Cyber Insights 2026: Information Sharing
Information sharing is necessary for efficient cybersecurity, and is widespread; but never quite perfect in practice.
The post Cyber Insights 2026: Information Sharing appeared first on SecurityWeek.
Information sharing is necessary for efficient cybersecurity, and is widespread; but never quite perfect in practice.
The post Cyber Insights 2026: Information Sharing appeared first on SecurityWeek.
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.
The post Anatomy of an Attack: The Payroll Pirates and the Power of Social Engineering appeared first on Unit 42.

We've known that social engineering would get AI wings. Now, at the beginning of 2026, we are learning just how high those wings can soar.
The post Cyber Insights 2026: Social Engineering appeared first on SecurityWeek.
behash:"4acaac53c8340a8c236c91e68244e6cb"
/api/v3/files/09a8b930c8b79e7c313e5e741e1d59c39ae91bc1f10cdefa68b47bf77519be57/execution_parents
signature:"Peastaking plenipotence ductileness chilopodous codicillary."
signature:"ยฉ 2026 Eosinophil LLC"
exports:15Mmm95ml1RbfjH1VUyelYFCf exports:2dlSKEtPzvo1mHDN4FYgv
behash:5ddb604194329c1f182d7ba74f6f5946
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")
}
| sha256 | description |
|---|---|
| 6773af31bd7891852c3d8170085dd4bf2d68ea24a165e4b604d777bd083caeaa | malwarebytes-windows-github-io-X.X.X.zip |
| 4294d6e8f1a63b88c473fce71b665bbc713e3ee88d95f286e058f1a37d4162be | malwarebytes-windows-github-io-X.X.X.zip |
| 5591156d120934f19f2bb92d9f9b1b32cb022134befef9b63c2191460be36899 | malwarebytes-windows-github-io-X.X.X.zip |
| 42d53bf0ed5880616aa995cad357d27e102fb66b2fca89b17f92709b38706706 | malwarebytes-windows-github-io-X.X.X.zip |
| 5aa6f4a57fb86759bbcc9fc6c61b5f74c0ca74604a22084f9e0310840aa73664 | malwarebytes-windows-github-io-X.X.X.zip |
| 84021dcfad522a75bf00a07e6b5cb4e17063bd715a877ed01ba5d1631cd3ad71 | malwarebytes-windows-github-io-X.X.X.zip |
| ca8467ae9527ed908e9478c3f0891c52c0266577ca59e4c80a029c256c1d4fce | malwarebytes-windows-github-io-X.X.X.zip |
| 9619331ef9ff6b2d40e77a67ec86fc81b050eeb96c4b5f735eb9472c54da6735 | malwarebytes-windows-github-io-X.X.X.zip |
| a2842c7cfaadfba90b29e0b9873a592dd5dbea0ef78883d240baf3ee2d5670c5 | malwarebytes-windows-github-io-X.X.X.zip |
| 4705fd47bf0617b60baef8401c47d21afb3796666092ce40fbb7fe51782ae280 | malwarebytes-windows-github-io-X.X.X.zip |
| 580d37fc9d9cc95dc615d41fa2272f8e86c9b4da2988a336a8b3a3f90f4363c2 | malwarebytes-windows-github-io-X.X.X.zip |
| d47fd17d1d82ea61d850ccc2af3bee54adce6975d762fb4dee8f4006692c5ef7 | malwarebytes-windows-github-io-X.X.X.zip |
| 606baa263e87d32a64a9b191fc7e96ca066708b2f003bde35391908d3311a463 | CoreMessaging.dll DLL loaded by DLL SideLoading |
| fd855aa20467708d004d4aab5203dd5ecdf4db2b3cb2ed7e83c27368368f02bb | CoreMessaging.dll DLL loaded by DLL SideLoading |
| a0687834ce9cb8a40b2bb30b18322298aff74147771896787609afad9016f4ea | CoreMessaging.dll DLL loaded by DLL SideLoading |
| 4235732440506e626fd4d0fffad85700a8fcf3e83ba5c5bc8e19ada508a6498e | CoreMessaging.dll DLL loaded by DLL SideLoading |
| cd1fe2762acf3fb0784b17e23e1751ca9e81a6c0518c6be4729e2bc369040ca5 | CoreMessaging.dll DLL loaded by DLL SideLoading |
| f798c24a688d7858efd6efeaa8641822ad269feeb3a74962c2f7c523cf8563ff | CoreMessaging.dll DLL loaded by DLL SideLoading |
| 0698a2c6401059a3979d931b84d2d4b011d38566f20558ee7950a8bf475a6959 | CoreMessaging.dll DLL loaded by DLL SideLoading |
| 1b3bee041f2fffcb9c216522afa67791d4c658f257705e0feccc7573489ec06f | CoreMessaging.dll DLL loaded by DLL SideLoading |
| 231c05f4db4027c131259d1acf940e87e15261bb8cb443c7521294512154379b | CoreMessaging.dll DLL loaded by DLL SideLoading |
| ec2e30d8e5cacecdf26c713e3ee3a45ebc512059a64ba4062b20ca8bec2eb9e7 | CoreMessaging.dll DLL loaded by DLL SideLoading |
| 58bd2e6932270921028ab54e5ff4b0dbd1bf67424d4a5d83883c429cadeef662 | CoreMessaging.dll DLL loaded by DLL SideLoading |
| 57ed35e6d2f2d0c9bbc3f17ce2c94946cc857809f4ab5c53d7cb04a4e48c8b14 | CoreMessaging.dll DLL loaded by DLL SideLoading |
| cfcf3d248100228905ad1e8c5849bf44757dd490a0b323a10938449946eabeee | CoreMessaging.dll DLL loaded by DLL SideLoading |
| f02be238d14f8e248ad9516a896da7f49933adc7b36db7f52a7e12d1c2ddc6af | CoreMessaging.dll DLL loaded by DLL SideLoading |
| f60802c7bec15da6d84d03aad3457e76c5760e4556db7c2212f08e3301dc0d92 | CoreMessaging.dll DLL loaded by DLL SideLoading |
| 02dc9217f870790b96e1069acd381ae58c2335b15af32310f38198b5ee10b158 | CoreMessaging.dll DLL loaded by DLL SideLoading |
| f9549e382faf0033b12298b4fd7cd10e86c680fe93f7af99291b75fd3d0c9842 | CoreMessaging.dll DLL loaded by DLL SideLoading |
| 92f4d95938789a69e0343b98240109934c0502f73d8b6c04e8ee856f606015c8 | CoreMessaging.dll DLL loaded by DLL SideLoading |
| 66fba00b3496d61ca43ec3eae02527eb5222892186c8223b9802060a932a5a7a | CoreMessaging.dll DLL loaded by DLL SideLoading |
| e5dd464a2c90a8c965db655906d0dc84a9ac84701a13267d3d0c89a3c97e1e9b | CoreMessaging.dll DLL loaded by DLL SideLoading |
| 35211074b59417dd5a205618fed3402d4ac9ca419374ff2d7349e70a3a462a15 | CoreMessaging.dll DLL loaded by DLL SideLoading |
| 6863b4906e0bd4961369b8784b968b443f745869dbe19c6d97e2287837849385 | CoreMessaging.dll DLL loaded by DLL SideLoading |
| a83c478f075a3623da5684c52993293d38ecaa17f4a1ddca10f95335865ef1e2 | CoreMessaging.dll DLL loaded by DLL SideLoading |
| 43e2936e4a97d9bc43b423841b137fde1dd5b2f291abf20d3ba57b8f198d9fab | CoreMessaging.dll DLL loaded by DLL SideLoading |
| f001ae3318ba29a3b663d72b5375d10da5207163c6b2746cfae9e46a37d975cf | CoreMessaging.dll DLL loaded by DLL SideLoading |
| c67403d3b6e7750222f20fa97daa3c05a9a8cce39db16455e196cd81d087b54d | CoreMessaging.dll DLL loaded by DLL SideLoading |
| 5ee9d4636b01fd3a35bd8e3dce86a8c114d8b0aa6b68b1d26ace7ef0f85b438a | Payload dropped by one of the malicious DLLs |
| e84b0dadb0b6be9b00a063ed82c8ddba06a2bd13f07d510d14e6fd73cd613fba | Payload dropped by one of the malicious DLLs |
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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. [โฆ]
The post How to Perform and Combat Social Engineering appeared first on Black Hills Information Security, Inc..
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 quicklyโand new trends and types of campaigns follow.
In 2025, the rapid development of Artificial Intelligenceย (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.
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 asย 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 daughterโs voice was AI-cloned and used in a scam.
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.
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.
Social media platforms are also widely used to peddle fake products, AI generated disinformation, dangerous goods,ย and drop-shipped goods.
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 wasnโt meant to do.
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.
Since early 2024, OpenAI says it has disrupted more than 20 campaigns around the world that attempted to abuse its AI platform for criminal operations and deceptive campaigns.
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 donโt just report on threatsโwe remove them
Cybersecurity risks should never spread beyond a headline. Keep threats off your devices byย downloading Malwarebytes today.
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 quicklyโand new trends and types of campaigns follow.
In 2025, the rapid development of Artificial Intelligenceย (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.
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 asย 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 daughterโs voice was AI-cloned and used in a scam.
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.
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.
Social media platforms are also widely used to peddle fake products, AI generated disinformation, dangerous goods,ย and drop-shipped goods.
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 wasnโt meant to do.
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.
Since early 2024, OpenAI says it has disrupted more than 20 campaigns around the world that attempted to abuse its AI platform for criminal operations and deceptive campaigns.
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 donโt just report on threatsโwe remove them
Cybersecurity risks should never spread beyond a headline. Keep threats off your devices byย downloading Malwarebytes today.
You might not know it, given the many headlines focused on new questions about copyright and Generative AI, but the yearโs biggest copyright case concerned an old-for-the-internet question: do ISPs have to be copyright cops? After years of litigation, that question is now squarely before the Supreme Court. And if the Supreme Court doesnโt reverse a lower courtโs ruling, ISPs could be forced to terminate peopleโs internet access based on nothing more than mere accusations of copyright infringement. This would threaten innocent users who rely on broadband for essential aspects of daily life.
This issue turns on what courts call โsecondary liability,โ which is the legal idea that someone can be held responsible not for what they did directly, but for what someone else did using their product or service. The case began when music companies sued Cox Communications, arguing that the ISP should be held liable for copyright infringement committed by some of its subscribers. The Court of Appeals for the Fourth Circuit agreed, adopting a โmaterial contributionโ standard for contributory copyright liability (a rule for when service providers can be held liable for the actions of users). Under that standard, providing a service that could be used for infringement is enough to create liability when a customer infringes.
The Fourth Circuitโs rule would have devastating consequences for the public. Given copyright lawโs draconian penalties, ISP would be under enormous pressure to terminate accounts whenever they get an infringement notice, whether or not the actual accountholder has infringed anything: entire households, schools, libraries, or businesses that share an internet connection. These would include:
And with more than a third of Americans having only one or no broadband provider, many users would have no way to reconnect.
EFFโalong with the American Library Association, the Association of Research Libraries, and Re:Createโfiled an amicus brief urging the Court to reverse the Fourth Circuitโs decision, taking guidance from patent law. In the Patent Act, where Congress has explicitly defined secondary liability, thereโs a different test: contributory infringement exists only where a product is incapable of substantial non-infringing use. Internet access, of course, is overwhelmingly used for lawful purposes, making it the very definition of a โstaple article of commerceโ that canโt be liable under the patent framework.
The Supreme Court held a hearing in the case on December 1, and a majority of the justices seemed troubled by the implications of the Fourth Circuitโs ruling. One exchange was particularly telling: asked what should happen when the notices of infringement target a university account upon which thousands of people rely, Sonyโs counsel suggested the university could resolve the issue by essentially slowing internet speeds so infringement might be less appealing. Itโs hard to imagine the university community would agree that research, teaching, artmaking, library services, and the myriad other activities that rely on internet access should be throttled because of the actions of a few students. Hopefully the Supreme Court wonโt either.
We expect a ruling in the case in the next few months. Fingers crossed that the Court rejects the Fourth Circuitโs draconian rule.
This article is part of our Year in Review series. Read other articles about the fight for digital rights in 2025.


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 vectorsโthe latest tactics Flashpoint is seeing in the wildโthat threat actors are using to manipulate users and bypass modern security perimeters.
Here are the latest methods threat actors are leveraging to facilitate infections:
Mark of the Web (MotW) is a critical Windows defense feature that tags files downloaded from the internet as โuntrustedโ by adding a hidden NTFS Alternate Data Stream (ADS) to the file. This tag triggers โProtected Viewโ 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:
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 Bรกlint 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 โheadless 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 victimโs systems.
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.
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 todayโs identity-based digital attacks.
As detailed in The Proactive Defenderโs 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 Defenderโs Guide to Infostealers today.
The post The Infostealer Gateway: Uncovering the Latest Methods in Defense Evasion appeared first on Flashpoint.
Keeping websites and applications secure starts with knowing which vulnerabilities exist, how severe they are, and whether they affect your stack. Thatโs exactly where the CVE program shines. Below, weโll cover some CVE fundamentals, including what they are, how to search and understand the data, and how to translate this information into actionable steps.
Introduction to the CVE database
So, what is CVE?
CVE stands for Common Vulnerabilities and Exposures, a community-driven program that assigns unique identifiers to publicly known vulnerabilities.
Continue reading A Beginnerโs Guide to the CVE Database at Sucuri Blog.

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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.
The post How to Design and Execute Effective Social Engineering Attacks by Phone appeared first on Black Hills Information Security, Inc..

British defence firms have reportedly warned staff not to connect their phones to Chinese-made EVs
Mobile phones and desktop computers are longstanding targets for cyber spies โ but how vulnerable are electric cars?
On Monday the i newspaper claimed that British defence firms working for the UK government have warned staff against connecting or pairing their phones with Chinese-made electric cars, due to fears that Beijing could extract sensitive data from the devices.
Continue reading...
ยฉ Photograph: Ying Tang/NurPhoto/REX/Shutterstock

ยฉ Photograph: Ying Tang/NurPhoto/REX/Shutterstock

ยฉ Photograph: Ying Tang/NurPhoto/REX/Shutterstock
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GoPhish provides a nice platform for creating and running phishing campaigns. This blog will guide you through installing GoPhish and creating a campaign.ย
The post Gone Phishing: Installing GoPhish and Creating a Campaign appeared first on Black Hills Information Security, Inc..
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by moth Hard-coded cryptographic secrets? In my commercially purchased, closed-source software? Itโs more likely than you think. Like, a lot more likely.ย This blog post details a true story of [โฆ]
The post Indecent Exposure: Your Secrets are Showingย appeared first on Black Hills Information Security, Inc..
p:5+ have:behavior fs:30d+ not have:sigma
p:5+ (sandbox_name:"CAPE Sandbox" or sandbox_name:"Zenbox") fs:30d+ not have:sigma
p:5+ have:behavior fs:30d+ sigma_critical:0 sigma_high:0 sigma_medium:0 sigma_low:2-
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
{
"System": {
"Provider": {
"Guid": "{5770385F-C22A-43E0-BF4C-06F5698FFBD9}",
"Name": "Microsoft-Windows-Sysmon"
},
"EventID": 1,
"Version": 5,
"Level": 4,
"Task": 1,
"Opcode": 0,
"Keywords": "0x8000000000000000",
"TimeCreated": {
"SystemTime": "2024-11-26T16:23:05.132539500Z"
},
"EventRecordID": 692861,
"Correlation": {},
"Execution": {
"ProcessID": 2396,
"ThreadID": 3116
},
"Channel": "Microsoft-Windows-Sysmon/Operational",
"Computer": "DESKTOP-B0T93D6",
"Security": {
"UserID": "S-1-5-18"
}
},
"EventData": {
"RuleName": "-",
"UtcTime": "2024-11-26 16:23:05.064",
"ProcessGuid": "{C784477D-F5E9-6745-6006-000000003F00}",
"ProcessId": 4184,
"Image": "C:\\Windows\\Microsoft.NET\\Framework\\v4.0.30319\\vbc.exe",
"FileVersion": "14.8.3761.0",
"Description": "Visual Basic Command Line Compiler",
"Product": "Microsoftยฎ .NET Framework",
"Company": "Microsoft Corporation",
"OriginalFileName": "vbc.exe",
"CommandLine": "C:\\Windows\\Microsoft.NET\\Framework\\v4.0.30319\\vbc.exe",
"CurrentDirectory": "C:\\Users\\george\\AppData\\Roaming\\comlocal\\RUYCLAXYVMFJ\\",
"User": "DESKTOP-B0T93D6\\george",
"LogonGuid": "{C784477D-9D9B-66FF-6E87-050000000000}",
"LogonId": "0x5876e",
"TerminalSessionId": 1,
"IntegrityLevel": "High",
"Hashes": {
"SHA1": "61F4D9A9EE38DBC72E840B3624520CF31A3A8653",
"MD5": "FCCB961AE76D9E600A558D2D0225ED43",
"SHA256": "466876F453563A272ADB5D568670ECA98D805E7ECAA5A2E18C92B6D3C947DF93",
"IMPHASH": "1460E2E6D7F8ECA4240B7C78FA619D15"
},
"ParentProcessGuid": "{C784477D-F5D4-6745-5E06-000000003F00}",
"ParentProcessId": 6572,
"ParentImage": "C:\\Windows\\SysWOW64\\more.com",
"ParentCommandLine": "C:\\Windows\\SysWOW64\\more.com",
"ParentUser": "DESKTOP-B0T93D6\\george"
}
}
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
{
"System": {
"Provider": {
"Guid": "{5770385F-C22A-43E0-BF4C-06F5698FFBD9}",
"Name": "Microsoft-Windows-Sysmon"
},
"EventID": 11,
"Version": 2,
"Level": 4,
"Task": 11,
"Opcode": 0,
"Keywords": "0x8000000000000000",
"TimeCreated": {
"SystemTime": "2024-12-02T00:52:23.072811600Z"
},
"EventRecordID": 1555690,
"Correlation": {},
"Execution": {
"ProcessID": 2624,
"ThreadID": 3112
},
"Channel": "Microsoft-Windows-Sysmon/Operational",
"Computer": "DESKTOP-B0T93D6",
"Security": {
"UserID": "S-1-5-18"
}
},
"EventData": {
"RuleName": "-",
"UtcTime": "2024-12-02 00:52:23.059",
"ProcessGuid": "{C784477D-04C6-674D-5C06-000000004B00}",
"ProcessId": 7592,
"Image": "C:\\Users\\george\\Desktop\\ezzz.exe",
"TargetFilename": "C:\\Users\\george\\AppData\\Roaming\\MyData\\DataLogs.conf",
"CreationUtcTime": "2024-12-02 00:52:23.059",
"User": "DESKTOP-B0T93D6\\george"
}
sigma_rule:a1021d4086a92fd3782417a54fa5c5141d1e75c8afc9e73dc6e71ef9e1ae2e9c
sigma_rule:8f179585d5c1249ab1ef8cec45a16d112a53f91d143aa2b0b6713602b1d19252
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