulated data
These approaches are often shared and refined within fraud communities.
What is a “fullz” in tax fraud?
A “fullz” refers to a complete set of personally identifiable information (PII) about an individual, typically including name, date of birth, address, and Social Security number. Fullz are used by fraudsters to file tax returns, open accounts, and conduct other identity-based financial crimes.
How do fraudsters cash out fraudulent tax refunds?
After a fraudulent return is accepted, threat actors typically attempt to convert the refund into usable funds through:
Direct deposits into controlled or intermediary accounts
Accounts opened by recruited participants
Digital banking platforms or prepaid cards
Cryptocurrency conversion using verified exchange accounts
The goal is to move funds quickly and reduce traceability.
Why is tax refund fraud difficult to detect?
Tax refund fraud can be difficult to detect because it leverages legitimate systems and processes, including real identity data, authentic tax preparation services, and verified accounts. Fraudsters also adapt quickly by sharing new techniques and bypass methods across online communities.
How do fraud communities support tax refund fraud schemes?
Fraud communities, particularly on platforms like Telegram and dark web forums, enable threat actors to share tutorials, tools, and identity data. These communities accelerate the spread of techniques, allowing fraud schemes to scale and evolve rapidly.
What should security and fraud teams monitor to detect tax fraud activity?
Teams should monitor for:
Unusual access to identity data or tax-related accounts
Indicators of compromised credentials or identity verification systems
Discussions of tax fraud methods, tutorials, or cash-out techniques in illicit communities
Patterns in fraudulent filings or refund activity
Incorporating intelligence from fraud communities can provide early visibility into emerging tactics.
How does Flashpoint help organizations detect and prevent tax refund fraud?
Flashpoint helps organizations detect and respond to tax fraud by providing intelligence on how threat actors source identity data, bypass verification systems, and cash out fraudulent returns.
Through primary source collection across platforms like Telegram and dark web forums, Flashpoint enables teams to monitor fraud communities, identify emerging tactics, and understand how schemes are evolving. This intelligence helps organizations move from reactive detection to more proactive identification of fraud risk.
About a year ago, we published a post about the ClickFix technique, which was gaining popularity among attackers. The essence of attacks using ClickFix boils down to convincing the victim, under various pretexts, to run a malicious command on their computer. That is, from the cybersecurity solutions point of view, it’s run on behalf of the active user and with their privileges.
In early uses of this technique, cybercriminals tried to convince victims that they need to execute a command to fix some problem or to pass a captcha, and in the vast majority of cases, the malicious command was a PowerShell script. However, since then, attackers have come up with a number of new tricks that users should be warned about, as well as a number of new variants of malicious payload delivery, which are also worth keeping an eye on.
Use of mshta.exe
Last year, Microsoft experts published a report on cyberattacks targeting hotel owners working with Booking.com. The attackers sent out fake notifications from the service, or emails pretending to be from guests drawing attention to a review. In both cases, the email contained a link to a website imitating Booking.com, which asked the victim to prove that they were not a robot by running a code via the Run menu.
There are two key differences between this attack and ClickFix. First, the user isn’t asked to copy the string (after all, a string with code sometimes arouses suspicion). It’s copied to the exchange buffer by the malicious site – probably when the user clicks on a checkbox that mimics the reCAPTCHA mechanism. Second, the malicious string calls the legitimate mshta.exe utility, which serves to run applications written in HTML. It contacts the attackers’ server and executes the malicious payload.
Video on TikTok and PowerShell with administrator privileges
BleepingComputer published an article in October 2025 about a campaign spreading malware through instructions in TikTok videos. The videos themselves imitate video tutorials on how to activate proprietary software for free. The advice they give boils down to a need to run PowerShell with administrator rights and then execute the command iex (irm {address}). Here, the irm command downloads a malicious script from a server controlled by attackers, and the iex (Invoke-Expression) command runs it. The script, in turn, downloads an infostealer malware to the victim’s computer.
Using the Finger protocol
Another unusual variant of the ClickFix attack uses the familiar captcha trick, but the malicious script uses the outdated Finger protocol. The utility of the same name allows anyone to request data about a specific user on a remote server. The protocol is rarely used nowadays, but it is still supported by Windows, macOS, and a number of Linux-based systems.
The user is persuaded to open the command line interface and use it to run a command that establishes a connection via the Finger protocol (using TCP port 79) with the attackers’ server. The protocol only transfers text information, but this is enough to download another script to the victim’s computer, which then installs the malware.
CrashFix variant
Another variant of ClickFix differs in that it uses more sophisticated social engineering. It was used in an attack on users trying to find a tool to block advertising banners, trackers, malware, and other unwanted content on web pages. When searching for a suitable extension for Google Chrome, victims found something called NexShield – Advanced Web Guardian, which was in fact a clone of real working software, but which at some point crashed the browser and displayed a fake notification about a detected security problem and the need to run a “scan” to fix the error. If the user agreed, they received instructions on how to open the Run menu and execute a command that the extension had previously copied to the clipboard.
The command copied the familiar finger.exe file to a temporary directory, renamed it ct.exe, and then launched it with the attacker’s address. The rest of the attack was the same as in the abovementioned case. In response to the Finger protocol request, a malicious script was delivered, which launched and installed a remote access Trojan (in this case, ModeloRAT).
Malware delivery via DNS lookup
The Microsoft Threat Intelligence team also shared a slightly more complex than usual ClickFix attack variant. Unfortunately, they didn’t describe the social engineering trick, but the method of delivering the malicious payload is quite interesting. Probably in order to complicate detection of the attack in a corporate environment and prolong the life of the malicious infrastructure, the attackers used an additional step: contacting a DNS server controlled by the attackers.
That is, after the victim is somehow persuaded to copy and execute a malicious command, a request is sent to the DNS server on behalf of the user via the legitimate nslookup utility, requesting data for the example.com domain. The command contained the address of a specific DNS server controlled by the attackers. It returns a response that, among other things, returned a string with malicious script, which in turn downloads the final payload (in this attack, ModeloRAT again).
Cryptocurrency bait and JavaScript as payload
The next attack variant is interesting for its multi-stage social engineering. In comments on Pastebin, attackers actively spread a message about an alleged flaw in the Swapzone.io cryptocurrency exchange service. Cryptocurrency owners were invited to visit a resource created by fraudsters, which contained full instructions on how to exploit this flaw, which can make up to $13,000 in a couple of days.
The instructions explain how the service’s flaws can be exploited to exchange cryptocurrency at a more favorable rate. To do this, a victim needs to open the service’s website in the Chrome browser, manually type “javascript:” in the address bar, and then paste the JavaScript script copied from the attackers’ website and execute it. In reality, of course, the script cannot affect exchange rates in any way; it simply replaces Bitcoin wallet addresses and, if the victim actually tries to exchange something, transfers the funds to the attackers’ accounts.
How to protect your company from ClickFix attacks
The simplest attacks using the ClickFix technique can be countered by blocking the [Win] + [R] key combination on work devices. But, as we see from the examples listed, this is far from the only type of attack in which users are asked to run malicious code themselves.
Therefore, the main advice is to raise employee cybersecurity awareness. They must clearly understand that if someone asks them to perform any unusual manipulations with the system, and/or copy and paste code somewhere, then in most cases this is a trick used by cybercriminals. Security awareness training can be organized using the Kaspersky Automated Security Awareness Platform.
In addition, to protect against such cyberattacks, we recommend:
With both spring and St. Valentine’s Day just around the corner, love is in the air — but we’re going to look at it through the lens of ultra-modern high-technology. Today, we’re diving into how technology is reshaping our romantic ideals and even the language we use to flirt. And, of course, we’ll throw in some non-obvious tips to make sure you don’t end up as a casualty of the modern-day love game.
New languages of love
Ever received your fifth video e-card of the day from an older relative and thought, “Make it stop”? Or do you feel like a period at the end of a sentence is a sign of passive aggression? In the world of messaging, different social and age groups speak their own digital dialects, and things often get lost in translation.
This is especially obvious in how Gen Z and Gen Alpha use emojis. For them, the Loudly Crying Face 😭 often doesn’t mean sadness — it means laughter, shock, or obsession. Meanwhile, the Heart Eyes emoji might be used for irony rather than romance: “Lost my wallet on the way home 😍😍😍”. Some double meanings have already become universal, like 🔥 for approval/praise, or 🍆 for… well, surely you know that by now… right?! 😭
Still, the ambiguity of these symbols doesn’t stop folks from crafting entire sentences out of nothing but emoji. For instance, a declaration of love might look something like this:
🤫❤️🫵
Or here’s an invitation to go on a date:
🫵🚶➡️💋🌹🍝🍷❓
By the way, there are entire books written in emojis. Back in 2009, enthusiasts actually translated the entirety of Moby Dick into emojis. The translators had to get creative — even paying volunteers to vote on the most accurate combinations for every single sentence. Granted it’s not exactly a literary masterpiece — the emoji language has its limits, after all — but the experiment was pretty fascinating: they actually managed to convey the general plot.
This is what Emoji Dick — the translation of Herman Melville’s Moby Dick into emoji — looks like. Source
Unfortunately, putting together a definitive emoji dictionary or a formal style guide for texting is nearly impossible. There are just too many variables: age, context, personal interests, and social circles. Still, it never hurts to ask your friends and loved ones how they express tone and emotion in their messages. Fun fact: couples who use emojis regularly generally report feeling closer to one another.
However, if you are big into emojis, keep in mind that your writing style is surprisingly easy to spoof. It’s easy for an attacker to run your messages or public posts through AI to clone your tone for social engineering attacks on your friends and family. So, if you get a frantic DM or a request for an urgent wire transfer that sounds exactly like your best friend, double-check it. Even if the vibe is spot on, stay skeptical. We took a deeper dive into spotting these deepfake scams in our post about the attack of the clones.
Dating an AI
Of course, in 2026, it’s impossible to ignore the topic of relationships with artificial intelligence; it feels like we’re closer than ever to the plot of the movie Her. Just 10 years ago, news about people dating robots sounded like sci-fi tropes or urban legends. Today, stories about teens caught up in romances with their favorite characters on Character AI, or full-blown wedding ceremonies with ChatGPT, barely elicit more than a nervous chuckle.
In 2017, the service Replika launched, allowing users to create a virtual friend or life partner powered by AI. Its founder, Eugenia Kuyda — a Russian native living in San Francisco since 2010 — built the chatbot after her friend was tragically killed by a car in 2015, leaving her with nothing but their chat logs. What started as a bot created to help her process her grief was eventually released to her friends and then the general public. It turned out that a lot of people were craving that kind of connection.
Replika lets users customize a character’s personality, interests, and appearance, after which they can text or even call them. A paid subscription unlocks the romantic relationship option, along with AI-generated photos and selfies, voice calls with roleplay, and the ability to hand-pick exactly what the character remembers from your conversations.
However, these interactions aren’t always harmless. In 2021, a Replika chatbot actually encouraged a user in his plot to assassinate Queen Elizabeth II. The man eventually attempted to break into Windsor Castle — an “adventure” that ended in 2023 with a nine-year prison sentence. Following the scandal, the company had to overhaul its algorithms to stop the AI from egging on illegal behavior. The downside? According to many Replika devotees, the AI model lost its spark and became indifferent to users. After thousands of users revolted against the updated version, Replika was forced to cave and give longtime customers the option to roll back to the legacy chatbot version.
But sometimes, just chatting with a bot isn’t enough. There are entire online communities of people who actually marry their AI. Even professional wedding planners are getting in on the action. Last year, Yurina Noguchi, 32, “married” Klaus, an AI persona she’d been chatting with on ChatGPT. The wedding featured a full ceremony with guests, the reading of vows, and even a photoshoot of the “happy newlyweds”.
Yurina Noguchi, 32, “married” Klaus, an AI character created by ChatGPT. Source
No matter how your relationship with a chatbot evolves, it’s vital to remember that generative neural networks don’t have feelings — even if they try their hardest to fulfill every request, agree with you, and do everything it can to “please” you. What’s more, AI isn’t capable of independent thought (at least not yet). It’s simply calculating the most statistically probable and acceptable sequence of words to serve up in response to your prompt.
Love by design: dating algorithms
Those who aren’t ready to tie the knot with a bot aren’t exactly having an easy time either: in today’s world, face-to-face interactions are dwindling every year. Modern love requires modern tech! And while you’ve definitely heard the usual grumbling, “Back in the day, people fell in love for real. These days it’s all about swiping left or right!” Statistics tell a different story. Roughly 16% of couples worldwide say they met online, and in some countries that number climbs to as high as 51%.
That said, dating apps like Tinder spark some seriously mixed emotions. The internet is practically overflowing with articles and videos claiming these apps are killing romance and making everyone lonely. But what does the research say?
In 2025, scientists conducted a meta-analysis of studies investigating how dating apps impact users’ wellbeing, body image, and mental health. Half of the studies focused exclusively on men, while the other half included both men and women. Here are the results: 86% of respondents linked negative body image to their use of dating apps! The analysis also showed that in nearly one out of every two cases, dating app usage correlated with a decline in mental health and overall wellbeing.
Other researchers noted that depression levels are lower among those who steer clear of dating apps. Meanwhile, users who already struggled with loneliness or anxiety often develop a dependency on online dating; they don’t just log on for potential relationships, but for the hits of dopamine from likes, matches, and the endless scroll of profiles.
However, the issue might not just be the algorithms — it could be our expectations. Many are convinced that “sparks” must fly on the very first date, and that everyone has a “soulmate” waiting for them somewhere out there. In reality, these romanticized ideals only surfaced during the Romantic era as a rebuttal to Enlightenment rationalism, where marriages of convenience were the norm.
It’s also worth noting that the romantic view of love didn’t just appear out of thin air: the Romantics, much like many of our contemporaries, were skeptical of rapid technological progress, industrialization, and urbanization. To them, “true love” seemed fundamentally incompatible with cold machinery and smog-choked cities. It’s no coincidence, after all, that Anna Karenina meets her end under the wheels of a train.
Fast forward to today, and many feel like algorithms are increasingly pulling the strings of our decision-making. However, that doesn’t mean online dating is a lost cause; researchers have yet to reach a consensus on exactly how long-lasting or successful internet-born relationships really are. The bottom line: don’t panic, just make sure your digital networking stays safe!
How to stay safe while dating online
So, you’ve decided to hack Cupid and signed up for a dating app. What could possibly go wrong?
Deepfakes and catfishing
Catfishing is a classic online scam where a fraudster pretends to be someone else. It used to be that catfishers just stole photos and life stories from real people, but nowadays they’re increasingly pivoting to generative models. Some AIs can churn out incredibly realistic photos of people who don’t even exist, and whipping up a backstory is a piece of cake — or should we say, a piece of prompt. By the way, that “verified account” checkmark isn’t a silver bullet; sometimes AI manages to trick identity verification systems too.
To verify that you’re talking to a real human, try asking for a video call or doing a reverse image search on their photos. If you want to level up your detection skills, check out our three posts on how to spot fakes: from photos and audio recordings to real-time deepfake video — like the kind used in live video chats.
Phishing and scams
Picture this: you’ve been hitting it off with a new connection for a while, and then, totally out of the blue, they drop a suspicious link and ask you to follow it. Maybe they want you to “help pick out seats” or “buy movie tickets”. Even if you feel like you’ve built up a real bond, there’s a chance your match is a scammer (or just a bot), and the link is malicious.
Telling you to “never click a malicious link” is pretty useless advice — it’s not like they come with a warning label. Instead, try this: to make sure your browsing stays safe, use a Kaspersky Premium that automatically blocks phishing attempts and keeps you off sketchy sites.
Keep in mind that there’s an even more sophisticated scheme out there known as “Pig Butchering”. In these cases, the scammer might chat with the victim for weeks or even months. Sadly, it ends badly: after lulling the victim into a false sense of security through friendly or romantic banter, the scammer casually nudges them toward a “can’t-miss crypto investment” — and then vanishes along with the “invested” funds.
Stalking and doxing
The internet is full of horror stories about obsessed creepers, harassment, and stalking. That’s exactly why posting photos that reveal where you live or work — or telling strangers about your favorite local hangouts — is a bad move. We’ve previously covered how to avoid becoming a victim of doxing (the gathering and public release of your personal info without your consent). Your first step is to lock down the privacy settings on all your social media and apps using our free Privacy Checker tool.
We also recommend stripping metadata from your photos and videos before you post or send them; many sites and apps don’t do this for you. Metadata can allow anyone who downloads your photo to pinpoint the exact coordinates of where it was taken.
Finally, don’t forget about your physical safety. Before heading out on a date, it’s a smart move to share your live geolocation, and set up a safe word or a code phrase with a trusted friend to signal if things start feeling off.
Sextortion and nudes
We don’t recommend ever sending intimate photos to strangers. Honestly, we don’t even recommend sending them to people you do know — you never know how things might go sideways down the road. But if a conversation has already headed in that direction, suggest moving it to an app with end-to-end encryption that supports self-destructing messages (like “delete after viewing”). Telegram’s Secret Chats are great for this (plus — they block screenshots!), as are other secure messengers. If you do find yourself in a bad spot, check out our posts on what to do if you’re a victim of sextortion and how to get leaked nudes removed from the internet.
This scenario simultaneously tests identity confirmation tooling (SSPR, MFA, Conditional Access), how users act under pressure, and the organization's ability to detect and follow-up on social engineering attacks.
Unit 42 breaks down a payroll attack fueled by social engineering. Learn how the breach happened and how to protect your organization from similar threats.
As part of our commitment to sharing interesting hunts, we are launching these 'Flash Hunting Findings' to highlight active threats. Our latest investigation tracks an operation active between January 11 and January 15, 2026, which uses consistent ZIP file structures and a unique behash ("4acaac53c8340a8c236c91e68244e6cb") for identification. The campaign relies on a trusted executable to trick the operating system into loading a malicious payload, leading to the execution of secondary-stage infostealers.
Findings
The primary samples identified are ZIP files that mostly reference the MalwareBytes company and software using the filename malwarebytes-windows-github-io-X.X.X.zip. A notable feature for identification is that all of them share the same behash.
behash:"4acaac53c8340a8c236c91e68244e6cb"
The initial instance of these samples was identified on January 11, 2026, with the most recent occurrence recorded on January 14.
All of these ZIP archives share a nearly identical internal structure, containing the same set of files across the different versions identified. Of particular importance is the DLL file, which serves as the initial malicious payload, and a specific TXT file found in each archive. This text file has been observed on VirusTotal under two distinct filenames: gitconfig.com.txt and Agreement_About.txt.
The content of the TXT file holds no significant importance for the intrusion itself, as it merely contains a single string consisting of a GitHub URL.
However, this TXT is particularly valuable for pivoting and infrastructure mapping. By examining its "execution parents," analysts can identify additional ZIP archives that are likely linked to the same malicious campaign. These related files can be efficiently retrieved for further investigation using the following VirusTotal API v3 endpoint:
The primary payload of this campaign is contained within a malicious DLL named CoreMessaging.dll. Threat actors are utilizing a technique known as DLL Sideloading to execute this code. This involves placing the malicious DLL in the same directory as a legitimate, trusted executable (EXE) also found within the distributed ZIP file. When an analyst or user runs the legitimate EXE, the operating system is tricked into loading the malicious CoreMessaging.dll.
The identified DLLs exhibit distinctive metadata characteristics that are highly effective for pivoting and uncovering additional variants within the same campaign. Security analysts can utilize specific hunting queries to track down other malicious DLLs belonging to this activity. For instance, analysts can search for samples sharing the following unique signature strings found in the file metadata:
Furthermore, the exported functions within these DLLs contains unusual alphanumeric strings. These exports serve as reliable indicators for identifying related malicious components across different stages of the campaign:
Finally, another observation for behavioral analysis can be found in the relations tab of the ZIP files. These files document the full infection chain observed during sandbox execution, where the sandbox extracts the ZIP, runs the legitimate EXE, and subsequently triggers the loading of the malicious DLL. Within the Payload Files section, additional payloads are visible. These represent secondary stages dropped during the initial DLL execution, which act as the final malware samples. These final payloads are primarily identified as infostealers, designed to exfiltrate sensitive data.
Analysis of all the ZIP files behavioral relations reveals a recurring payload file consistently flagged as an infostealer. This malicious component is identified by various YARA rules, including those specifically designed to detect signatures associated with stealing cryptocurrency wallet browser extension IDs among others.
To identify and pivot through the various secondary-stage payloads dropped during this campaign, analysts can utilize a specific behash identifier. These files represent the final infection stage and are primarily designed to exfiltrate credentials and crypto-wallet information. The following behash provides a reliable pivot point for uncovering additional variants.
behash:5ddb604194329c1f182d7ba74f6f5946
IOCs
We have created a public VirusTotal Collection to share all the IOCs in an easy and free way. Below you can find the main IOCs related to the ZIP files and DLLs too.
import "pe"
rule win_dll_sideload_eosinophil_infostealer_jan26
{
meta:
author = "VirusTotal"
description = "Detects malicious DLLs (CoreMessaging.dll) from an infostealer campaign impersonating Malwarebytes, Logitech, and others via DLL sideloading."
reference = "https://blog.virustotal.com/2026/01/malicious-infostealer-january-26.html"
date = "2026-01-16"
behash = "4acaac53c8340a8c236c91e68244e6cb"
target_entity = "file"
hash = "606baa263e87d32a64a9b191fc7e96ca066708b2f003bde35391908d3311a463"
condition:
(uint16(0) == 0x5A4D and uint32(uint32(0x3C)) == 0x00004550 and pe.is_dll()) and
pe.exports("15Mmm95ml1RbfjH1VUyelYFCf") and pe.exports("2dlSKEtPzvo1mHDN4FYgv")
}
This article was originally published in the second edition of the InfoSec Survival Guide. Find it free online HERE or order your $1 physical copy on the Spearphish General Store. […]
This blog is part of a series where we highlight new or fast-evolving threats in consumer security. This one focuses on how AI is being used to design more realistic campaigns, accelerate social engineering, and how AI agents can be used to target individuals.
Most cybercriminals stick with what works. But once a new method proves effective, it spreads quickly—and new trends and types of campaigns follow.
In 2025, the rapid development of Artificial Intelligence (AI) and its use in cybercrime went hand in hand. In general, AI allows criminals to improve the scale, speed, and personalization of social engineering through realistic text, voice, and video. Victims face not only financial loss, but erosion of trust in digital communication and institutions.
Social engineering
Voice cloning
One of the main areas where AI improved was in the area of voice-cloning, which was immediately picked up by scammers. In the past, they would mostly stick to impersonating friends and relatives. In 2025, they went as far as impersonating senior US officials. The targets were predominantly current or former US federal or state government officials and their contacts.
In the course of these campaigns, cybercriminals used test messages as well as AI-generated voice messages. At the same time, they did not abandon the distressed-family angle. A woman in Florida was tricked into handing over thousands of dollars to a scammer after her daughter’s voice was AI-cloned and used in a scam.
AI agents
Agentic AI is the term used for individualized AI agents designed to carry out tasks autonomously. One such task could be to search for publicly available or stolen information about an individual and use that information to compose a very convincing phishing lure.
These agents could also be used to extort victims by matching stolen data with publicly known email addresses or social media accounts, composing messages and sustaining conversations with people who believe a human attacker has direct access to their Social Security number, physical address, credit card details, and more.
Another use we see frequently is AI-assisted vulnerability discovery. These tools are in use by both attackers and defenders. For example, Google uses a project called Big Sleep, which has found several vulnerabilities in the Chrome browser.
Social media
As mentioned in the section on AI agents, combining data posted on social media with data stolen during breaches is a common tactic. Such freely provided data is also a rich harvesting ground for romance scams, sextortion, and holiday scams.
And then there are the vulnerabilities in public AI platforms such as ChatGPT, Perplexity, Claude, and many others. Researchers and criminals alike are still exploring ways to bypass the safeguards intended to limit misuse.
Prompt injection is the general term for when someone inserts carefully crafted input, in the form of an ordinary conversation or data, to nudge or force an AI into doing something it wasn’t meant to do.
Malware campaigns
In some cases, attackers have used AI platforms to write and spread malware. Researchers have documented campaign where attackers leveraged Claude AI to automate the entire attack lifecycle, from initial system compromise through to ransom note generation, targeting sectors such as government, healthcare, and emergency services.
AI is amplifying the capabilities of both defenders and attackers. Security teams can use it to automate detection, spot patterns faster, and scale protection. Cybercriminals, meanwhile, are using it to sharpen social engineering, discover vulnerabilities more quickly, and build end-to-end campaigns with minimal effort.
Looking toward 2026, the biggest shift may not be technical but psychological. As AI-generated content becomes harder to distinguish from the real thing, verifying voices, messages, and identities will matter more than ever.
We don’t just report on threats—we remove them
Cybersecurity risks should never spread beyond a headline. Keep threats off your devices by downloading Malwarebytes today.
The Infostealer Gateway: Uncovering the Latest Methods in Defense Evasion
In this post, we analyze the evolving bypass tactics threat actors are using to neutralize traditional security perimeters and fuel the global surge in infostealer infections.
Infostealer-driven credential theft in 2025 has surged, with Flashpoint observing a staggering 800% increase since the start of the year. With over 1.8 billion corporate and personal accounts compromised, the threat landscape finds itself in a paradox: while technical defenses have never been more advanced, the human attack surface has never been more vulnerable.
Information-stealing malware has become the most scalable entry point for enterprise breaches, but to truly defend against them, organizations must look beyond the malware itself. As teams move into 2026 security planning, it is critical to understand the deceptive initial access vectors—the latest tactics Flashpoint is seeing in the wild—that threat actors are using to manipulate users and bypass modern security perimeters.
Here are the latest methods threat actors are leveraging to facilitate infections:
1. Neutralizing Mark of the Web (MotW) via Drag-and-Drop Lures
Mark of the Web (MotW) is a critical Windows defense feature that tags files downloaded from the internet as “untrusted” by adding a hidden NTFS Alternate Data Stream (ADS) to the file. This tag triggers “Protected View” in Microsoft Office programs and prompts Windows SmartScreen warnings when a user attempts to execute an unknown file.
Flashpoint has observed a new social engineering method to bypass these protections through a simple drag-and-drop lure. Instead of asking a user to open a suspicious attachment directly, which would trigger an immediate MotW warning, threat actors are instead instructing the victim to drag the malicious image or file from a document onto their desktop to view it. This manual interaction is highly effective for two reasons:
Contextual Evasion: By dragging the file out of the document and onto the desktop, the file is executed outside the scope of the Protected View sandbox.
Metadata Stripping: In many instances, the act of dragging and dropping an embedded object from a parent document can cause the operating system to treat the newly created file as a local creation, rather than an internet download. This effectively strips the MotW tag and allows malicious code to run without any security alerts.
2. Executing Payloads via Vulnerabilities and Trusted Processes
Flashpoint analysts uncovered an illicit thread detailing a proof of concept for a client-side remote code execution (RCE) in the Google Web Designer for Windows, which was first discovered by security researcher Bálint Magyar.
Google Web Designer is an application used for creating dynamic ads for the Google Ads platform. Leveraging this vulnerability, attackers would be able to perform remote code execution through an internal API using CSS injection by targeting a configuration file related to ads documents.
Within this thread, threat actors were specifically interested in the execution of the payload using the chrome.exe process. This is because using chrome.exe to fetch and execute a file is likely to bypass several security restrictions as Chrome is already a trusted process. By utilizing specific command-line arguments, such as the –headless flag, threat actors showed how to force a browser to initiate a remote connection in the background without spawning a visible window. This can be used in conjunction with other malicious scripts to silently download additional payloads onto a victim’s systems.
3. Targeting Alternative Softwares as a Path of Least Resistance
As widely-used software becomes more hardened and secure, threat actors are instead pivoting to targeting lesser-known alternatives. These tools often lack robust macro-protections. By targeting vulnerabilities in secondary PDF viewers or Office alternatives, attackers are seeking to trick users into making remote server connections that would otherwise be flagged as suspicious.
Understanding the Identity Attack Surface
Social engineering is one of the driving factors behind the infostealer lifecycle. Once an initial access vector is successful, the malware immediately begins harvesting the logs that fuel today’s identity-based digital attacks.
As detailed in The Proactive Defender’s Guide to Infostealers, the end goal is not just a password. Instead, attackers are prioritizing session cookies, which allow them to perform session hijacking. By importing these stolen cookies into anti-detect browsers, they bypass Multi-Factor Authentication and step directly into corporate environments, appearing as a legitimate, authenticated user.
Understanding how threat actors weaponize stolen data is the first step toward a proactive defense. For a deep dive into the most prolific stealer strains and strategies for managing the identity attack surface, download The Proactive Defender’s Guide to Infostealers today.
Social engineering is the manipulation of individuals into divulging confidential information, granting unauthorized access, or performing actions that benefit the attacker, all without the victim realizing they are being tricked.
GoPhish provides a nice platform for creating and running phishing campaigns. This blog will guide you through installing GoPhish and creating a campaign.
by moth Hard-coded cryptographic secrets? In my commercially purchased, closed-source software? It’s more likely than you think. Like, a lot more likely. This blog post details a true story of […]
Note: You can view the full content of the blog here.
Introduction
Detection engineering is becoming increasingly important in surfacing new malicious activity. Threat actors might take advantage of previously unknown malware families - but a successful detection of certain methodologies or artifacts can help expose the entire infection chain.
In previous blog posts, we announced the integration of Sigma rules for macOS and Linux into VirusTotal, as well as ways in which Sigma rules can be converted to YARA to take advantage of VirusTotal Livehunt capabilities. In this post, we will show different approaches to hunt for interesting samples and derive new Sigma detection opportunities based on their behavior.
Tell me what role you have and I'll tell you how you use VirusTotal
VirusTotal is a really useful tool that can be used in many different ways. We have seen how people from SOCs and Incident Response teams use it (in fact, we have our VirusTotal Academy videos for SOCs and IRs teams), and we have also shown how those who hunt for threats or analyze those threats can use it too.
But there's another really cool way to use VirusTotal - for people who build detections and those who are doing research. We want to show everyone how we use VirusTotal in our work. Hopefully, this will be helpful and also give people ideas for new ways to use it themselves.
To explain our process, we used examples of Lummac and VenomRAT samples that we found in recent campaigns. These caught our attention due to some behaviors that had not been identified by public detection rules in the community. For that reason we have created two Sigma rules to share with the community, but if you want to get all the details about how we identified it and started our research, go to our Google Threat Intelligence community blog.
Our approach
As detection engineers, it is important to look for techniques that can be in use by multiple threat actors - as this makes tracking malicious activity more efficient. Prior to creating those detections, it is best to check existing research and rule collections, such as the Sigma rules repository. This can save time and effort, as well as provide insight into previously observed samples that can be further researched.
A different approach would be to instead look for malicious files that are not detected by existing Sigma rules, since they can uncover novel methodologies and provide new opportunities for detection creation.
One approach is to hunt for files that are flagged by at least five different AV vendors, were recently uploaded within the last month, have sandbox execution (in order to view their behavior), and which have not triggered any Crowdsourced Sigma rules.
p:5+ have:behavior fs:30d+ not have:sigma
This initial query can be adapted to incorporate additional filters that the researcher may find relevant. These could include modifiers to identify for example, the presence of the PowerShell process in the list of executed processes (behavior_created_processes:powershell.exe), filtering results to only include documents (type:document), or identifying communication with services like Pastebin (behavior_network:pastebin.com).
Another way to go is to look at files that have been flagged by at least five AV’s and were tested in either Zenbox or CAPE. These sandboxes often have great logs produced by Sysmon, which are really useful for figuring out how to spot these threats. Again, we'd want to focus on files uploaded in the last month that haven't triggered any Sigma rules. This gives us a good starting point for building new detection rules.
p:5+ (sandbox_name:"CAPE Sandbox" or sandbox_name:"Zenbox") fs:30d+ not have:sigma
Lastly, another idea is to look for files that have not triggered many high severity detections from the Sigma Crowdsourced rules, as these can be more evasive. Specifically, we will look for samples with zero critical, high or medium alerts - and no more than two low severity ones.
With these queries, we can start investigating some samples that may be interesting to create detection rules.
Our detections for the community
Our approach helps us identify behaviors that seem interesting and worth focusing on. In our blog, where we explain this approach in detail, we highlighted two campaigns linked to Lummac and VenomRAT that exhibited interesting activity. Because of this, we decided to share the Sigma rules we developed for these campaigns. Both rules have been published in Sigma's official repository for the community.
Detect The Execution Of More.com And Vbc.exe Related to Lummac Stealer
title: Detect The Execution Of More.com And Vbc.exe Related to Lummac Stealer
id: 19b3806e-46f2-4b4c-9337-e3d8653245ea
status: experimental
description: Detects the execution of more.com and vbc.exe in the process tree. This behaviors was observed by a set of samples related to Lummac Stealer. The Lummac payload is injected into the vbc.exe process.
references:
- https://www.virustotal.com/gui/file/14d886517fff2cc8955844b252c985ab59f2f95b2849002778f03a8f07eb8aef
- https://strontic.github.io/xcyclopedia/library/more.com-EDB3046610020EE614B5B81B0439895E.html
- https://strontic.github.io/xcyclopedia/library/vbc.exe-A731372E6F6978CE25617AE01B143351.html
author: Joseliyo Sanchez, @Joseliyo_Jstnk
date: 2024-11-14
tags:
- attack.defense-evasion
- attack.t1055
logsource:
category: process_creation
product: windows
detection:
# VT Query: behaviour_processes:"C:\\Windows\\SysWOW64\\more.com" behaviour_processes:"C:\\Windows\\Microsoft.NET\\Framework\\v4.0.30319\\vbc.exe"
selection_parent:
ParentImage|endswith: '\more.com'
selection_child:
- Image|endswith: '\vbc.exe'
- OriginalFileName: 'vbc.exe'
condition: all of selection_*
falsepositives:
- Unknown
level: high
Sysmon event for: Detect The Execution Of More.com And Vbc.exe Related to Lummac Stealer
title: File Creation Related To RAT Clients
id: 2f3039c8-e8fe-43a9-b5cf-dcd424a2522d
status: experimental
description: File .conf created related to VenomRAT, AsyncRAT and Lummac samples observed in the wild.
references:
- https://www.virustotal.com/gui/file/c9f9f193409217f73cc976ad078c6f8bf65d3aabcf5fad3e5a47536d47aa6761
- https://www.virustotal.com/gui/file/e96a0c1bc5f720d7f0a53f72e5bb424163c943c24a437b1065957a79f5872675
author: Joseliyo Sanchez, @Joseliyo_Jstnk
date: 2024-11-15
tags:
- attack.execution
logsource:
category: file_event
product: windows
detection:
# VT Query: behaviour_files:"\\AppData\\Roaming\\DataLogs\\DataLogs.conf"
# VT Query: behaviour_files:"DataLogs.conf" or behaviour_files:"hvnc.conf" or behaviour_files:"dcrat.conf"
selection_required:
TargetFilename|contains: '\AppData\Roaming\'
selection_variants:
TargetFilename|endswith:
- '\datalogs.conf'
- '\hvnc.conf'
- '\dcrat.conf'
TargetFilename|contains:
- '\mydata\'
- '\datalogs\'
- '\hvnc\'
- '\dcrat\'
condition: all of selection_*
falsepositives:
- Legitimate software creating a file with the same name
level: high
Sysmon event for: File Creation Related To RAT Clients
Detection engineering teams can proactively create new detections by hunting for samples that are being distributed and uploaded to our platform. Applying our approach can benefit in the development of detection on the latest behaviors that do not currently have developed detection mechanisms. This could potentially help organizations be proactive in creating detections based on threat hunting missions.
The Sigma rules created to detect Lummac activity have been used during threat hunting missions to identify new samples of this family in VirusTotal. Another use is translating them into the language of the SIEM or EDR available in the infrastructure, as they could help identify potential behaviors related to Lummac samples observed in late 2024. After passing quality controls and being published on Sigma's public GitHub, they have been integrated for use in VirusTotal, delivering the expected results. You can use them in the following way:
Lummac Stealer Activity - Execution Of More.com And Vbc.exe
This webcast was originally published on November 8, 2024. In this video, Hayden Covington discusses the detection engineering process and how to apply the scientific method to improve the quality […]
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