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Security Evangelist at Gen

Threat Operations Analyst at Gen

Research

A fake invoice in your email is easy to ignore. A fake invoice inside your order history feels different.&nbsp;Norton customers have reported fake Norton invoices appearing inside the Shop app, the shopping and order-tracking app from Shopify. Public reports suggest the same technique is not limited to Norton. Similar suspicious Shop app notifications have used McAfee, Apple gift cards, iPhones, PayPal-style payment claims and other high-value purchases as bait. The impersonated brand may change, but the mechanics are familiar: make the user believe they have been charged, then give them a phone number to call.&nbsp;That phone number is where the scam really begins.&nbsp;<h4>Why the Shop app makes this more believable&nbsp;</h4>Shop is a legitimate app. People use it to track packages, check receipts and receive order updates. According to Shop’s own documentation, the app can automatically track orders from several sources, including Gmail, Outlook, the email address linked to the user’s Shop account and orders paid through Shop Pay. If Gmail or Outlook is connected, Shop says it scans recent emails for keywords such as “tracking number” and “track your package” to populate deliveries in the Orders tab.&nbsp;

Shop also supports push notifications for delivery updates, payments and favorite products. So the first alert the victim sees may not be an email, an SMS or a calendar invite. It may be a notification from an app they already use for real purchases.&nbsp;That context matters. The scam text is not especially polished. Some of the examples we reviewed contain awkward wording and obvious signs of fraud. But when the message appears inside an order-tracking app, surrounded by real receipts and shipping updates, users may give it more credibility than it deserves.&nbsp;The screenshots reported to us show fake orders under generic seller names such as “My Store.” The receipts claim that a Norton or Norton LifeLock subscription has been activated or renewed, usually for several hundred dollars. The phone number appears in the product description, the receipt body or even the shipping address field.&nbsp;That is not how a normal receipt should look.&nbsp;<h4>The brand is interchangeable&nbsp;</h4>Norton is the example we saw through customer reports. Norton support now warns that scammers may abuse Android and iOS shopping platforms or applications to generate fake order notifications that appear to be legitimate Norton purchases. Those notifications may include fake order confirmations, fake charges, trusted third-party app delivery and fake support phone numbers embedded in order details.&nbsp;Public user reports point to a wider pattern. One Reddit user described receiving a Shop.app message about a transaction they did not recognize, with no follow-up email confirmation, and a message telling them to call support if the order was not placed by them. Another thread includes reports of unrecognized iPhone purchases under “My Store,” suspicious contact emails and users saying they saw no matching charges on their bank cards. Other reports mention fictitious Apple gift card purchases and iPhone orders using the same kind of wording: “If Order Not Placed By You,” followed by a phone number.&nbsp;The choice of brand is part of the social engineering. A fake security subscription charge plays on two fears at once: being billed for something you did not buy, and being left unprotected if you ignore it. Apple products and gift cards create high-value purchase panic. PayPal-style wording makes users worry about payment account abuse. In each case, the fake order is there to push the victim toward a call.&nbsp;<h4>What we know, and what we do not&nbsp;</h4>We have not found any evidence that any of the impersonated brands have been compromised.&nbsp;&nbsp;We also should not claim that Shop or Shopify was breached based on the evidence we have. The exact abuse path still needs confirmation. The fake order may be entering the app through a merchant/order workflow, through email parsing, through account association, or through another legitimate mechanism that scammers have found a way to misuse. Shop’s own documentation shows that orders can populate from multiple sources, so assuming one route would be premature.&nbsp;&nbsp;What we can say is narrower and stronger: scammers are placing fake purchase claims inside shopping app order experiences, where users are used to seeing real receipts and order updates. Norton’s own support page now describes this class of shopping app scam, and public reports show similar lures using other brands and products.&nbsp;&nbsp;That is enough to warn users without overstating the technical evidence.&nbsp;<h4>How the scam works&nbsp;</h4>The user receives a notification or sees a new order in the Shop app. The order looks like a receipt for something expensive: a security subscription, an Apple gift card, an iPhone, a MacBook or another high-value purchase.

The receipt says the charge has been processed, the subscription has renewed, or the order is being prepared. Somewhere in the order details, the scammer inserts a phone number, email address or support instruction.&nbsp;The wording is often clumsy. We have seen phrases like “If Order Not Place By You” and “If Need Help.” Those errors may look obvious when reviewing a screenshot. They are easier to miss when a phone notification tells you that hundreds of dollars have just been charged.&nbsp;If the victim calls, the scam moves off-platform. The person answering may claim to be billing support, Norton support, PayPal support or a cancellation department. The script can vary, but the request usually moves toward credentials, payment data, one-time codes or remote access software.&nbsp;By that point, the fake receipt has done its job. It has created enough urgency for the victim to enter a conversation controlled by the scammer.&nbsp;<h4>Why this is harder for users to judge&nbsp;</h4>Most people understand that email can be spam. They may still fall for phishing, but at least the inbox is a place where scams are expected.&nbsp;Order-tracking apps are different. Their purpose is to collect receipts, shipping updates and purchase information in one place. A notification from that environment can feel as if it has already passed through some layer of trust, even when the content inside the order is fraudulent.&nbsp;We have seen the same logic with calendar invite scams. The message itself is not always convincing, but the delivery channel changes how people read it. A fake invoice in an inbox is one thing. A fake invoice inside a calendar reminder or shopping app receipt can feel closer to a real event.&nbsp;For security products, this also creates a detection problem. The first visible alert may not be an email, SMS or malicious website. It may be a legitimate app notification carrying fraudulent text. The scam still depends on social engineering, but the delivery surface is harder to classify with the usual rules.&nbsp;<h4>What to do if you see one&nbsp;</h4>Do not call the number in the receipt.&nbsp;Check the charge directly. Open your bank or credit card app. Open the official account for the brand being impersonated. If it claims to be Norton, go to your Norton account or official Norton support. If it claims to involve PayPal, open PayPal directly. Do not use the phone number, email address or link inside the suspicious order.&nbsp;If there is no matching charge, treat the receipt as a scam attempt.&nbsp;Report the suspicious store or message in the Shop app if the option is available. Shop’s help pages say suspicious Shop stores can be reported from the store page through the app, and Shopify also tells users to forward phishing messages to its phishing inbox.&nbsp;&nbsp;Norton users can forward suspicious messages to Norton and should verify unexpected charges only through their Norton account or official Norton support channels.&nbsp;&nbsp;Opening the receipt does not automatically mean your device was compromised. The risk increases if you call the number, share information, install software or follow instructions from the person on the phone.&nbsp;<h4>What to do if you have already called&nbsp;</h4>End the call.&nbsp;If you shared payment details, contact your bank or card provider immediately using the number on your card or inside your banking app. If you gave away a password, change it from a different device if possible. Start with your email account, Apple ID or Google account, banking account and any account connected to the fake invoice.&nbsp;If you installed remote access software, disconnect the device from the internet and remove the software. Run a security scan before using the device again for banking, email or shopping.&nbsp;If you gave a one-time code to the caller, treat the related account as compromised. Change the password, review recovery options and check recent login activity.&nbsp;<h4>The lesson&nbsp;</h4>Scammers keep testing new places to put old tricks. For years, fake invoices were mostly an email problem. Then similar subscription notices appeared in calendar invites. Now we are seeing fake receipts inside shopping app order histories and push notification flows.&nbsp;The safest response is to separate the alert from the action. If an app, email, text message or calendar invite says you have been charged, do not use the contact details inside that alert. Go directly to the account or payment provider yourself. Check whether the charge exists. Use official support channels.&nbsp;The fake receipt, the fake support number and the fake emergency are all part of the same controlled environment. The moment you verify the charge somewhere else, the scam loses most of its power.&nbsp;

Scammers are using order-tracking apps to place fake receipts where users expect to see real purchases, then pushing them to call fake support numbers. 

Fake invoices are moving from inboxes to shopping apps 

Threat Researcher at Gen

<h2>Introduction&nbsp;</h2>Infostealers are constantly evolving, and so are the techniques they use to bypass <a href="https://security.googleblog.com/2024/07/improving-security-of-chrome-cookies-on.html" target="_blank" rel="noreferrer noopener">Application-Bound Encryption (ABE)</a>. In recent weeks, Vidar has been among the most actively developed stealers and, apart from <a href="https://x.com/GenThreatLabs/status/2064836539952361976" target="_blank" rel="noreferrer noopener">multiple updates to its string obfuscation</a> and <a href="https://x.com/GenThreatLabs/status/2061462524604649840" target="_blank" rel="noreferrer noopener">a reworked approach to protecting its configuration</a>, it has also introduced a novel technique for bypassing ABE. And while there have been many other changes in Vidar lately, with new versions dropping every week, in this blog post we focus solely on the ABE bypass and its technical aspects.&nbsp; &nbsp;<h2>The ABE Bypass&nbsp;</h2>From a high-level perspective, Vidar’s approach to bypassing ABE is somewhat similar to that of Remus/Lumma, in that both try to extract the <code>v20_master_key</code> directly from the browser’s memory. What sets them apart, however, is how they achieve it. Since we’ve already covered the inner workings of ABE in earlier posts (<a href="https://www.gendigital.com/blog/insights/research/remus-64bit-variant-of-lumma-stealer" target="_blank" rel="noreferrer noopener">Remus</a>, <a href="https://www.gendigital.com/blog/insights/research/voidstealer-abe-bypass" target="_blank" rel="noreferrer noopener">VoidStealer</a>), we don’t revisit them here and focus instead on the bypass itself. For readers interested in the broader background, we encourage checking those earlier posts.&nbsp;&nbsp;In short, the end goal of infostealers is to obtain the <code>v20_master_key</code>, as it alone is sufficient for decrypting any ABE-protected data tied to a specific application. This key can be obtained in one of two ways: either from disk, where it is protected by two layers of <code>CryptProtectData</code> (one applied in the context of the logged-in user and one as <code>NT AUTHORITY\SYSTEM</code>), or from the browser’s memory – the direction Vidar took.&nbsp;In memory, however, the key is kept protected with <code>CryptProtectMemory</code> using the <code>CRYPTPROTECTMEMORY_SAME_PROCESS</code> flag. This ensures that even if an attacker manages to read the encrypted form of the key from memory, they cannot simply decrypt it (they would need to invoke the complementary <code>CryptUnprotectMemory</code> function directly from within the browser process). That leaves attackers with two problems to solve: how to find the encrypted key in memory, and how to get it decrypted.&nbsp;<h2>Locating the v20_master_key in Browser’s Memory&nbsp;</h2>Vidar’s approach to locating the encrypted <code>v20_master_key</code> in memory involves scanning the entire memory of a running browser for a specific pattern. But that first requires a browser process to scan.&nbsp;When the target browser is already running, Vidar reuses the existing process. However, it doesn’t read its memory directly. Instead, it creates a fork of it using <code>NtCreateProcessEx</code> with <code>SectionHandle</code> set to <code>NULL</code>, instructing the kernel to clone the address space of the process specified by the <code>ParentProcess</code> as a copy-on-write mapping. The resulting process has no threads and is never resumed – it exists purely as a static memory snapshot.&nbsp;&nbsp;To perform the fork, Vidar first opens a handle to the target browser via <code>OpenProcess</code> with <code>PROCESS_QUERY_INFORMATION | PROCESS_CREATE_PROCESS | PROCESS_VM_READ</code>, falling back to <code>PROCESS_CREATE_PROCESS</code> if the initial call fails. It then passes this handle as the <code>ParentProcess</code> argument to <code>NtCreateProcessEx</code>, requesting <code>PROCESS_ALL_ACCESS</code> on the resulting child. If the fork fails, Vidar falls back to scanning the live browser process directly.&nbsp;

If no target browser process is found running, Vidar creates a new isolated desktop via <code>CreateDesktopA</code>, assigns it a name of the form <code>v20_%d</code> (where <code>%d</code> is replaced with the browser’s index in Vidar’s configuration), and launches the target browser in there. In the case of Vidar version 2.1, the browser is launched with the command line <code>--no-first-run --disable-gpu about:blank</code>, although the exact command line varies across versions. Since modern browsers use a multi-process architecture, there are typically multiple processes matching a given browser. Vidar enumerates all of them (up to 64) and applies the fork-and-scan procedure to each one independently.&nbsp;

With a handle to the forked process in hand, Vidar proceeds to enumerate its virtual memory regions using <code>NtQueryVirtualMemory</code>. It iterates through the entire browser’s address space, collecting regions that match a specific profile: committed (<code>MEM_COMMIT</code>), private (<code>MEM_PRIVATE</code>), and either readable (<code>PAGE_READONLY</code>) or read-write (<code>PAGE_READWRITE</code>). Vidar records up to 4096 memory regions matching these requirements, along with their base addresses and sizes. The actual pattern scanning is then performed in parallel with up to 64 worker threads, depending on the system.&nbsp;&nbsp;

Each thread scans its assigned regions using <code>NtReadVirtualMemory</code>, searching for a 32-byte pattern. When a match is found, the thread reads 8 bytes at offset +32 from the match, interprets them as a pointer, and validates that it points to committed, private, and readable memory (the expected memory state for the encrypted <code>v20_master_key</code>).&nbsp;&nbsp;

This naturally raises the question of what pattern Vidar searches for. The answer is a 32-byte signature: <code>76 32 30 00 ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? 03 00 00 00 00 01 ?? ?? ??</code>, where <code>??</code> denotes a wildcard (Vidar uses the byte <code>AA</code> as the wildcard marker in its internal representation). Notably, the first four bytes correspond to the ASCII <code>v20\x00</code>, which is a common prefix used for ABE-related data.&nbsp;&nbsp;

We assess with high confidence that this pattern is intended to match a node structure within Chromium’s <code>Encryptor::KeyRing</code>, the structure that holds the keys used by the <code>Encryptor</code> class. Now, this part is admittedly a bit tricky as it relies on several assumptions we made about the internal memory layout, as well as on implementation-specific representations of various C++ standard library types <code>(std::map</code>, <code>std::string</code>, <code>std::vector</code>, <code>std::optional</code>), which is why we frame this as a high-confidence rather than a certainty.&nbsp;&nbsp;Our reasoning is as follows: <code>KeyRing</code> is <a href="https://github.com/chromium/chromium/blob/cf5d502bcaedb22927e3829a18be1ff6d8b6f598/components/os_crypt/async/common/encryptor.h#L132" target="_blank" rel="noreferrer noopener">defined</a> as <code>std::map&lt;/*tag=*/std::string, std::optional&lt;Key&gt;&gt;</code>, and <code>std::map</code> is typically implemented as a red-black tree, where each tree node stores three pointers (left child, parent, and right child) followed by the key-value pair. In this case, the pair is in the form of <code>std::pair&lt;/*tag=*/std::string, std::optional&lt;Key&gt;&gt;</code>, meaning that the <code>std::string</code> and the <code>Key</code> object might be laid out consecutively in memory within each node. For a <code>v20_master_key</code> entry, the tag corresponds to the string <code>v20</code>, which at 4 bytes including the null terminator fits within the small string optimization (SSO) buffer and is therefore stored inline rather than heap-allocated. The <code>Key</code> class that immediately follows <a href="https://github.com/chromium/chromium/blob/cf5d502bcaedb22927e3829a18be1ff6d8b6f598/components/os_crypt/async/common/encryptor.h#L93-L98" target="_blank" rel="noreferrer noopener">contains three member variables</a>: <code>std::optional&lt;mojom::Algorithm&gt; algorithm_</code>, <code>std::vector&lt;uint8_t&gt; key_</code>, and <code>bool encrypted_ = false</code>.&nbsp;Putting it all together and applying it to a concrete example from a browser memory dump, we get the following layout:&nbsp;

Please note that while the exact layout may contain minor inaccuracies on our part, the overall structure should be roughly correct. For easier orientation, the following diagram illustrates how the individual structures are nested within each other:&nbsp;

At this point, it should be clear how the scan pattern maps onto the searched structure. The 32-byte match covers the <code>std::string</code> tag <code>v20\x00</code> and the beginning of the <code>Key</code> object up to and including <code>algorithm_</code>. The pointer at offset +32 from the start of the match then lands precisely on <code>key_.begin</code>, the first member of <code>std::vector&lt;uint8_t&gt;</code> that holds the encrypted <code>v20_master_key</code>.&nbsp;Matches from all threads are collected into a shared buffer and subjected to a majority-voting scheme: candidates consisting mostly of zeroes are discarded, the rest are pairwise-compared, and each is scored by the number of identical copies found. This voting is performed independently for each browser process, producing one best candidate per process.&nbsp;<h2>Decrypting the v20_master_key via APC Injections&nbsp;</h2>For each browser process that produced a match, Vidar now holds a candidate address pointing to a potential encrypted <code>v20_master_key</code>. However, as already noted, since the key is protected with <code>CryptProtectMemory</code> using <code>CRYPTPROTECTMEMORY_SAME_PROCESS</code>, it can only be decrypted by invoking <code>CryptUnprotectMemory</code> from within the browser process itself. Vidar solves this by injecting an Asynchronous Procedure Call (APC) into the live browser process.&nbsp;Vidar supports two APC injection methods, selected based on whether ESET or Bitdefender is detected on the system. If either of these products is present, Vidar uses the first method, which it refers to as “classic” (based on decrypted strings) – it creates a suspended thread in the target browser via <code>CreateRemoteThread</code> (with <code>NtTestAlert</code> as the thread start address and the <code>CREATE_SUSPENDED</code> flag), queues an APC using <code>NtQueueApcThread</code>, resumes the thread, and then waits for it to complete via <code>WaitForSingleObject</code> with a randomized timeout. When the thread resumes, it calls <code>NtTestAlert</code>, which flushes the APC queue and executes the injected routine.&nbsp;

If neither product is detected, it uses the “special” method – it finds an existing thread in the target browser using a combination of <code>CreateToolhelp32Snapshot</code> and <code>Thread32First/Thread32Next</code>, opens it via <code>NtOpenThread</code>, and calls <code>NtQueueApcThreadEx2</code> with the <code>QUEUE_USER_APC_FLAGS_SPECIAL_USER_APC</code> flag. Unlike regular APCs, special user APCs execute immediately. Therefore, the target thread does not need to be in an alertable wait state.&nbsp;

In both cases, the APC routine to be executed is <code>CryptUnprotectMemory</code>, called with the following three arguments: the address of the encrypted <code>v20_master_key</code> candidate, its size (32 bytes), and <code>CRYPTPROTECTMEMORY_SAME_PROCESS</code> for the <code>flags</code> parameter.&nbsp;&nbsp;Since <code>CryptUnprotectMemory</code> decrypts the data in place, once the APC executes, the key is decrypted directly in the live browser's memory. To read it, Vidar creates a second fork of the browser process and reads the key from the same address using <code>NtReadVirtualMemory</code>. It then compares the result against a snapshot of the encrypted key taken from the first fork before the APC call – if the data is unchanged, decryption failed, and Vidar moves on to the next browser process’s candidate.&nbsp;If the data differs, Vidar uses the same second fork to perform an additional verification: it scans the forked process’s memory for the byte sequence <code>v20</code> and, treating each match as a potentially ABE-encrypted entry (cookie, password), attempts AES-256-GCM decryption with the key candidate using <code>BCryptDecrypt</code> (successful GCM tag verification confirms that the key is correct).&nbsp;Whenever the data has changed (indicating that decryption of the key took place), Vidar re-encrypts the key in the browser’s memory by injecting another APC with <code>CryptProtectMemory</code> as the routine, preserving the browser’s state. If no candidate produced a successful decryption (i.e., the data remained unchanged after every APC call), Vidar takes a more aggressive approach: it terminates all browser instances, restarts the browser, and repeats the entire scan-decrypt-verify cycle.&nbsp;By abusing APC injections instead of the more traditional approach of injecting code via <code>NtWriteVirtualMemory</code> that performs the decryption, Vidar trades one suspicious behavior for another. APC injections are arguably less common and may thus slip past some detection systems, though they are still well-known techniques monitored by AV and EDR solutions. But that is the nature of ABE bypasses – each has its own advantages and disadvantages, and that’s exactly why infostealers keep inventing new ones. &nbsp;<h2>Indicator of Compromise (IoC)&nbsp;</h2>459daa809751e73f60fbbe4384a7d1653c36bb06945e4eb3635270924241100a (Vidar 2.1)&nbsp;

A Technical Walkthrough of How Vidar Defeats Application-Bound Encryption

Inside Vidar’s ABE Bypass: From Memory Scanning to APC Injections

You have a summer flight booked. You have seen the headlines about jet fuel shortages, cancelled routes and higher travel costs. Then a text message arrives: your flight has been affected, and you need to click a link to confirm your refund or rebook before the offer expires.&nbsp;That message feels plausible because the situation around it is real. In April, <a href="https://www.iata.org/en/pressroom/2026-releases/2026-04-17-01/" target="_blank" rel="noreferrer noopener">IATA warned</a> that Europe could start seeing flight cancellations by the end of May if jet fuel shortages materialized. Airlines and governments have since been trying to reassure travelers, while also publishing guidance on passenger rights, refunds and re-routing.&nbsp;&nbsp;That is exactly the kind of confusion scammers like. They do not need to cause the disruption, they only need to arrive at the right moment, with the right brand logo and the right sense of urgency.&nbsp;<h4>Why travel disruption works so well as a scam hook&nbsp;</h4>Most scam messages fail because they arrive out of context. A random refund email from an airline you never use is easy to ignore. A refund message from the airline you are flying with next week, during a period of public travel disruption, is different.&nbsp;The scammer’s job is to make you think: “This could be real.”&nbsp;That can happen in several ways. You might receive a fake cancellation email that links to a refund form. You might search for an airline’s customer service number and land on a fake support page. You might post publicly on social media asking an airline for help, then receive a reply from an account that looks official. Or you might get a WhatsApp message that knows your hotel name, travel dates or booking reference.&nbsp;Each version uses the same pressure point: you are worried about losing money, losing your flight or being stranded.&nbsp;Criminals are already using disruption this way. In March, <a href="https://www.theguardian.com/money/2026/mar/15/travel-scam-airline-accounts-fake-refunds-iran-war-flight-disruption" target="_blank" rel="noreferrer noopener">The Guardian reported</a> that fake airline support accounts were targeting travelers affected by Middle East flight disruption. Some victims were looking for help or refunds, then received replies from accounts pretending to represent airlines. The scam moved into direct messages, where victims were asked for details and sent links that led to money being taken instead of refunded.&nbsp;&nbsp;The jet fuel story gives scammers another pretext. A message can claim that your flight was cancelled, your fare changed, your fuel surcharge failed, or your refund must be claimed within a few hours. None of those claims needs to be true to be effective.&nbsp;<h4>The scam may start before the message arrives&nbsp;</h4>A fake travel message becomes much harder to spot when it includes real information.&nbsp;This is the idea behind <a href="https://www.gendigital.com/blog/insights/research/reservation-hijack-scam" target="_blank" rel="noreferrer noopener">Reservation Hijack scams</a>, a form of targeted phishing where cybercriminals use real booking details to make a message look legitimate. In some cases, attackers may use information exposed through breaches or compromised travel systems. In other cases, they collect details from emails, social media posts, previous leaks or fake customer service interactions. Gen has previously described Reservation Hijack scams as scams that use real hotel booking details to make fraudulent payment or verification requests feel authentic.&nbsp;&nbsp;That distinction matters. A scam message does not need your credit card number to fool you. Knowing your name, your destination, your travel dates and your booking platform may be enough.&nbsp;<h4>What these scams can look like&nbsp;</h4>A travel disruption scam usually does not begin with a strange offer. It begins with something boring and administrative. That is part of the trick.&nbsp;You may see messages like:&nbsp;“Your flight has been cancelled due to fuel supply issues. Claim your refund here.”&nbsp;“Your rebooking is pending. Confirm passenger details within two hours.”&nbsp;“Additional airport tax or fuel surcharge is required to keep your booking.”&nbsp;“Your hotel reservation will be cancelled unless payment is verified.”&nbsp;“Due to high call volume, please continue this request on WhatsApp.”&nbsp;The exact wording changes, but the structure is familiar. There is a problem, a deadline and a link. The message may also move you away from the official channel into SMS, WhatsApp, Telegram, direct messages or a phone call. Once that happens, the scammer controls the conversation.&nbsp;There is another detail worth watching: some messages do not ask for money immediately. They start by asking for your booking reference, last name, phone number or one-time password. That can still be dangerous. Those details can help scammers access a booking, impersonate you to customer support, reset an account or make the next message more convincing.&nbsp;<h4>Your real passenger rights can be used against you&nbsp;</h4>If an airline cancels your flight, you may have rights to a refund, re-routing or assistance, depending on where you are travelling and the rules that apply. The European Commission said passengers affected by cancellations continue to have rights to reimbursement, re-routing or return, airport assistance and compensation for last-minute cancellations in relevant circumstances. It also said airlines cannot retroactively add fuel surcharges to already published final ticket prices.&nbsp;&nbsp;That information is useful for travelers, but it is also useful for scammers.&nbsp;A fake refund message becomes more believable when people already know refunds are possible. A fake rebooking link becomes more believable when airlines are genuinely changing schedules. A fake “fuel surcharge” demand becomes more dangerous when fuel prices are in the news, even though legitimate retroactive charges may not be allowed in many cases.&nbsp;This is why the safest route is not to judge the message only by whether the story sounds plausible. During travel disruption, many scam stories will sound plausible. Judge the channel instead.&nbsp;<h4>How to check before you click&nbsp;</h4>Start from the airline or booking platform’s official app or website. Do not use the link in the message. Open the app yourself, type the website address yourself, or use the contact details listed on your original booking confirmation.&nbsp;Check whether the same cancellation, refund or payment request appears inside your account. If the message is real, the airline or hotel should be able to confirm it through an official channel.&nbsp;Be careful with social media support. If an account replies to your public complaint, check whether it is linked from the airline’s official website. Look at the handle carefully. Fake support accounts often copy logos and names, but have small differences in spelling, low follower counts or recently created profiles. The Guardian reported that fraudsters often scan social media for passengers trying to contact legitimate airline accounts and reply to them first, which makes the scam harder to spot.&nbsp;&nbsp;Do not share one-time passwords, payment card details or login credentials with anyone who contacts you unexpectedly. A real airline or hotel does not need your banking OTP to send you a refund.&nbsp;Be especially suspicious of pressure. “You have 30 minutes to claim your refund” is not customer service. It is a tactic.&nbsp;<h4>What to do if you already clicked&nbsp;</h4>If you entered payment details, contact your bank or card provider immediately. Ask whether the transaction can be blocked or reversed, and request a new card if needed.&nbsp;If you shared login details for an airline, hotel or booking platform, change the password from the official site or app. Use a unique password, not one you use elsewhere. Turn on two-factor authentication where it is available.&nbsp;If you shared a one-time password or authentication code, assume the account may have been accessed. Check your booking details, payment methods and recent account activity.&nbsp;Report the scam to the airline, hotel or booking platform being impersonated. If the scam came through social media, report the account as impersonation. If money was taken, report it to the relevant fraud authority in your country.&nbsp;<h4>The real lesson for summer travel&nbsp;</h4>Travel disruption changes how people behave. They search for support numbers in a hurry. They post publicly because customer service queues are long. They accept strange instructions because they are worried about losing a flight or refund.&nbsp;Scammers build around that behavior.&nbsp;The safest assumption is simple: if a message about your flight, hotel or refund creates urgency and gives you a link, pause. Go back to the official source. A real cancellation will still be there when you check through the airline or booking platform. A scammer needs you to act before you do.&nbsp;

Travel disruption gives scammers the one thing they need most: a believable reason to rush you

Your flight was cancelled. Is the refund message real? 

A fake job interview page is easier to trust than a fake ticket shop.In samples reviewed by Gen threat researchers, scammers used branded hiring pages that looked like ordinary recruitment flows: a company logo, a recruiter profile, a 30-minute meeting, and a button to continue with Google or Facebook. There was no attachment to open and no software to install. The credential theft attempt sat inside a step many job applicants already expect: signing in to schedule a call.FIFA is an obvious lure ahead of the 2026 World Cup. On May 22, ESET reported fake FIFA-themed websites that copied ticket and merchandise flows to steal money and personal data. Our researchers later found another FIFA-themed domain, hxxps://fifa-mg[.]shop, which followed a naming pattern not mentioned in that report. Domains like these can be short-lived, often appearing for hours or days before they are blocked, abandoned, or taken down.Our researchers also found a different kind of FIFA lure: hxxps://fifahiring[.]com, a fake hiring page that encouraged users to schedule a call with an alleged FIFA recruiter. When a user tried to register with a personal Gmail address, the page asked for a business email instead. The page was steering victims toward credentials with more value: accounts that could provide access to a company environment.

<h4>The FIFA hiring page did not point to FIFA</h4>The fake page looked believable at first glance.

It used FIFA branding, presented a recruiter identity, and copied parts of the experience users see on legitimate recruiting and scheduling platforms. The recruiter’s name and photo appeared to match a real LinkedIn profile, which made the page feel more credible. For publication, we should keep that person’s identity redacted.The domain did not match FIFA’s real hiring infrastructure. FIFA’s legitimate careers pages are jobs.fifa.com and fifa.pinpointhq.com. The fake page used neither. It also loaded the favicon from the real FIFA recruitment site hosted at pinpointhq.com, giving the page a small visual cue borrowed from the legitimate service.Other signals pointed in the same direction. The domain and certificate were newly created, from May 23. The site used commodity hosting infrastructure, including Vercel and Render, rather than a corporate environment. Those signs are not enough on their own, but they fit the rest of the evidence.The login flow was the clearest part. Credentials were posted to:fifeq2026eqbackeq[.]onrender[.]com/api/loginThe Google sign-in prompt was not a real Google authentication window. It was rendered inside the page itself. The visible controls and links did nothing, and the page did not send the user into a genuine Google sign-in flow. According to the analysis, the only Google asset loaded was the favicon, apparently to make the fake window look familiar.A page can look like it opened a Google login without ever handing control to Google.Researchers also observed hxxps://careers-fifahiring[.]com, which later became inactive, and hxxps://fifajobs[.]com, which showed the same content and was indexed by Google.&nbsp;<h4>The recruitment lure went beyond FIFA</h4>The FIFA page was one version of a wider recruitment phishing pattern. The brand can change. The flow stays familiar: recruiter profile, meeting prompt, business email, fake sign-in.Researchers found similar fake hiring pages abusing other major brands, including Heineken, Hilton, Coca-Cola, Netflix, PepsiCo, Delta,&nbsp;and Spotify. In the Heineken case, the page appeared to use the identity of someone who, according to LinkedIn, really works as a recruiter at the company.

Additional hunting found two other recruitment-themed kits:hxxps://aquent-talent-jobs[.]com hxxps://hays-talent-opportunities[.]comThe Hays-themed version targeted Facebook credentials instead of Google credentials. Researchers assessed that the Aquent and Hays pages shared code, although they differed from the earlier FIFA examples.

This flexibility makes the campaign harder to frame as a single World Cup scam. FIFA provides the timely hook, but the mechanism is recruitment phishing. A scammer can swap the logo, recruiter name, and sign-in provider while keeping the same basic path to the victim’s credentials.<h4>Why fake recruitment pages work</h4>Recruitment gives attackers a clean excuse to ask for things that might otherwise raise suspicion.A candidate expects to click a scheduling link. They expect a recruiter profile. They expect a calendar prompt. In some cases, they may also expect to use a work email, especially for corporate, contractor, or partner-facing roles.The fake login window turns that ordinary flow into credential theft. Users are used to “Continue with Google” and “Continue with Facebook” buttons, but the button itself proves nothing. A genuine authentication flow should take the user to the provider’s domain and controls. A fake page can draw a convincing copy while keeping the victim inside the attacker-controlled site.The business email prompt is also a warning sign. A page that rejects a personal email and asks for a work address may be trying to raise the value of the stolen account.<h4>What job seekers should check before signing in</h4>Start from the company’s official careers page, not from a link in a message, ad, search result, or forwarded post. For FIFA, that means checking jobs.fifa.com or fifa.pinpointhq.com, not trusting a domain because it contains “fifa,” “career,” “hiring,” or “jobs.”Look closely at the domain before entering credentials. Scam pages often add plausible hiring words around a brand name: career, talent, jobs, hiring, portal, or connect. A polished page on a new domain is still a new domain.Treat the sign-in step as the real checkpoint. If a page offers Google or Facebook login, check whether the flow actually opens a real provider-controlled authentication page. A fake window embedded inside the original site, broken controls, inactive links, or a provider logo used only as decoration should stop the process.Use security software that includes web and phishing protection. These tools can block known malicious pages before they load, which is especially useful when scam domains look polished, appear in search results, or are shared through otherwise legitimate channels.<h4>What companies can do</h4>Recruitment pages are public by design, which makes them easy to copy.Security teams should monitor for newly registered domains combining their brand with hiring-related terms. They should also watch for pages that reuse favicons, recruiter photos, job descriptions, and calendar-booking language from legitimate hiring flows.HR and recruiting teams should make the official hiring path easy to verify. Candidates should not have to guess whether a domain is legitimate. A clear careers page, consistent recruiter communication, and warnings about unofficial scheduling links can remove some of the ambiguity attackers rely on.<h4>Conclusion</h4>The fake FIFA hiring page worked because it did not feel unusual. It looked like a normal step in a hiring process.Attackers do not need to invent new behavior when they can copy one people already trust. A branded scheduling page, a recruiter profile, and a familiar sign-in button can be enough to move a victim from interest to credential entry.The decision point is the login. Before entering a work email or password, check the domain, check where the authentication flow really goes, and verify the role through the company’s official careers site. Security software with web and phishing protection adds another layer, especially against short-lived scam pages that may only stay online for hours or days.IOCs:hxxps://fifahiring[.]com hxxps://careers-fifahiring[.]com hxxps://fifajobs[.]com hxxps://fifa-mg[.]shop hxxps://heineken-careers[.]com hxxps://aquent-talent-jobs[.]com hxxps://hays-talent-opportunities[.]com fifeq2026eqbackeq[.]onrender[.]com/api/login

Scammers are copying recruitment and calendar-booking flows to make fake Google and Facebook sign-ins look routine. 

Fake hiring pages abuse FIFA and other major brands to steal work credentials 

Threat Research Team Lead

Since January 2026, we have detected 7.4 million malicious incidents tied to residential proxy traffic, affecting 572,000 users in our telemetry. In this model, the household whose connection is being used is often not the buyer of the traffic. It is the exit point.That is the uncomfortable part of residential proxy networks. A home router, laptop, phone, smart TV, or other always-on device can quietly become part of a commercial network that routes strangers’ internet traffic through ordinary consumer IP addresses. To the websites on the other end, that traffic looks as if it came from a real household, because it did.The technology itself is not inherently malicious. Businesses use residential proxies for market research, ad verification, brand protection, security testing, and services that need to see the web as a real user would. But the same feature that makes these networks useful also makes them attractive for abuse: traffic from a household IP address is hard to block without risking false positives against legitimate users.The problem for consumers is simpler. Many people do not realize their device or connection can be enrolled into this kind of network at all. Some users opt in through pay-for-bandwidth apps. Others are enrolled through “free” VPNs, bundled software development kits inside unrelated apps, or unwanted software that installs proxy components without meaningful consent.Here is how the market works, who pays for it, and what it can cost the household that unknowingly hosts it.<h2>What is a residential proxy</h2>A residential proxy is an internet connection – typically a home laptop, phone, smart TV, or router – that has been enrolled into a third-party network and is being used to route someone else's traffic. To the rest of the internet, that traffic looks like it is coming from an ordinary household IP address assigned by a consumer ISP, not from a datacenter or&nbsp;VPN provider.Residential proxy networks are sold commercially. The model is two-sided: some people pay to tunnel their traffic out through someone else's connection (much like a VPN, except the exit point is a real home), while others get paid to provide those exit endpoints. Some are structured as paid peer-to-peer networks, where any participant's connection can serve as an exit for any other.Buyers pay for access to pools of millions of these "real user" IPs because they are extremely difficult to block, or rather, blocking them carries real consequences: the traffic originates from genuine households, so any block risks shutting out a legitimate user – effectively a false positive.Common buyers include market research firms, price-comparison services, brand-protection vendors, security teams testing their own defenses, and consumer privacy tools. At the abusive end of the market, the same infrastructure used by operators running automated purchasing bots, scrapping campaigns, ad fraud, credential stuffing, phishing and other attacks where hiding the true origin of the traffic matters. Demand is also being shaped by large-scale web scraping, including&nbsp;<a href="https://blog.includesecurity.com/2026/06/the-smart-tv-in-your-livingroom-is-a-node-in-the-aiscraping-economy/">scraping for AI training data</a>. As anti-bot systems become better at identifying traffic from datacenters and known VPN providers, residential IP addresses become more attractive because they look like ordinary users. That does not make every residential proxy user malicious. It does mean consumers may be carrying traffic for businesses and actors they have never heard of.<h2>How consumer devices end up in these networks</h2>There are several ways a home device can become part of a residential proxy network. Some are explicit. Others are easy to miss.The most obvious route is the pay-for-bandwidth model. Apps such as Honeygain, IPRoyal Pawns, and PacketStream pay users to share their internet connection. The user opts in, but they may not understand who will buy access to that connection later, what kind of traffic will pass through it, or what happens if that traffic triggers abuse reports.A second route is the “free” VPN or proxy app. Instead of charging a subscription, the app monetizes the user’s connection by reselling bandwidth. In practice, the user may think they are getting a privacy tool while also becoming infrastructure for other people’s traffic.A third route is through bundled software development kits, or SDKs. A developer adds a third-party component to an otherwise unrelated app, such as a flashlight, wallpaper, game, or utility. The developer gets paid when the app is active. The user sees the host app, not the proxy network behind it.The fourth route is malware or potentially unwanted software. In those cases, the proxy component is installed silently or deceptively, with no meaningful user consent at all.<h2>Why the "consent" is rarely meaningful</h2>Even when consent technically exists, it is often not the kind of consent most people would recognize.The disclosure may be buried in a long terms-of-service document. The wording may be vague, with phrases such as “share your unused bandwidth,” “support the app,” or “join our network.” These phrases do not clearly tell the user that arbitrary third parties may route traffic through the user’s home IP address.There is also the question of downstream use. Users are rarely told who buys access to the network or what those buyers do with it. Their connection could be used for benign market research. It could also be used for scraping, automated account creation, ad fraud, credential stuffing, or phishing infrastructure.The problem gets worse when the proxy component sits inside another app. The host app’s privacy policy may mention a partner SDK by brand name, but that still does not make it clear to a normal user that their device can become a proxy endpoint.Uninstalling the visible app may not always resolve the issue cleanly either, especially when proxy components are installed separately or bundled with other unwanted software.<h2>The risks consumers actually carry</h2>When a household device becomes a residential proxy node, the user takes on risks created by someone else’s traffic.The first risk is attribution. Abusive, fraudulent, or illegal activity routed through the device appears to come from the household’s IP address. ISP abuse notices, platform bans, fraud checks, and law enforcement requests point first to the subscriber, not to the person who bought the proxy traffic.The second risk is performance. Continuous third-party traffic can consume bandwidth, slow the home network, hit data caps, or violate ISP terms of service. The user may only notice that the internet feels slower, a bill is higher, or a service suddenly blocks access.The third risk is security exposure. Proxy clients run persistent background processes and maintain network connections so they can relay traffic. That adds always-on networking software to the device, often with limited visibility for the user.The fourth risk is reputation. A home IP address used for abuse can end up on blocklists used by banks, retailers, streaming services, and online platforms. The result can be account friction, failed logins, extra verification steps, or outright blocking from services the household actually uses.There is also a family angle. A child or another household member may install a free game, VPN, or utility without understanding the consequences. The legal, financial, and reputational risk still lands on the account holder.<h2>What our telemetry shows</h2>Since the beginning of 2026, we have detected 7.4 million malicious traffic incidents associated with residential proxy traffic, affecting 572,000 unique users in our user base.&nbsp; This is not a measurement of the entire residential proxy market. It reflects what we observe in our telemetry: malicious traffic, detections, and related activity affecting protected users. The real footprint is far larger, because residential proxy networks span major consumer platforms and many devices outside our visibility.The activity is global, but it is concentrated in a smaller set of countries. India leads by a wide margin, with roughly 83,000 affected users, followed by Vietnam with 61,000 and Brazil with 39,000. The Philippines (23,000), Poland (18,000), Indonesia (18,000) and Thailand (14,000) form a second tier, with Egypt, Spain, Ukraine, Argentina, Mexico and Italy each accounting for 10,000 to 13,000. The spread across South and Southeast Asia, Latin America, and Eastern and Southern Europe reflects where the host apps that carry this software are most widely installed.That geographic spread likely reflects a combination of app distribution, local install patterns, user appetite for free or pay-for-bandwidth apps, and the economics of bandwidth resale. It should not be read as a simple ranking of where residential proxy abuse originates.The chart below breaks down the threats we observed in this proxy-related traffic. Two categories dominate: phishing and malicious advertising. Behind them sits a long tail of consumer-facing fraud, including e-shop scams, generic scams, financial scams, and fake-tutorial pages, alongside outright malware such as trojans and other malicious traffic.&nbsp;

In the malicious proxy traffic we observed, scams and malware dominate. The important point for consumers is not only that their connection may be used by someone else. It is that, when abuse happens, their household IP address can be the return address.<h2>What consumers can do</h2>Most people will not identify a residential proxy component by name. But there are practical warning signs.Review “free” VPNs, proxy apps, bandwidth-sharing apps, and utilities from unknown publishers. Check whether they mention bandwidth sharing, peer-to-peer networking, traffic routing, or network participation in their terms. If an app offers payment for leaving your device online, assume your connection may be resold unless the provider explains exactly how traffic is vetted and who can buy access.On phones and computers, remove apps you do not recognize or no longer use. On home routers, check the list of connected devices and look for anything unfamiliar. If your ISP sends abuse notices, websites start blocking you, or your home IP address suddenly fails fraud checks, a proxy component is one possible explanation.Security software can also help by detecting unwanted proxy clients, bundled components, and related malicious activity. But this should not be a guessing game for consumers. Software that turns a household connection into a commercial exit node should be named clearly and made easy to remove.<h2>The takeaway&nbsp;</h2>With residential proxiesthe user’s connection becomes an infrastructure for someone else’s business.The issue is not that residential proxy technology exists. It has legitimate uses. The issue is whether people know when their devices are part of it, understand what kind of traffic may pass through their connection, and have a real choice to opt out.The fix is not to pretend consumers will read every EULA. This software should be named clearly, shown plainly, and made easy to remove before a household connection becomes someone else’s infrastructure.

Your home connection could be routing traffic for strangers. Here's how residential proxy networks work, how devices get enrolled and what our telemetry reveals about the risks for consumers.

Who's Really Using Your Home Internet Connection?

<a href="https://github.com/VirusTotal/yara">YARA</a> is a language and tool used to describe and match detection patterns for malware classification. At Avast, we actively contribute to the development of YARA-based tools and libraries. Recently, we open-sourced our <a href="https://engineering.avast.io/yls-first-step-towards-yara-development-environment/">YARA Language Server</a> and now open sourced YaraNG, a reinvention of the YARA scanner.We run YARA at a big scale at Avast. Large rulesets (tens of thousands of rules) scan hundreds of millions of files in a single scan. YARA scanning at scale is not as easy as it sounds. You can look at it from two different points of views:<ul><li>You have an extensive set of files to scan</li><li>You have a large ruleset that you need to scan with</li></ul>Scanning extensive file sets across multiple machines/workers is straightforward, depending on the number of machines used to horizontally scale. A single file per server could be used, but is costly (and a little bit extreme).&nbsp;Scanning with extensive rulesets is trickier. There are dependencies across rules, so just splitting your .yar file into multiple parts might not work right away. The more intertwined your rules are, the harder it is going to be. If you are able to do it, you can duplicate your file set, scan with different rules on each machine and then merge all the results together.And then you might be in the same position as Avast, with a large ruleset and an extensive set of files to scan. In this case, you need to scale your fileset, but also scale your fileset shards and the ruleset shards. This can get expensive rather quickly.&nbsp;At Avast, we decided to scale files but not rules. That left us with large rulesets, consisting of tens of thousands of rules. Using these rulesets, we noticed something most YARA users probably never have – YARA being CPU bound. Our disk controllers were not utilized to the maximum, while CPU was fully utilized.On top of all of this, the YARA language standard is mostly centralized, as there is one reference implementation of the scanner dictating how the language evolves. Language defined by implementation is not a new concept, but having more options to choose your own scanner based on your needs while using the same language is useful. Imagine the same YARA language, but using it in various places, specialized for your needs, not limited by the single implementation of the scanner.We started a little experiment, trying to address everything mentioned above.<h2>Bottlenecks</h2>It all started with the identification of bottlenecks. Where is YARA actually spending the most time during scanning? In order to identify bottlenecks, we ran YARA through <a href="https://man7.org/linux/man-pages/man1/perf.1.html">perf</a> and it eventually boiled down to these three bottlenecks:<ul><li>Execution of the bytecode</li><li>Execution of the regular expression bytecode</li><li>Aho-Corasick</li></ul>Nothing unusual if you know how YARA works internally. YARA rule conditions are compiled into bytecode, which is then interpreted during the rule evaluation. The same applies for regular expressions with their own bytecode. Lastly, there’s Aho-Corasick, an algorithm for multi-pattern matching (matching multiple patterns during the single pass over the matched data). This algorithm efficiently matches the strings section of the rules. However, the algorithm is old and not suitable for modern architectures. Other shortcomings are described in our <a href="https://engineering.avast.io/yara-in-search-of-regular-expressions/">previous blogpost</a>.<h2>Closer look at the bytecode</h2>The bytecode in YARA is simple. It consists of 117 instructions for regular bytecode and 26 instructions for regular expressions bytecode. The bytecode interpreter is a stack-based machine with a few slots for random-access memory, primarily used for loops and storing iteration count, the current iteration number, etc.When YARA rules are compiled, bytecode is generated during a single pass and then it’s left as-is. Bytecode is not optimized (apart from very simplistic constant folding). We initially thought of introducing bytecode optimizations. However, we&nbsp; worried about the benefits, as we couldn’t identify major pain points and the optimizations YARA bytecode would benefit from.At that time, we decided to take a step back and look at the whole picture. How many real instructions are executed for a simple rule with a few instructions? So we took this rule:

This gets compiled into these bytecode instructions:

The actual native code is executed with these four instructions (compiled with optimizations on Linux x64 machine, leaving out MATCH_RULE to make the comparison fair):

This is a simple rule. Imagine what it would look&nbsp; like if you started writing actual production-level rules. We asked ourselves, can we get rid of all of this? Can we somehow translate the YARA rules directly into the native code? And as it will be shown further in this blog post, we can.<h2>Multi-pattern matching</h2>Regular expression bytecode is heavily specialized for its purpose. The principles to address the regular bytecode bottleneck can be applied to regular expression bytecode, without building our own regular expression engine.We were aware of <a href="https://github.com/intel/hyperscan">HyperScan</a> and that it performs well in the <a href="https://rust-leipzig.github.io/regex/2017/03/28/comparison-of-regex-engines/">regex engine benchmarks</a>. It is capable of multi-pattern matching and, compared to Aho-Corasick, it is well suited for modern processor architectures, utilizing SIMD instruction sets like SSE or AVX.&nbsp;<a href="https://twitter.com/regeciovad">Dominika Regéciová</a> performed a <a href="https://engineering.avast.io/yara-vs-hyperscan-alternative-pattern-matching-engines/">comparison of HyperScan vs YARA</a>, proving it pays off. Therefore, we chose HyperScan&nbsp; as a solution, not only for regular expressions, but to replace the Aho-Corasick algorithm.<h2>Compiler</h2>We didn’t want to build a compiler toolchain ourselves (for example, with the help of the <a href="https://llvm.org/">LLVM</a>) as&nbsp; we would probably be still writing it instead of this blog post. We wanted to reuse existing tooling that is known to work. We know C++ the best from all the languages that are compiled to native code, so we decided to… (dramatic pause)… transpile YARA rules into C++.The compiler is based on the <a href="https://github.com/avast/yaramod">yaramod</a> library, our open-source library for the manipulation of YARA rules. In the first phase, it inspects the input ruleset and extracts the strings and regular expressions into the HyperScan pattern databases. HyperScan itself supports two different pattern types – literals, which contain no special metacharacters and ordinary regular expressions. Plain strings are mapped to literal patterns, while regular expressions are mapped to the regular expression pattern types. Special cases are hex strings, which are transformed into regular expressions, however, if we recognize that the hex string contains no special metacharacters (alternations, wildcards, jumps, …), we treat them like plain strings. &nbsp;The second phase consists of transpling the condition of rules into C++ code, representing the same logic as in YARA. The transpiled ruleset is then compiled to the shared/dynamic library, which can be imported using the <a href="https://man7.org/linux/man-pages/man3/dlopen.3.html">dlopen</a> function.<h2>Runtime</h2>Even if YARA is transpiled to C++, logic capable of performing the YARA specific task of matching the strings at first, executing the condition of each rule, comparing string matches, etc, is required.We therefore provide a common shared runtime that implements the two important things:<ol><li>Operational environment of YARA, bringing the concept of YARA rules into the whole compiled ruleset. It is capable of starting the YARA matching, recording the matches and handing the control flow to each rule for its evaluation.</li><li>Common functions substitute YARA bytecode instructions, not used by the operational environment itself, but through the transpiled rulesets. Instead of generating a big snippet of code for an expression <code>$str at &lt;offset&gt;</code>, we generate a call to a common function <code>match_at()</code>,&nbsp; implemented in the runtime. Transpiled rule conditions are compact and easy to read.</li></ol>To maximize compiler optimization efficiency, we heavily rely on the template metaprogramming in C++ to perform as many things as possible during the compilation time. The best example are the loops. Instead of using a generic template for all loops, requiring dynamic allocations, we generate a unique code for each loop in each depth.Here’s an example of the transpiled form and compiled form of the rule shown in the beginning of this blog post (excluding the matching of course).&nbsp;

Here’s an example of a call to a built-in function that reads big-endian bytes on a low-endian machine. The compiler optimizes away our compile-time oriented runtime and directly compares the values against the big-endian form.

<h2>Putting it together</h2>This is how the whole YaraNG&nbsp; toolchain looks like:.

We provide two tools called yarangc, implementing the compiler, and yarang implementing the scanner capable of loading the compiled ruleset.&nbsp;In the end, we achieved an improvement on our scanning hardware. Our regular scanner took around 20 minutes while the new scanner took around 13 minutes, which is a 35% improvement. Overall, CPU utilization was lowered, so with the new scanner, there was still headroom when it comes to the CPU utilization. Numbers from the <code>time</code> utility can be found in the <a href="https://github.com/avast/yarang#results">README file</a> in our repository.<h2>Conclusion</h2>We have shown that it is possible to take a different approach to YARA and that it might be possible to have multiple scanning tools over the YARA language in the future, depending on the needs of the user. In this blog post, we focused on the use-case of having faster scanning speed.When considering a more distant future, despite disks usually being the bottleneck for now, NVMe SSDs are becoming more and more common on consumer machines. The throughput of the PCIe bus is also reaching speeds where we might not consider disks as bottlenecks anymore.For now, only a limited subset of YARA language is supported, but with the help of the YARA community, this could become the alternative YARA scanner implementation. We made this project available on <a href="https://github.com/avast/yarang">our GitHub</a> where you can try to play around with it and even contribute. Let’s see what the future brings us!

Revolutionizing YARA Scanner Performance

YaraNG: Reinventing the YARA scanner

<a href="https://github.com/VirusTotal/yara">YARA</a> has been popular tool and language in a malware hunting community for quite some time and it is still getting a lot of attention. Individuals and organizations collect rulesets that they process, scan with them, export them to VirusTotal Hunting etc. Another possibility is that they have some kind of intelligence elsewhere and they generate YARA rulesets which are then processed as usual. Doing all of this manually would be a burden on malware analysts and that’s where automation steps in. But for automation, you need tools. Tools that would help you with processing YARA rules. That’s the reason why <a href="https://github.com/avast/yaramod">yaramod</a> was created.<h2>What is yaramod?</h2>Yaramod is a library for parsing, creating and formatting YARA rulesets. It is able to give you YARA ruleset in form of internal representation, which you can analyze or modify and then turn the internal representation back into YARA ruleset. It does not depend on libyara because it uses its own parser (written in <a href="https://github.com/metthal/pog">pog</a>). In this blog post, I willl showcase what is yaramod capable of and how you can utilize it yourself.<h2>Analyze rules</h2>Let’s say you want to do the simplest thing imaginable – print our all rules that you have in your YARA file and also location of all rules. The very first thing that you might think of are regular expressions. It’s not that hard to come up with regular expression <code>rule\s+[a-zA-Z_]\w*</code>. BUT, what if the rule is commented out? You might or might not want it. What if someone was so evil and put block comment in between rule and its name? What if you care about included files? The more you think about it, the more edge cases keep popping up (like what if someone put <code>rule &lt;name&gt;</code> inside meta?). It’s just simply better if you don’t have to care.

In the same way as rules, you can also iterate over strings and inspect their contents. For example, counting number of strings of each type is as easy as this. &nbsp;

Working with conditions is much harder. As you know, condition is an expression that is evaluated when the rule is being matched against the input. The condition itself can be as simple as just a single expression or it can be complex and consist of several expressions joined with several logical ands, ors, nots, …. In yaramod, we represent the condition in form of <a href="https://en.wikipedia.org/wiki/Abstract_syntax_tree">Abstract Syntax Tree</a> (AST). It is just a tree where each node represents some specific syntactic construct in your condition. For example, let’s take condition <code>$str1 or (#str2 &gt; 2)</code>. When represented as AST, this condition would look like this.

So in order to inspect and analyze conditions you have to preform some kind of traversal though AST. Yaramod doesn’t leave you on your own here and tries to ease this process by providing helper types which assist you in traversal using <a href="https://en.wikipedia.org/wiki/Visitor_pattern">visitor-like</a> interface.One of the most performance-heavy things you can do in YARA are regular expressions. They have their own bytecode and each time we want to match any regular expressions we are basically running interpreter which executes some sort of instructions so it is <a href="https://gist.github.com/Neo23x0/e3d4e316d7441d9143c7#regular-expressions">often recommended</a> to stay away from them. But you can’t completely avoid them and you’ll run into multiple cases where regular expressions are going to be needed. In that matter, let’s build a tool using yaramod, that will keep track of how many regular expressions we use for each rule in our ruleset.

And that’s it. We are using <code>ObservingVisitor</code> what is a helper class that will traverse the whole AST by default and we can only provide specific <code>visit</code> methods for the parts of condition we are interested in. We could have used <code>Visitor</code> directly but we would need to write our own traversal for all the <code>and</code> expressions, <code>or</code> expressions, function calls and many others. It is usual to base the code that revolves around traversing the whole AST on <code>ObservingVisitor</code>. We’ve then provided specific <code>visit</code> method for <code>RegexpExpression</code> which is a specific type of expression used for regular expressions inside conditions. This method is called whenever AST runs into node with <code>RegexpExpression</code> in it. Output of this script will look like this:

for ruleset like this one: &nbsp;

There are many different expression types which you can run into. List of all of them can be found in our <a href="https://yaramod.readthedocs.io/en/latest/parsing_rulesets.html#expression-types">documentation</a>.<h4>VirusTotal ruleset</h4>If you use your rulesets on VirusTotal Hunting, there is a high chance that you are using at least one of the <a href="https://www.virustotal.com/intelligence/help/malware-hunting/#tag-externals">external variables</a> provided by VirusTotal during YARA runtime. By default, you will be able to parse those without any problems as you can see here.

<h2>Modifying rules</h2>In the same way how you observe rulesets, you can also modify them while doing the traversal. You just need to use <code>ModifyingVisitor</code>. The only change between observing one and modifying one is that <code>ModifyingVistor</code> expects return values of <code>visit</code> methods. There are 3 possible return values:<ul><li><code>None</code> which means you would like to keep this AST node intact</li><li>Another instance of <code>Expression</code> type which means that you would like to replace this AST node with the one you return</li><li>Special value <code>yaramod.VisitAction.Delete</code> which means that you want to delete this AST node. Deleting a node has a subsequent impact on other AST nodes which are in relationship with this node (parents, siblings, …). The consequences differ from type to type but usually, the delete request will be propagated to parents and that will mean the whole branch of AST will be removed.</li></ul>Let’s say that we want to simplify conditions a little bit. According to YARA documentation, there are multiple overloads of <a href="https://yara.readthedocs.io/en/latest/modules/pe.html#c.imports">pe.imports</a> function some of which accepts regular expressions and others strings. The overloads with strings will have less impact on performance because they are just doing simple <a href="https://github.com/VirusTotal/yara/blob/master/libyara/modules/pe/pe.c#L2307"><code>strcasecmp</code></a>. Let’s do a script which will simplify those calls.At first, we would like to recognize which regular expressions to simplify. We can’t for example easily convert regular expression <code>a.c</code> into anything string-like because we can’t do wildcards. Same for many other operations offered by regular expressions. Since it is not the point of this blogpost to build function that recognizes these regular expressions, we will use something rather simple which just iterates over the regexp and whenever it runs into <code>\</code> for escape sequence, it skips the character immediately following it. Otherwise it checks whether some specific characters are in the rest of the string. It is not ideal but sufficient for this example.

Another step is to create function that will transform the regular expression into the string because we can’t have the same escape sequences in both of them. For example <code>\.</code> in regular expression denotes actual <code>.</code> character but in strings we can’t use it. Again, let’s come up with something simple.

At the start, it just removes anchors for the beginning and end of line. Then it looks for escape sequences and copies only the character which is following the <code>\</code> in the escape sequence. Now that we have these functions, let’s put it all together using <code>ModifyingVisitor</code>. &nbsp;

We try to visit each <code>FunctionCallExpression</code> and detect whenever we enter <code>pe.imports</code> function call. Once this condition is satisfied, we recursively visit each function argument using <code>accept</code> method of each argument. These will invoke visits of <code>RegexpExpression</code> where we detect simple regular expressions using our <code>simple_regexp</code> function and convert it to string using <code>to_string</code> if so. Since there can be regular expressions in other functions, we need <code>inside_pe_imports</code> to distinguish those calls. If there were functions in YARA which return regular expressions, we might need to use stack for storing these as there may be nested function calls in arguments of <code>pe.imports</code>. This isn’t our case so we went with simple solution.<h2>Generate rules</h2><h4>Condition</h4>Yaramod is capable of generating your own YARA rulesets out of data that you might have in a database, text file, etc. It is heavily based on <a href="https://en.wikipedia.org/wiki/Builder_pattern">builder design-pattern</a> and operator overloading to make the generation as easy as possible without having to write much code. It aims for declarative style of generation. Let’s image that we pulled the list of strings out of database and we would like to create YARA rule out of them.

We can also request specific number of matches if we don’t want all of them at once. &nbsp;

Let’s modify the condition so that each string must hit at least 2 times. &nbsp;

There are much more things you can express using the declarative style of yaramod. See our <a href="https://yaramod.readthedocs.io/en/latest/creating_rulesets.html#list">documentation</a> for list of all functions that are available for creating expressions.<h4>Rule</h4>Now that we have condition, we can go to generation of rules. Rules use the same declarative style of describing how they should look like. Following piece of code will generate rule named <code>rule_name</code> with plain strings with both <code>ascii</code> and <code>wide</code> modifiers, named <code>$s&lt;index&gt;</code> and the condition we’ve created in the previous step.

The generated rule looks exactly like this: &nbsp;

To make it more feature rich, we can add few more meta information. &nbsp;

Which will make our rule look like this: &nbsp;

<h4>YARA file</h4>To put it all together, we need to create YARA file which contains multiple rules at the same time and also specifies required imports.

<h2>Formatting</h2>Very recent addition to yaramod features is automatic formatting The purpose of this feature is make it easier to write YARA rules in a specific format like following the style of placement of <code>{</code> and <code>}</code>, <code>(</code> and <code>)</code>, aligning of comments, normalization of whitespaces, indentation levels etc. The big advantage of autoformatting is that it keeps all the comments you have in the YARA file (usually parses discard any comments). We have the formatting hardcoded for now but we would definitely like if users were able to specify their own configuration for example for indentation character being used. In order to use autoformatting, instead of accessing <code>text</code> attribute (which just turns internal representation into YARA) of YARA file use <code>text_formatted</code> (which will use token stream of all seen tokens on the input and will take care of formatting them). Let’s say this is our YARA file on the input:

And if we parse this and ask for <code>text_formatted</code>: &nbsp;

As you can see, it has fixed some of the things I have mentioned but it still doesn’t want to be too intrusive. We are trying to fix the very necessary and still keep some of your formatting. If the feedback will be positive then we can push for being more and more intrusive.Formatting doesn’t work well with modifying of rulesets but it is something we would like to work on. Just be aware when you are using those two at the same time. It’s always best to start building the rule bottom-up – from condition, to rule and to the whole YARA file. So let’s start with the condition by matching all the strings.<h2>Conclusion</h2>We’ve shown how yaramod can be user to analyze, modify or even generate new YARA rulesets. We’ve shown some of its useful features and that it is a viable alternative to <a href="https://github.com/plyara/plyara">plyara</a> while providing even more features like parsing of conditions, modifications of rulesets, generating of completely new ones, autoformatting and more. Hopefully the examples and our extensive <a href="https://yaramod.readthedocs.io/en/latest/">documentation</a> will help you to build your own tools. Don’t forget to visit our <a href="https://github.com/avast/yaramod">GitHub repository</a> and leave an issue if you need any help (or if you just want to tell us that you use yaramod).

Marek Milkovič

Posts by Marek Milkovič

YaraNG: Reinventing the YARA scanner

Yaramod: Inspect, analyze and modify your YARA rules with ease