# All About Stealer Log and Cyber Threat
## All About Stealer Log and Cyber Threat
After I wrote the article and made changes to my resources on GitHub, many people asked about data breaches, log stealers, log formats, doxing, tracking, and cyber threats, as well as other information regarding threat actors. In this article, I will discuss several points, such as what a log stealer is, where the data comes from, and cyber threat intelligence
### Stealer log what is?
A stealer log is a file created by a type of malware (a harmful program) that secretly collects personal information from a person’s device. In simple terms: A stealer log is like a hidden report made by a virus. It gathers things like saved passwords, login details, browser history, and sometimes files or payment information from your computer or phone. Then, this information can be sent to a hacker without you knowing. So, if someone talks about a “stealer log,” they usually mean a collection of stolen personal data taken from an infected device.
There are many types of data stealers, such as LummaC2, Raccoon, RedLine, and others. This malware is designed to steal your data, including saved browser credentials, active cookies, and even your search history and autofill data. Additionally, some malware includes a C2 server panel (depending on the malware). Each piece of malware and data stealer has its own unique characteristics in how it operates.
#### Where stealer data come from?
The data in a stealer log comes from a device that has been infected with malware. This usually happens when someone downloads or installs unsafe files, such as cracked software, fake activators (keygens), game mods from unofficial sources, or files from untrusted websites. Once the malware is installed, it runs silently in the background without the user noticing. It starts collecting personal information from the device, such as saved passwords, login details, browser data, and sometimes even payment information or files. All of this stolen data is then gathered into a file called a “stealer log,” which can be sent to a hacker or stored for later misuse.
#### Format data stealer log
#### 1. ULP (URL:Login:Password) — Most Common
```
https://accounts.google.com user@gmail.com MyPassword123
```
Tab or pipe-separated. Used by Vidar, LummaC2, RedLine, StealC, StarLink, Santa. The dominant format across all families. Our parser handles multi-colon edge cases (passwords containing `:`) through right-to-left field parsing.
#### 2. Multiline Block Format
```
Host: accounts.google.com
URL: https://accounts.google.com/signin
Username: user@gmail.com
Password: MyPassword123
Application: Chromium
```
Key-value blocks separated by blank lines or dashes. Observed in RedLine, Vidar (some variants), and StealC logs. Block headers may use `Soft:`, `Host:`, `URL:`, or `Browser:` as the leading field. The `Browser:` variant (observed in Xeno/RedLine machines) includes `Profile:` and uses `===============` as block separator instead of blank lines.
#### 3. TSV (Tab-Separated Values)
```
https://example.com user@email.com password123
```
Used specifically by Phemedrone and Snatch in their `passwords.tsv` files.
#### 4. JSON Cookies
```
[{"domain":".google.com","name":"SID","value":"abc123","path":"/","secure":true}]
```
Browser cookie data in JSON arrays. Also observed as Netscape/Mozilla TSV cookie format in some families.
#### 5. Combolist (user:pass)
```
user@email.com:password123
```
Colon-separated email/user:password pairs without URLs. Common in ULP redistribution files (`@zelenkalink`, `VIP@Elite_Cloud`) where logs are processed into stripped credential lists.
#### 6. Pipe-Separated with Section Headers
```
PASSWORDS FROM: C:\Users\ASUS\AppData\Local\Google\Chrome\User Data\Profile 4\
URL: https://fanciclub.com/my-account/ | USERNAME: lynn | PASSWORD: Z4$S$ZfozL(!i2igFrZiXM#m
PASSWORDS FROM: C:\Users\ASUS\AppData\Local\Microsoft\Edge\User Data\Default\
URL: https://example.com/ | USERNAME: admin | PASSWORD: password123
```
Pipe-separated (`|`) credentials with `PASSWORDS FROM:` section headers showing the full browser profile AppData path. Observed in `[UN]@BRADMAX` numbered machines.
#### 7. Banner-Separated Blocks (BlankGrabber)
```
==================Blank Grabber===================
URL: https://accounts.google.com/
Username: user@gmail.com
Password: MyPassword123
```
ULP blocks separated by `==================Blank Grabber===================` banner lines. Unique to BlankGrabber.
#### 8. StealC Multiline Format
```
browser: Google Chrome
profile: Default
url: https://accounts.google.com/
login: user@gmail.com
password: MyPassword123
```
Key-value blocks with lowercase `browser:`, `profile:`, `url:`, `login:`, `password:` keys. Distinct from the generic multiline format (which uses capitalized keys).
#### 9. CIDULP TSV (Country:IP:Date:URL:Login:Password)
```
AE 151.253.243.245 2026-03-15 accounts.binance.com user@gmail.com Password123
```
Six tab-separated fields: country code, victim IP, date, URL/domain, login, password. Produced by `@logstester` as pre-enriched credential dumps with geographic metadata. Files follow `mix_YYYYMMDD_cidulp.tsv` naming convention. Unlike standard TSV (3 fields), CIDULP carries machine-level metadata that would otherwise require cross-referencing with the original machine logs.
### Parser Design Observations
* **Format auto-detection**: The parser samples the first N lines to determine format, then delegates to the appropriate specialized parser
* **Deduplication**: Bounded in-memory dedup set (100K entries) prevents duplicate credential indexing while maintaining constant memory usage
* **Multi-colon handling**: Passwords containing `:` characters require parsing from right-to-left to correctly separate URL:user:pass fields
### Threat actor what is?
A threat actor is a person or a group that carries out harmful activities in the digital world. This can include hackers, cybercriminals, or even organizations that try to steal data, spread malware, or attack computer systems. Their goal can be to make money, steal information, or cause damage. In simple terms, a threat actor is anyone behind a cyber attack or malicious activity. Threat actor is the individual or group responsible for an attack or malicious activity on the internet.
#### Type of threat actor
There are different types of threat actors, depending on their goals and background.
1. **State-sponsored actors** are groups supported by a government. They usually carry out cyber attacks for espionage, political goals, or national security purposes.
2. **Cybercriminals** are individuals or organized groups that attack systems to make money, for example by stealing data, selling information, or running scams.
3. **Hacktivists** are attackers who are motivated by political or social causes. They often target organizations to spread a message or protest.
4. **Insiders** are people who already have access to a system, such as employees or contractors, and misuse that access for harmful purposes.
5. **Script kiddies** are less experienced individuals who use ready-made tools or scripts created by others to carry out attacks without fully understanding how they work.
In simple terms, threat actors can be anyone—from beginners to highly organized groups—who perform malicious activities in the digital world.
### What is malware?
Malware (malicious software) is a harmful program designed to damage a device, steal data, or take control of a system without the user’s permission.
#### 1. Basic malware
This is the most common type of malware.
* It usually enters a device through unsafe downloads, suspicious emails, or cracked software
* It requires user action, such as clicking or installing a file
* Examples include viruses, trojans, and spyware
**Simple example**\
You download a “crack” file, and after opening it, it secretly steals your passwords.
#### 2. Phishing-based attacks
* These attacks trick users into giving their own information
* They usually come through emails, fake websites, or chat messages
* They still require user interaction
**Example**\
You click a fake login link and enter your username and password, and the data is immediately stolen.
#### 3. Exploit-based malware
* This type takes advantage of security vulnerabilities (bugs) in systems or applications
* It may not require much interaction, sometimes just opening a file or visiting a website is enough
**Example**\
Simply opening a PDF file or visiting a certain website can infect the device due to a security flaw.
#### 4. Fileless malware
* It does not leave obvious files on the system
* It runs directly in memory (RAM)
* It is harder for antivirus software to detect
#### 5. Advanced Persistent Threat (APT)
* These are high-level attacks, often carried out by organized groups or state-sponsored actors
* They target specific organizations, such as companies or governments
* They can remain undetected for a long time
#### 6. Zero-click malware
This is one of the most advanced and dangerous types.
* It does not require any user interaction
* A device can be infected just by receiving a message, call, or certain data
* It exploits very specific and advanced security vulnerabilities
**Simple example**\
You receive a message in a chat app, and without opening it, your device is already infected.
### How stealer log dump your data?
In short, if your device has been infected by a stealer log—whether through cracked software, browser extensions, Visual Studio Code extensions, or programming packages such as npm, Python, and others—once the program runs, your device becomes compromised.
The malware will start collecting your data. Some of these programs are persistent, meaning they can remain on your system even after you restart your device and will automatically run again.
Even though browsers use encryption to store saved passwords, this protection can still be bypassed. The malware can decrypt the stored data, allowing attackers to see the passwords saved in your browser.
Based on my research in IT security and malware, as well as creating simple malware and stealer logs, this is generally how such programs work:
#### 1. Injection into software or packages
The malicious code is inserted into software, such as cracked programs, browser extensions, or programming libraries.
If you understand reverse engineering, it becomes easier to analyze how this works. However, without that knowledge, it can be very difficult to detect how the malware operates.
In simple terms, the malicious code is hidden inside software, packages, or even running processes on your device.
#### 2. HTTP requests for data exfiltration
Once the malware runs and collects data, it sends that information to the attacker using HTTP requests.
This can be done through a command-and-control (C2) server or through services like Telegram webhooks and similar platforms.
As a result, the attacker can receive your data. In some cases, they can also remotely control the device, such as taking screenshots, deleting files, executing commands, and performing other actions.
#### 3. Formating and checker
The next step is to format the data, for example, into ULP format. Then, verify whether the username and password (credentials) are valid. Check if the account is registered and still active, as well as the cookies and other information.
Malware creators use a variety of techniques depending on the attacker’s operational security (OPSEC), so your role is to understand their TTPs (Tactics, Techniques, and Procedures) using references such as MITRE and to perform reverse engineering
## What Should You Do?
If your system has been compromised, the first step is to conduct a security audit. Change your password, enable 2FA, run a virus scan, and monitor the processes running on your laptop. I also recommend reinstalling the operating system so your laptop is like new. Additionally, do not store your passwords in documents or your browser; instead, use a third-party password manager like KeePass or a similar app. For added protection, enable your firewall and antivirus software, and avoid using pirated software, untrusted packages, or unverified information.
#### Monitoring
If you want to check whether your data has been compromised, here are a few tips you can follow
#### Underground Forums
Hacking forums where threat actors trade tools, techniques, and stolen data. Key activities include:
* Sale of stolen credentials and databases
* Malware-as-a-Service (MaaS) offerings
* Exploit trading and vulnerability disclosure
* Recruitment for cybercrime operations
#### Marketplaces
Dark web marketplaces for:
* Stolen data (credentials, PII, financial)
* Access brokers selling corporate network access
* Fraud tools (carding, identity theft)
#### Telegram Channels
Increasingly used for:
* [Stealer log](https://cypherdynamics.com/research/Stealer-Logs) distribution
* Combolists and credential dumps
* Real-time threat actor communication
* Malware sample sharing
#### Check Document Leak
* scribd
* document cloud
* pastebin
* telegra
Or available site, for further information you can check the main Jieyab89 repo
#### Dark Web Monitoring
Dark web monitoring involves the systematic collection and analysis of data from underground forums, marketplaces, and encrypted messaging platforms to identify threats to organizations and individuals.
#### Monitoring Approach
1. **Source Discovery** — Identify relevant forums, channels, and marketplaces
2. **Account Establishment** — Create and maintain personas for access
3. **Automated Collection** — Deploy collectors for continuous monitoring
4. **Alerting** — Real-time notifications for organizational mentions
5. **Analysis** — Contextualize findings within the broader threat landscape
## Stealer Log Distribution Channels
he following distribution channels were identified through direct inspection of archive filenames, directory structures, and embedded branding.
Part of the [Infostealer Field Research](https://cypherdynamics.com/research/Infostealer-Field-Research) series. See also: [Stealer Family Profiles](https://cypherdynamics.com/research/Stealer-Family-Profiles), [Archive Inventory](https://cypherdynamics.com/research/Archive-Inventory).
### Archive Naming Patterns
| Archive Pattern | Observed Source | Content |
| -------------------------------------------- | ---------------------------------- | ---------------------------------------------------------------------- |
| `burn DD MM.zip` | Direct panel export | Vidar logs with `VIDAR STEALER` banner |
| `standart XXX pcs DD.MM.YY.zip` | Standard log packs | Vidar-format logs, machine count in filename |
| `@zelenkalink NN.rar/.zip` | Zelenka marketplace redistribution | Primarily ULP combolists |
| `Private Russia 34 - DD.MM.rar` | Russia34 panel | Vidar logs repackaged under Russia34/Santa branding |
| `Private Russia 34 - N.N.7z` | Russia34 panel | Mixed Vidar (\~80%) + Santa (\~20%) logs |
| `Private_StarLink_XXXX pcs.rar` | StarLink cloud | StarLink-family logs with `[Country]IP` folders |
| `[Private] @CartelJohnDoe.partN.rar` | Telegram reseller | Mixed logs aggregated from multiple families |
| `@BRADMAX 19000 JAN-FEB.zip` | Multi-family aggregation | StealC (\~13K) + Vidar (\~2K) + Snatch + BlankGrabber + cookie-grabber |
| `au244.zip`, `eb161.zip`, etc. | BUYINSTALLS panel | Vidar v18.2/18.3 with BUYINSTALLS ASCII banner |
| `vidar_YYYYMMDD_private*.zip` | @logstester reseller | Zip-of-zips: individual machine zips, flat inner structure |
| `@HUNTER_CLOUDS PREMIUM LOGS DD-MM-YYYY.zip` | @HUNTER\_CLOUDS (PREMIUM) | Multi-family: Vidar + ShapeShifter + Snatch + Katz. ZipCrypto |
| `@HUNTER_CLOUDS VIP LOGS PART N.zip` | @HUNTER\_CLOUDS (VIP) | Multi-family. AES-256 encrypted |
| `CRONCLOUD(VIP)NNNN.zip` | CRONCLOUD panel | Vidar v16.1 logs. \~83 machines per batch. Sequential numbering |
| `43c2607abf8d57027dbd30c7d582a766.zip` | MD5-named archives | LummaC2 logs. Crypto-tagged: `[MetaMask]`, `[Binance]` suffixes |
| `@LulzsecCloudLogs_NNNN.rar` | @LulzsecCloudLogs | 610 parts, \~780K+ machines. Obfuscated timestamps |
| `Crypto.rar` / `CryptoN.rar` | Crypto-filtered redistribution | RedLine logs pre-filtered for crypto wallet holders |
| `17K Pcs - @Xeno_Logs_.7z.001/.002` | @Xeno\_Logs channel | Mixed Vidar/StealC/RedLine. Password-protected 7zAES |
| `ArtHouse Cloud Logs Feb.zip` / `vN.zip` | ArtHouse Cloud panel | Multi-family stealer logs |
| `mix_YYYYMMDD_cidulp.tsv` | @logstester CIDULP exports | Pre-enriched credential TSV with country/IP/date metadata |
| `snow*priv*.txt` / `snowpriv*.txt` | SnowStealer ULP channel | ULP credential dumps |
| `sunulp*.txt` / `@sunulp*.txt` | @sunulp Telegram channel | ULP credential dumps |
| `Zulu traffic XXXX pcs.rar` | Zulu traffic | Stealer log archives with machine count |
| `forza.z01` | Forzatraffic | Split ZIP, \~20K machines per archive |
### Channel Types Observed
#### 1. Direct Panel Exports
Archives produced directly by stealer panels (e.g., `burn` archives with Vidar branding). Raw machine logs with consistent internal structure.
#### 2. Redistribution Panels
Operators who collect logs from one family and rebrand/repackage them (e.g., Russia34 redistributing Vidar logs under Santa branding). Creates attribution complexity — the archive branding doesn’t always match the underlying stealer family.
#### 3. Telegram Resellers
Aggregators who collect logs from multiple sources and distribute via Telegram (e.g., `@CartelJohnDoe`). Archives contain mixed-family logs since the reseller is family-agnostic.
#### 4. ULP Processors
Channels that strip raw machine logs down to credential-only combolists (e.g., `@zelenkalink`, `VIP@Elite_Cloud`, `@sunulp`, `SnowStealer`). The original machine context is discarded. A newer variant (`@logstester` CIDULP) preserves machine metadata (country, IP, date) alongside credentials in a 6-field TSV format.
#### 5. Multi-Family Aggregators
Large-scale operators who aggregate logs from multiple stealer families, applying their own folder naming scheme (e.g., `@BRADMAX` using `[CC]IP` format for all machines regardless of family). The largest archives observed — 19,000+ machines, 29.7 GB, spanning 5+ families.
See [BRADMAX Archive Analysis](https://cypherdynamics.com/research/BRADMAX-Archive-Analysis) for a detailed breakdown.
#### 6. Install Marketplace Panels
Operators selling “installs” (malware deployments) who use branded stealer panels with promotional banners. BUYINSTALLS (`labinstalls.info/bot`) uses short random alphanumeric archive names and standard Vidar format with ASCII art advertising.
#### 7. Per-Machine Zip-of-Zips Resellers
Operators who repackage individual machine logs as separate zip files within an outer archive. @logstester distributes Vidar logs as `vidar_YYYYMMDD_CC_IP.zip` files inside dated archives. Inner zips have flat structure (no machine folder wrapper).
#### 8. Automated Cron-Based Panels
CRONCLOUD operates an automated collection panel with sequential batch numbering (observed: 1873-2099+, 200+ batches). Each batch zip contains \~83 individually-zipped machine folders. All sampled logs are Vidar v16.1 with the Russian banner variant.
#### 9. Crypto-Filtered Redistribution
Archives containing logs pre-filtered to only include machines with crypto wallet data. The `Crypto*.rar` (RedLine) and MD5-named archives (LummaC2) represent this distinct market segment. MD5-named archives include wallet-type tags in folder names (e.g., `[BR][MetaMask]189.34.224.38`). Confirms a specialized supply chain: stealer operators → bulk log sellers → crypto-filtered resellers → buyers.
#### 10. Massive Multi-Part Stealer Clouds
@LulzsecCloudLogs distributes logs from a previously undocumented lightweight stealer across 610 multi-part RAR archives (\~780,000+ total machines). Uses obfuscated timestamps (invalid hours/minutes/seconds). The stealer itself lacks system info files — lightweight browser-data-only focus.
#### 11. Split 7z Multi-Family Redistribution
@Xeno\_Logs distributes mixed-family logs via split 7z archives with password-protected inner containers (7zAES). 17,872 machines with Vidar (\~84%), StealC (\~7%), and RedLine (\~4%). All text files contain invisible `Xeno_Logs` watermark strings.
#### 12. Tiered Multi-Family Panels
@HUNTER\_CLOUDS operates a two-tier distribution panel (PREMIUM + VIP), both password-protected. Both tiers aggregate 4+ stealer families. Notable feature: each machine folder in PREMIUM archives contains a **random English word .txt file** (e.g., `osteochondroma.txt`) — likely a tracking token per machine.
#### 13. Nested Aggregator Clouds
The `@cvv190_cloud2.zip` archive demonstrates 3-layer packaging: Darkside Cloud (outer) > Legion Cloud 2 (middle) > actual stealer data (inner). A single archive contains 4+ distinct stealer families. See [Observed Resellers & Aggregators](https://cypherdynamics.com/research/Stealer-Family-Profiles#observed-resellers--aggregators) for details.
### Attribution Complexity
A critical observation from our analysis: **archive branding frequently does not match the underlying stealer family.** For example:
* `Private Russia 34` archives contain logs that classify as Vidar by structure and banners
* `@CartelJohnDoe` archives contain a mix of families
* `@zelenkalink` archives are processed ULP text, making family attribution impossible
* `@BRADMAX` repackages StealC, Vidar, Snatch, BlankGrabber, and cookie-grabber logs under a unified `[CC]IP` naming scheme
* `@Xeno_Logs` aggregates Vidar, StealC, and RedLine logs with Xeno\_Logs watermarks injected — each family retains its own machine folder naming and log structure
This means any analysis based solely on archive names or channel branding will produce incorrect family attribution. Our multi-priority [classification system](https://cypherdynamics.com/research/Stealer-Classification-System) was specifically designed to look at the log content itself, not the packaging.
For further information you can check main repo Jieyab89 for CTI like deepdark CTI resouces and other
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## Conclusion
Now that you understand the basics of stealers, malware, and cyber threats, here are a few recommendations from me: you should be cautious with documents or programs you don’t trust. I also suggest you learn about programming languages and basic IT security awareness to prevent hacking incidents and minimize human error. Indeed, cyberattacks can come from anywhere and at any time—for example, from 0-day exploits, data leaks from third parties, or advanced malware like zero-click attacks.
If you want to learn all of this, it will take a long time—especially regarding zero-click malware—and it’s not an easy task. However, the only way to protect yourself from cyberattacks is to learn about IT security awareness. This serves as the foundation: for instance, by understanding how hackers operate, identifying malicious programs, and gaining some knowledge of programming, basic networking, and HTTP. This will form a stronger foundation for your security.
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