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Social Engineering and Microsoft SSPR: The Road to Pwnage is Paved with Good Intentions 

Social Engineering and Microsoft SSPR

This scenario simultaneously tests identity confirmation tooling (SSPR, MFA, Conditional Access), how users act under pressure, and the organization's ability to detect and follow-up on social engineering attacks.

The post Social Engineering and Microsoft SSPR: The Road to Pwnage is Paved with Good Intentions  appeared first on Black Hills Information Security, Inc..

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Anatomy of an Attack: The Payroll Pirates and the Power of Social Engineering

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.

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New Infostealer Campaign Targets Users via Spoofed Software Installers

Introduction

As part of our commitment to sharing interesting hunts, we are launching these 'Flash Hunting Findings' to highlight active threats. Our latest investigation tracks an operation active between January 11 and January 15, 2026, which uses consistent ZIP file structures and a unique behash ("4acaac53c8340a8c236c91e68244e6cb") for identification. The campaign relies on a trusted executable to trick the operating system into loading a malicious payload, leading to the execution of secondary-stage infostealers.

Findings

The primary samples identified are ZIP files that mostly reference the MalwareBytes company and software using the filename malwarebytes-windows-github-io-X.X.X.zip. A notable feature for identification is that all of them share the same behash.
behash:"4acaac53c8340a8c236c91e68244e6cb"
The initial instance of these samples was identified on January 11, 2026, with the most recent occurrence recorded on January 14.
All of these ZIP archives share a nearly identical internal structure, containing the same set of files across the different versions identified. Of particular importance is the DLL file, which serves as the initial malicious payload, and a specific TXT file found in each archive. This text file has been observed on VirusTotal under two distinct filenames: gitconfig.com.txt and Agreement_About.txt.
The content of the TXT file holds no significant importance for the intrusion itself, as it merely contains a single string consisting of a GitHub URL.
However, this TXT is particularly valuable for pivoting and infrastructure mapping. By examining its "execution parents," analysts can identify additional ZIP archives that are likely linked to the same malicious campaign. These related files can be efficiently retrieved for further investigation using the following VirusTotal API v3 endpoint:
/api/v3/files/09a8b930c8b79e7c313e5e741e1d59c39ae91bc1f10cdefa68b47bf77519be57/execution_parents
The primary payload of this campaign is contained within a malicious DLL named CoreMessaging.dll. Threat actors are utilizing a technique known as DLL Sideloading to execute this code. This involves placing the malicious DLL in the same directory as a legitimate, trusted executable (EXE) also found within the distributed ZIP file. When an analyst or user runs the legitimate EXE, the operating system is tricked into loading the malicious CoreMessaging.dll.
The identified DLLs exhibit distinctive metadata characteristics that are highly effective for pivoting and uncovering additional variants within the same campaign. Security analysts can utilize specific hunting queries to track down other malicious DLLs belonging to this activity. For instance, analysts can search for samples sharing the following unique signature strings found in the file metadata:
signature:"Peastaking plenipotence ductileness chilopodous codicillary."
signature:"© 2026 Eosinophil LLC"
Furthermore, the exported functions within these DLLs contains unusual alphanumeric strings. These exports serve as reliable indicators for identifying related malicious components across different stages of the campaign:
exports:15Mmm95ml1RbfjH1VUyelYFCf exports:2dlSKEtPzvo1mHDN4FYgv
Finally, another observation for behavioral analysis can be found in the relations tab of the ZIP files. These files document the full infection chain observed during sandbox execution, where the sandbox extracts the ZIP, runs the legitimate EXE, and subsequently triggers the loading of the malicious DLL. Within the Payload Files section, additional payloads are visible. These represent secondary stages dropped during the initial DLL execution, which act as the final malware samples. These final payloads are primarily identified as infostealers, designed to exfiltrate sensitive data.
Analysis of all the ZIP files behavioral relations reveals a recurring payload file consistently flagged as an infostealer. This malicious component is identified by various YARA rules, including those specifically designed to detect signatures associated with stealing cryptocurrency wallet browser extension IDs among others.
To identify and pivot through the various secondary-stage payloads dropped during this campaign, analysts can utilize a specific behash identifier. These files represent the final infection stage and are primarily designed to exfiltrate credentials and crypto-wallet information. The following behash provides a reliable pivot point for uncovering additional variants.
behash:5ddb604194329c1f182d7ba74f6f5946

IOCs

We have created a public VirusTotal Collection to share all the IOCs in an easy and free way. Below you can find the main IOCs related to the ZIP files and DLLs too.
import "pe"

rule win_dll_sideload_eosinophil_infostealer_jan26
{
  meta:
    author = "VirusTotal"
    description = "Detects malicious DLLs (CoreMessaging.dll) from an infostealer campaign impersonating Malwarebytes, Logitech, and others via DLL sideloading."
    reference = "https://blog.virustotal.com/2026/01/malicious-infostealer-january-26.html"
    date = "2026-01-16"
    behash = "4acaac53c8340a8c236c91e68244e6cb"
    target_entity = "file"
    hash = "606baa263e87d32a64a9b191fc7e96ca066708b2f003bde35391908d3311a463"
  condition:
    (uint16(0) == 0x5A4D and uint32(uint32(0x3C)) == 0x00004550 and pe.is_dll()) and
    pe.exports("15Mmm95ml1RbfjH1VUyelYFCf") and pe.exports("2dlSKEtPzvo1mHDN4FYgv")
}
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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How to Perform and Combat Social Engineering

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. […]

The post How to Perform and Combat Social Engineering appeared first on Black Hills Information Security, Inc..

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How AI made scams more convincing in 2025

This blog is part of a series where we highlight new or fast-evolving threats in consumer security. This one focuses on how AI is being used to design more realistic campaigns, accelerate social engineering, and how AI agents can be used to target individuals.

Most cybercriminals stick with what works. But once a new method proves effective, it spreads quickly—and new trends and types of campaigns follow.

In 2025, the rapid development of Artificial Intelligence (AI) and its use in cybercrime went hand in hand. In general, AI allows criminals to improve the scale, speed, and personalization of social engineering through realistic text, voice, and video. Victims face not only financial loss, but erosion of trust in digital communication and institutions.

Social engineering

Voice cloning

One of the main areas where AI improved was in the area of voice-cloning, which was immediately picked up by scammers. In the past, they would mostly stick to impersonating friends and relatives. In 2025, they went as far as impersonating senior US officials. The targets were predominantly current or former US federal or state government officials and their contacts.

In the course of these campaigns, cybercriminals used test messages as well as AI-generated voice messages. At the same time, they did not abandon the distressed-family angle. A woman in Florida was tricked into handing over thousands of dollars to a scammer after her daughter’s voice was AI-cloned and used in a scam.

AI agents

Agentic AI is the term used for individualized AI agents designed to carry out tasks autonomously. One such task could be to search for publicly available or stolen information about an individual and use that information to compose a very convincing phishing lure.

These agents could also be used to extort victims by matching stolen data with publicly known email addresses or social media accounts, composing messages and sustaining conversations with people who believe a human attacker has direct access to their Social Security number, physical address, credit card details, and more.

Another use we see frequently is AI-assisted vulnerability discovery. These tools are in use by both attackers and defenders. For example, Google uses a project called Big Sleep, which has found several vulnerabilities in the Chrome browser.

Social media

As mentioned in the section on AI agents, combining data posted on social media with data stolen during breaches is a common tactic. Such freely provided data is also a rich harvesting ground for romance scams, sextortion, and holiday scams.

Social media platforms are also widely used to peddle fake products, AI generated disinformation, dangerous goods,  and drop-shipped goods.

Prompt injection

And then there are the vulnerabilities in public AI platforms such as ChatGPT, Perplexity, Claude, and many others. Researchers and criminals alike are still exploring ways to bypass the safeguards intended to limit misuse.

Prompt injection is the general term for when someone inserts carefully crafted input, in the form of an ordinary conversation or data, to nudge or force an AI into doing something it wasn’t meant to do.

Malware campaigns

In some cases, attackers have used AI platforms to write and spread malware. Researchers have documented campaign where attackers leveraged Claude AI to automate the entire attack lifecycle, from initial system compromise through to ransom note generation, targeting sectors such as government, healthcare, and emergency services.

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.

Looking ahead

AI is amplifying the capabilities of both defenders and attackers. Security teams can use it to automate detection, spot patterns faster, and scale protection. Cybercriminals, meanwhile, are using it to sharpen social engineering, discover vulnerabilities more quickly, and build end-to-end campaigns with minimal effort.

Looking toward 2026, the biggest shift may not be technical but psychological. As AI-generated content becomes harder to distinguish from the real thing, verifying voices, messages, and identities will matter more than ever.


We don’t just report on threats—we remove them

Cybersecurity risks should never spread beyond a headline. Keep threats off your devices by downloading Malwarebytes today.

  •  

How AI made scams more convincing in 2025

This blog is part of a series where we highlight new or fast-evolving threats in consumer security. This one focuses on how AI is being used to design more realistic campaigns, accelerate social engineering, and how AI agents can be used to target individuals.

Most cybercriminals stick with what works. But once a new method proves effective, it spreads quickly—and new trends and types of campaigns follow.

In 2025, the rapid development of Artificial Intelligence (AI) and its use in cybercrime went hand in hand. In general, AI allows criminals to improve the scale, speed, and personalization of social engineering through realistic text, voice, and video. Victims face not only financial loss, but erosion of trust in digital communication and institutions.

Social engineering

Voice cloning

One of the main areas where AI improved was in the area of voice-cloning, which was immediately picked up by scammers. In the past, they would mostly stick to impersonating friends and relatives. In 2025, they went as far as impersonating senior US officials. The targets were predominantly current or former US federal or state government officials and their contacts.

In the course of these campaigns, cybercriminals used test messages as well as AI-generated voice messages. At the same time, they did not abandon the distressed-family angle. A woman in Florida was tricked into handing over thousands of dollars to a scammer after her daughter’s voice was AI-cloned and used in a scam.

AI agents

Agentic AI is the term used for individualized AI agents designed to carry out tasks autonomously. One such task could be to search for publicly available or stolen information about an individual and use that information to compose a very convincing phishing lure.

These agents could also be used to extort victims by matching stolen data with publicly known email addresses or social media accounts, composing messages and sustaining conversations with people who believe a human attacker has direct access to their Social Security number, physical address, credit card details, and more.

Another use we see frequently is AI-assisted vulnerability discovery. These tools are in use by both attackers and defenders. For example, Google uses a project called Big Sleep, which has found several vulnerabilities in the Chrome browser.

Social media

As mentioned in the section on AI agents, combining data posted on social media with data stolen during breaches is a common tactic. Such freely provided data is also a rich harvesting ground for romance scams, sextortion, and holiday scams.

Social media platforms are also widely used to peddle fake products, AI generated disinformation, dangerous goods,  and drop-shipped goods.

Prompt injection

And then there are the vulnerabilities in public AI platforms such as ChatGPT, Perplexity, Claude, and many others. Researchers and criminals alike are still exploring ways to bypass the safeguards intended to limit misuse.

Prompt injection is the general term for when someone inserts carefully crafted input, in the form of an ordinary conversation or data, to nudge or force an AI into doing something it wasn’t meant to do.

Malware campaigns

In some cases, attackers have used AI platforms to write and spread malware. Researchers have documented campaign where attackers leveraged Claude AI to automate the entire attack lifecycle, from initial system compromise through to ransom note generation, targeting sectors such as government, healthcare, and emergency services.

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.

Looking ahead

AI is amplifying the capabilities of both defenders and attackers. Security teams can use it to automate detection, spot patterns faster, and scale protection. Cybercriminals, meanwhile, are using it to sharpen social engineering, discover vulnerabilities more quickly, and build end-to-end campaigns with minimal effort.

Looking toward 2026, the biggest shift may not be technical but psychological. As AI-generated content becomes harder to distinguish from the real thing, verifying voices, messages, and identities will matter more than ever.


We don’t just report on threats—we remove them

Cybersecurity risks should never spread beyond a headline. Keep threats off your devices by downloading Malwarebytes today.

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The Infostealer Gateway: Uncovering the Latest Methods in Defense Evasion

Blogs

Blog

The Infostealer Gateway: Uncovering the Latest Methods in Defense Evasion

In this post, we analyze the evolving bypass tactics threat actors are using to neutralize traditional security perimeters and fuel the global surge in infostealer infections.

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December 22, 2025

Infostealer-driven credential theft in 2025 has surged, with Flashpoint observing a staggering 800% increase since the start of the year. With over 1.8 billion corporate and personal accounts compromised, the threat landscape finds itself in a paradox: while technical defenses have never been more advanced, the human attack surface has never been more vulnerable.

Information-stealing malware has become the most scalable entry point for enterprise breaches, but to truly defend against them, organizations must look beyond the malware itself. As teams move into 2026 security planning, it is critical to understand the deceptive initial access vectors—the latest tactics Flashpoint is seeing in the wild—that threat actors are using to manipulate users and bypass modern security perimeters.

Here are the latest methods threat actors are leveraging to facilitate infections:

1. Neutralizing Mark of the Web (MotW) via Drag-and-Drop Lures

Mark of the Web (MotW) is a critical Windows defense feature that tags files downloaded from the internet as “untrusted” by adding a hidden NTFS Alternate Data Stream (ADS) to the file. This tag triggers “Protected View” in Microsoft Office programs and prompts Windows SmartScreen warnings when a user attempts to execute an unknown file.

Flashpoint has observed a new social engineering method to bypass these protections through a simple drag-and-drop lure. Instead of asking a user to open a suspicious attachment directly, which would trigger an immediate MotW warning, threat actors are instead instructing the victim to drag the malicious image or file from a document onto their desktop to view it. This manual interaction is highly effective for two reasons:

  1. Contextual Evasion: By dragging the file out of the document and onto the desktop, the file is executed outside the scope of the Protected View sandbox.
  2. Metadata Stripping: In many instances, the act of dragging and dropping an embedded object from a parent document can cause the operating system to treat the newly created file as a local creation, rather than an internet download. This effectively strips the MotW tag and allows malicious code to run without any security alerts.

2. Executing Payloads via Vulnerabilities and Trusted Processes

Flashpoint analysts uncovered an illicit thread detailing a proof of concept for a client-side remote code execution (RCE) in the Google Web Designer for Windows, which was first discovered by security researcher Bálint Magyar.

Google Web Designer is an application used for creating dynamic ads for the Google Ads platform. Leveraging this vulnerability, attackers would be able to perform remote code execution through an internal API using CSS injection by targeting a configuration file related to ads documents.

Within this thread, threat actors were specifically interested in the execution of the payload using the chrome.exe process. This is because using chrome.exe to fetch and execute a file is likely to bypass several security restrictions as Chrome is already a trusted process. By utilizing specific command-line arguments, such as the –headless flag, threat actors showed how to force a browser to initiate a remote connection in the background without spawning a visible window. This can be used in conjunction with other malicious scripts to silently download additional payloads onto a victim’s systems.

3. Targeting Alternative Softwares as a Path of Least Resistance

As widely-used software becomes more hardened and secure, threat actors are instead pivoting to targeting lesser-known alternatives. These tools often lack robust macro-protections. By targeting vulnerabilities in secondary PDF viewers or Office alternatives, attackers are seeking to trick users into making remote server connections that would otherwise be flagged as suspicious.

Understanding the Identity Attack Surface

Social engineering is one of the driving factors behind the infostealer lifecycle. Once an initial access vector is successful, the malware immediately begins harvesting the logs that fuel today’s identity-based digital attacks.

As detailed in The Proactive Defender’s Guide to Infostealers, the end goal is not just a password. Instead, attackers are prioritizing session cookies, which allow them to perform session hijacking. By importing these stolen cookies into anti-detect browsers, they bypass Multi-Factor Authentication and step directly into corporate environments, appearing as a legitimate, authenticated user.

Understanding how threat actors weaponize stolen data is the first step toward a proactive defense. For a deep dive into the most prolific stealer strains and strategies for managing the identity attack surface, download The Proactive Defender’s Guide to Infostealers today.

Request a demo today.

The post The Infostealer Gateway: Uncovering the Latest Methods in Defense Evasion appeared first on Flashpoint.

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How to Design and Execute Effective Social Engineering Attacks by Phone

How to Design and Execute Effective Social Engineering Attacks by Phone

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..

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Gone Phishing: Installing GoPhish and Creating a Campaign

GoPhish provides a nice platform for creating and running phishing campaigns. This blog will guide you through installing GoPhish and creating a campaign. 

The post Gone Phishing: Installing GoPhish and Creating a Campaign appeared first on Black Hills Information Security, Inc..

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Indecent Exposure: Your Secrets are Showing 

by moth Hard-coded cryptographic secrets? In my commercially purchased, closed-source software? It’s more likely than you think. Like, a lot more likely.  This blog post details a true story of […]

The post Indecent Exposure: Your Secrets are Showing  appeared first on Black Hills Information Security, Inc..

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Research that builds detections

Note: You can view the full content of the blog here.

Introduction

Detection engineering is becoming increasingly important in surfacing new malicious activity. Threat actors might take advantage of previously unknown malware families - but a successful detection of certain methodologies or artifacts can help expose the entire infection chain.
In previous blog posts, we announced the integration of Sigma rules for macOS and Linux into VirusTotal, as well as ways in which Sigma rules can be converted to YARA to take advantage of VirusTotal Livehunt capabilities. In this post, we will show different approaches to hunt for interesting samples and derive new Sigma detection opportunities based on their behavior.

Tell me what role you have and I'll tell you how you use VirusTotal

VirusTotal is a really useful tool that can be used in many different ways. We have seen how people from SOCs and Incident Response teams use it (in fact, we have our VirusTotal Academy videos for SOCs and IRs teams), and we have also shown how those who hunt for threats or analyze those threats can use it too.
But there's another really cool way to use VirusTotal - for people who build detections and those who are doing research. We want to show everyone how we use VirusTotal in our work. Hopefully, this will be helpful and also give people ideas for new ways to use it themselves.
To explain our process, we used examples of Lummac and VenomRAT samples that we found in recent campaigns. These caught our attention due to some behaviors that had not been identified by public detection rules in the community. For that reason we have created two Sigma rules to share with the community, but if you want to get all the details about how we identified it and started our research, go to our Google Threat Intelligence community blog.

Our approach

As detection engineers, it is important to look for techniques that can be in use by multiple threat actors - as this makes tracking malicious activity more efficient. Prior to creating those detections, it is best to check existing research and rule collections, such as the Sigma rules repository. This can save time and effort, as well as provide insight into previously observed samples that can be further researched.
A different approach would be to instead look for malicious files that are not detected by existing Sigma rules, since they can uncover novel methodologies and provide new opportunities for detection creation.
One approach is to hunt for files that are flagged by at least five different AV vendors, were recently uploaded within the last month, have sandbox execution (in order to view their behavior), and which have not triggered any Crowdsourced Sigma rules.
p:5+ have:behavior fs:30d+ not have:sigma
This initial query can be adapted to incorporate additional filters that the researcher may find relevant. These could include modifiers to identify for example, the presence of the PowerShell process in the list of executed processes (behavior_created_processes:powershell.exe), filtering results to only include documents (type:document), or identifying communication with services like Pastebin (behavior_network:pastebin.com).
Another way to go is to look at files that have been flagged by at least five AV’s and were tested in either Zenbox or CAPE. These sandboxes often have great logs produced by Sysmon, which are really useful for figuring out how to spot these threats. Again, we'd want to focus on files uploaded in the last month that haven't triggered any Sigma rules. This gives us a good starting point for building new detection rules.
p:5+ (sandbox_name:"CAPE Sandbox" or sandbox_name:"Zenbox") fs:30d+ not have:sigma
Lastly, another idea is to look for files that have not triggered many high severity detections from the Sigma Crowdsourced rules, as these can be more evasive. Specifically, we will look for samples with zero critical, high or medium alerts - and no more than two low severity ones.
p:5+ have:behavior fs:30d+ sigma_critical:0 sigma_high:0 sigma_medium:0 sigma_low:2-
With these queries, we can start investigating some samples that may be interesting to create detection rules.

Our detections for the community

Our approach helps us identify behaviors that seem interesting and worth focusing on. In our blog, where we explain this approach in detail, we highlighted two campaigns linked to Lummac and VenomRAT that exhibited interesting activity. Because of this, we decided to share the Sigma rules we developed for these campaigns. Both rules have been published in Sigma's official repository for the community.

Detect The Execution Of More.com And Vbc.exe Related to Lummac Stealer

title: Detect The Execution Of More.com And Vbc.exe Related to Lummac Stealer
  id: 19b3806e-46f2-4b4c-9337-e3d8653245ea
  status: experimental
  description: Detects the execution of more.com and vbc.exe in the process tree. This behaviors was observed by a set of samples related to Lummac Stealer. The Lummac payload is injected into the vbc.exe process.
  references:
      - https://www.virustotal.com/gui/file/14d886517fff2cc8955844b252c985ab59f2f95b2849002778f03a8f07eb8aef
      - https://strontic.github.io/xcyclopedia/library/more.com-EDB3046610020EE614B5B81B0439895E.html
      - https://strontic.github.io/xcyclopedia/library/vbc.exe-A731372E6F6978CE25617AE01B143351.html
  author: Joseliyo Sanchez, @Joseliyo_Jstnk
  date: 2024-11-14
  tags:
      - attack.defense-evasion
      - attack.t1055
  logsource:
      category: process_creation
      product: windows
  detection:
      # VT Query: behaviour_processes:"C:\\Windows\\SysWOW64\\more.com" behaviour_processes:"C:\\Windows\\Microsoft.NET\\Framework\\v4.0.30319\\vbc.exe"
      selection_parent:
          ParentImage|endswith: '\more.com'
      selection_child:
          - Image|endswith: '\vbc.exe'
          - OriginalFileName: 'vbc.exe'
      condition: all of selection_*
  falsepositives:
      - Unknown
  level: high

Sysmon event for: Detect The Execution Of More.com And Vbc.exe Related to Lummac Stealer

{
  "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": "Microsoft® .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"
  }
} 

File Creation Related To RAT Clients

title: File Creation Related To RAT Clients
  id: 2f3039c8-e8fe-43a9-b5cf-dcd424a2522d
  status: experimental
  description: File .conf created related to VenomRAT, AsyncRAT and Lummac samples observed in the wild.
  references:
      - https://www.virustotal.com/gui/file/c9f9f193409217f73cc976ad078c6f8bf65d3aabcf5fad3e5a47536d47aa6761
      - https://www.virustotal.com/gui/file/e96a0c1bc5f720d7f0a53f72e5bb424163c943c24a437b1065957a79f5872675
  author: Joseliyo Sanchez, @Joseliyo_Jstnk
  date: 2024-11-15
  tags:
      - attack.execution
  logsource:
      category: file_event
      product: windows
  detection:
      # VT Query: behaviour_files:"\\AppData\\Roaming\\DataLogs\\DataLogs.conf"
      # VT Query: behaviour_files:"DataLogs.conf" or behaviour_files:"hvnc.conf" or behaviour_files:"dcrat.conf"
      selection_required:
          TargetFilename|contains: '\AppData\Roaming\'
      selection_variants:
          TargetFilename|endswith:
              - '\datalogs.conf'
              - '\hvnc.conf'
              - '\dcrat.conf'
          TargetFilename|contains:
              - '\mydata\'
              - '\datalogs\'
              - '\hvnc\'
              - '\dcrat\'
      condition: all of selection_*
  falsepositives:
      - Legitimate software creating a file with the same name
  level: high

Sysmon event for: File Creation Related To RAT Clients

{
  "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"
  }

Wrapping up

Detection engineering teams can proactively create new detections by hunting for samples that are being distributed and uploaded to our platform. Applying our approach can benefit in the development of detection on the latest behaviors that do not currently have developed detection mechanisms. This could potentially help organizations be proactive in creating detections based on threat hunting missions.
The Sigma rules created to detect Lummac activity have been used during threat hunting missions to identify new samples of this family in VirusTotal. Another use is translating them into the language of the SIEM or EDR available in the infrastructure, as they could help identify potential behaviors related to Lummac samples observed in late 2024. After passing quality controls and being published on Sigma's public GitHub, they have been integrated for use in VirusTotal, delivering the expected results. You can use them in the following way:
Lummac Stealer Activity - Execution Of More.com And Vbc.exe
sigma_rule:a1021d4086a92fd3782417a54fa5c5141d1e75c8afc9e73dc6e71ef9e1ae2e9c
File Creation Related To RAT Clients
sigma_rule:8f179585d5c1249ab1ef8cec45a16d112a53f91d143aa2b0b6713602b1d19252
We hope you found this blog interesting and useful, and as always we are happy to hear your feedback.
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