![]() ![]() The statistics for the URL-shortened link on bit.ly confirm the observations from our telemetry, showing targets mainly in Brazil, as depicted in Figure 4 Distribution of Bit.ly campaign on 27-28th December.įigure 4 Distribution of Bit.ly campaign on 27-28th December The malicious documents contain a simple Macro, which executes a remotely-hosted script using MSHTA: The most prevalent combinations of methods observed are depicted in Figure 2.Īs an example of an email delivery used during December 2018 campaigns, let’s look at what pretended to be a rooming list (SHA256: ac70d15106cc368c571c3969c456778b494d62c5319dc366b7e2c116834c6187), which follows one path from Figure 2, more precisely the steps described in Figure 3.įigure 3 December 2018 campaign delivery example Following this approach, this actor can find multiple tools and resources to perform their activities, and at the same time, make attribution and tracking more difficult for analysts. The attackers make use of multiple common off-the-shelf methods that are observed in many campaigns, such as external references to remote scripts executed by MSHTA. The author consistently uses an acronym throughout their work - “C.D.T Original” (see details on Figure 1).įigure 1 Example of malicious document metadata While investigating the malicious documents used in the campaign, we discovered an interesting consistency in the document metadata. Table 1 Some email subjects and attachment names representative of this campaign. Voucher de Reserva ADRIANA MILLER RODRIGUES.ppa “Ficha cadastral Leticia Ferreira Mendes.ppam”, “Ficha cadastral Jacinto Mendes da Silva.ppam”, “Ficha cadastral Marcos Portela Correa.ppam”, “Ficha cadastral Francisco Prado.ppam” Table 1 shows a representative list of typical subjects and attachment names found during the campaign. Topics used by the actor are typically related to travel bookings and vouchers, and target mainly Brazilian victims. Our telemetry for this campaign identified email as the primary delivery mechanism and found the first related samples were distributed in August 2018. Have you ever wondered how an actor can run a very cheap and effective credit card data underground business? Welcome to “Operation Comando”. We profiled this threat actor and that has resulted in uncovering not only their delivery mechanisms, but also their arsenal of remote access tools and info-stealing trojans, both acquired from underground forums as well as open source tools found in GitHub repositories. Our discovery of this malware family shows the reason for the persistent focus on hotel reservations as a primary vector: stealing credit card information from customers. ![]() We followed network traces and pivoted on the information left behind by this actor, such as open directories, document metadata, and binary peculiarities, which enabled us to find a custom-made piece of malware, that we named “CapturaTela”. Although our initial analysis didn’t show any novel or advanced techniques, we did observe strong persistence during the campaign that triggered our curiosity. All the computations are performed in your browser so that your credit card number remains safely with you.In December 2018, Palo Alto Networks Unit 42 researchers identified an ongoing campaign with a strong focus on the hospitality sector, specifically on hotel reservations. NO! We can't and won't access any of the data you insert in this tool, including you credit card number. The only risk is if anyone is literally looking at your screen while you use this tool. Is it safe to use wtools online credit card verification tool? ![]() And click to the button "Validate" and you will get result in the result section. Just paste your Credit Card Numbers to the input above that you want to check. If you want to check this tool, you can generate a random card number using Generator of Credit Card Numbers. You can also save time by using online Validate Credit Card Numbers to perform the checksum calculations for you. The calculation to comparing a credit card number with your checksum is known as the Luhn algorithm. This gives merchants the opportunity to verify the validity of the card number before accepting the buyer's payment. Credit card numbers are generated accordance with certain rules so we perform some checks on your number and explain what each part of it means. ![]()
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