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CaptiveCrunch: Midnight Blizzard targets travelers worldwide for malware delivery and credential theft

Since early May 2026, Microsoft Threat Intelligence has observed Storm-2945, a sub-cluster of Midnight Blizzard, conducting widespread but targeted traffic manipulation attacks involving hospitality sector networks served by captive portals worldwide. Despite some tactic, technique, and procedure (TTP) similarities to the Forest Blizzard DNS hijacking operation that we publicly disclosed in April 2026, we attribute this campaign, which we call CaptiveCrunch, to Storm-2945. As reported by ReliaQuest on July 23, a portion of this activity leverages doppelganger domains mimicking Microsoft online services to conduct follow-on adversary-in-the-middle (AitM) phishing operations that abuse the device code authentication flow in Microsoft Entra ID. Microsoft Threat Intelligence has also identified active traffic manipulation attacks leading to the delivery of malware on impacted systems. Microsoft has observed Storm-2945 leveraging AI to support a significant portion of these operations.

Today, we are sharing our findings on these ongoing intrusions to raise awareness of this threat and enable customers to protect their devices, especially while traveling. We provide our assessment of Storm-2945’s relationship to Midnight Blizzard and analysis of the CaptiveCrunch campaign, detailing the malware and tradecraft used in these operations. We also provide mitigation, detection, and hunting guidance to help organizations identify and defend against Storm-2945 and related activity.

Microsoft Threat Intelligence would like to thank our partners at Anthropic and OpenAI for their collaboration and support during this investigation.

The CaptiveCrunch campaign

Since February 2026, Storm-2945 has conducted AI-augmented operations including targeted device code and OAuth code phishing campaigns leading to Entra device registration and subsequent data collection from Microsoft 365. Since early May 2026, Microsoft Threat Intelligence has observed Storm-2945 manipulating DNS and HTTP traffic from networks served by captive portals to redirect user traffic through actor-controlled infrastructure. Although our investigation into the initial compromise vector for the captive portal networks is ongoing, we have observed notable commonalities in the equipment and management systems used across multiple affected networks. These similarities suggest that the activity might not be limited to isolated compromises of individual venues and could reflect access to shared services within portions of the captive portal ecosystem.

Diagram depicting an overview of the CaptiveCrunch campaign attack flow
Figure 1. Overview of the CaptiveCrunch attack flow

As part of the CaptiveCrunch campaign, Storm-2945 has leveraged their AitM position to redirect users through actor-controlled phishing infrastructure and has also delivered malware purporting to be browser or operating system updates in response to automated connectivity checks issued by users’ browsers. Multiple variants have been delivered, including fully-featured Windows remote access trojans (RAT) in compiled Golang, with functionality to conduct system enumeration, collect files and keystrokes, steal credentials and session tokens, conduct audio and video surveillance, monitor for removable media, and provide the threat actor a remote shell on infected systems.  

The threat actor infrastructure leverages a variety of ClickFix techniques to elicit the user into downloading and executing the malware:

A Windows Driver Repair Utility interface, with instructions for manually repairing a failed automated driver repair, including steps to run a verification script via Windows Terminal.
Figure 2. ClickFix prompt with manual user instructions
A Google web page claiming the verification check failed with additional manual instructions for the user to follow.
Figure 3. ClickFix prompt with additional user instructions after verification failure

In addition to variants of malware targeting Windows systems, Microsoft Threat Intelligence is also aware of indications that the threat actor might be targeting Android devices with similar techniques as the ClickFix landings also include instructions for Android devices to download and install an APK file.

To date, Microsoft has identified widespread compromise of Wi-Fi networks at hospitality-related organizations and other networks serviced by captive portal equipment in several countries. ReliaQuest has identified this activity not only at hotels, but also conference centers and other shared venues, and assesses that the goal of this activity is to access the accounts of corporate travelers.

Storm-2945 and Midnight Blizzard

Microsoft Threat Intelligence assesses that Storm-2945 is an operational sub-cluster of Midnight Blizzard based on distinctive technical and operational overlaps. These include technical similarities to Storm-2372, a Midnight Blizzard initial access operations sub-cluster, also notable for their device code and OAuth code phishing operations tracked throughout 2025, Microsoft Graph-based email exfiltration, social engineering delivered via commercial messaging apps, and significant similarities in victimology.

Midnight Blizzard is a Russia-based threat actor attributed by the US and UK governments to the Foreign Intelligence Service of the Russian Federation, also known as the SVR. This threat actor is known to primarily target governments, diplomatic entities, non-governmental organizations (NGOs), and information technology (IT) service providers, primarily in the US and Europe. Midnight Blizzard is consistent and persistent in their operational targeting, and their objectives rarely change. Their focus is to collect intelligence through longstanding and dedicated espionage in support of Russian foreign policy interests.

Midnight Blizzard operations often involve compromise of valid accounts and, in some highly targeted cases, advanced techniques to compromise authentication mechanisms within an organization to expand access and evade detection. They utilize diverse initial access methods, and Midnight Blizzard is also adept at identifying and abusing OAuth applications to move laterally across cloud environments and for post-compromise activity, such as email collection.

CaptiveCrunch tradecraft and tooling

CornFlake: Remote access and infostealer implant

CornFlake is a full-featured Windows RAT written in Go that serves as Storm-2945’s primary persistent implant. Microsoft has observed the threat actor rapidly iterating on this malware layer, which features customizable capabilities from the social engineering user interface and data collection capabilities to anti-detection and evasion techniques.

On initial execution, CornFlake operates in dropper mode: it displays a convincing fake progress window designed to occupy the victim’s attention while the binary copies itself to %APPDATA%\svchost32\svchost32.exe and establishes persistence.

Fake window options configurable by the threat actor at build time:

  • winupdate — A Windows Update screen displaying “Working on updates… Don’t turn off your computer”
  • defender — A Windows Security virus scan
  • directx — A DirectX End-User Runtime Web Installer
  • vcredist — A Microsoft Visual C++ 2015-2022 Redistributable installer
  • sysopt — A disk optimization utility
  • netfix — A Windows Network Diagnostics tool
  • browser — A browser update prompt
  • pdfview — A document viewer installer
A false update window claiming the updates are 3 percent downloaded.
Figure 4. False update window

CornFlake registers as a Windows service named svchost32 with the display name “Cloud Sync Service and description “Synchronizes files with the cloud storage provider”, deliberately mimicking the legitimate svchost.exe process. It establishes redundant persistence mechanisms: Windows service registrations, Registry Run keys, named scheduled tasks, and a persistence watchdog routine that runs continuously to restore any persistence mechanism that is removed by defenders or endpoint protection.

For command and control (C2), CornFlake performs an Elliptic Curve Diffie-Hellman (ECDH) P-256 ephemeral key exchange with the C2 server, derives a session key via SHA-256, and communicates over a custom JSON protocol framed within the encrypted channel. This provides an encrypted channel to the C2 server, with each C2 session using a unique ephemeral key, making decryption of captured traffic impossible without the session-specific private key. The runtime configuration file sync.dat supports hot reconfiguration of C2 servers, watched directories, file targeting patterns, and Transport Layer Security (TLS) settings without requiring redeployment.

Once established on a victim system, CornFlake provides the operator with a comprehensive collection toolkit, gated by configuration flags that allow selective activation post-deployment:

CapabilityDescription
KeyloggingRaw input API-based keylogger capturing all keystrokes, including password fields
Clipboard monitoringCaptures clipboard changes with SHA-256 deduplication and records the active window title at time of capture
Screenshot captureIdle-triggered and on-demand screenshots with configurable idle threshold
Audio surveillanceWindows Audio Session API (WASAPI)-based microphone capture, encoded as WAV files
Video surveillanceMedia Foundation-based webcam capture, encoded as JPEG
Browser credential theftChromeKatz-derived module supporting live cookie extraction from process memory (Chromium browsers) and stored password extraction from on-disk databases, including Chrome App-Bound Encryption (ABE) bypass and Firefox NSS/SDR decryption
File exfiltrationTargets files based on file extensions with real-time file system monitoring and an upload throttle (1,000 files or 500 MB per cycle). File extensions are categorized as Documents, Archives, Images, Code, Data, Emails, and Keys
USB drive monitoringDetects and scans removable media when inserted
Security posture sweepCollects 18 categories of host intelligence including installed software, antivirus (AV)/endpoint detection and response (EDR) products, Defender exclusions, User Account Control (UAC) level, Remote Desktop Protocol (RDP) history, Office most recently used (MRU) files, and credential hints
Remote shellArbitrary command execution via cmd.exe or PowerShell (with -NoP flag to suppress profile-based detection)

CornFlake also exposes a localhost HTTP API server (/upload, /reload, /status) that transforms the RAT into a modular platform: companion or next-stage payloads such as ChocoShell could task file exfiltration, trigger configuration hot reloads or check C2 connectivity using the pre-established secure C2 channel for communication.

ChocoShell: PowerShell infostealer

ChocoShell is the campaign’s Powershell-based infostealer, delivered and executed entirely in-memory. Its primary objective is the high-volume theft of browser session cookies, saved passwords, Microsoft 365 Single Sign-On (SSO) tokens, and Wi-Fi credentials from compromised systems. Where CornFlake provides the operator with a persistent, long-running foothold on the device, ChocoShell is designed to extract the most operationally valuable credentials, giving the operator access to victim cloud environments.

The ChocoShell script was authored with full developer comments that reveal the operator’s intent behind each code decision, including explicit references to Microsoft detection signatures and the reasoning behind specific evasion choices. The consistent coding standard and descriptive commentary suggest the author might have leveraged AI-assisted code generation.

Defense evasion. Upon execution, ChocoShell beacons to a hardcoded C2 server at 213.145.86[.]112 and implements several evasion techniques in sequence. It disables the Antimalware Scan Interface (AMSI) via .NET reflection to prevent ScriptBlock scanning and evades Microsoft behavioral detection that triggers on suspicious PowerShell web request cmdlets. A timing-based sandbox detection check is also employed as a virtual machine (VM) detection mechanism, silently exiting without performing any collection if detected.

C2 communication. ChocoShell communicates with its C2 server using HTTPS with URI paths designed to blend in with legitimate web traffic. Beacons use /t/pixel.gif?m=<status>, mimicking an image tracking pixel. Additional tooling is fetched from /cdn/chunks/polyfill-7e2b.min.js, disguised as a JavaScript polyfill file. This downloaded module is Base64-decoded and executed in memory via [ScriptBlock]::Create(), providing browser encryption key extraction capabilities, SYSTEM token impersonation, and Defender signature locking. Exfiltrated data is sent by POST to /t/event as GZip-compressed, Base64-wrapped JSON.

Privilege escalation. ChocoShell requires administrative privileges for its most impactful capabilities: SYSTEM token impersonation for Chrome ABE decryption, Volume Shadow Copy Service (VSS) shadow copy creation, Defender signature locking. It implements three silent UAC bypass techniques with ordered fallback:

  1. SilentCleanup task hijack: Writes a malicious command to HKCU\Environment\windir, then triggers the built-in SilentCleanup scheduled task, which resolves %windir% from the user’s environment, executing the threat actor’s command at elevated privilege. The registry value is cleaned up after two seconds to avoid cloud detection.
  2. wsreset.exe COM hijack: Creates a COM handler key in HKCU\Software\Classes and launches the auto-elevating Windows Store reset tool.
  3. sdclt.exe folder hijack: Hijacks HKCU\Software\Classes\Folder\shell\open\command and launches the Windows Backup utility with the /KickOffElev flag.

If none of the silent bypasses succeed (for example, the user is not a local administrator), ChocoShell falls back to a visible UAC prompt via Start-Process -Verb RunAs. Notably, the script also contains a variant designed to execute within the WinGet Desired State Configuration (DSC) host process (ConfigurationRemotingServer), suggesting an attack vector through malicious WinGet DSC configuration used in Windows machine provisioning.

Credential and session theft. Once running with elevated permissions, ChocoShell locks Defender signature updates and systematically harvests data from multiple sources. For Chromium-based browsers (Chrome, Edge, Brave, Opera, Opera GX, Vivaldi), it extracts the master encryption key from the browser’s Local State file, handling both the modern ABE scheme (Chrome v127+) and the legacy data protection API (DPAPI)-only scheme. ABE decryption requires SYSTEM-level DPAPI access, which the malware obtains by impersonating a SYSTEM process token borrowed from winlogon.exe, wininit.exe, or services.exe. Locked browser SQLite databases are accessed through three strategies: shared file access, Volume Shadow Service snapshots, and direct copy as a fallback.

As a parallel collection path, ChocoShell launches Chrome, Edge, and Brave with the –remote-debugging-port flag and issues Network.getAllCookies through the Chrome DevTools Protocol (CDP). This completely bypasses ABE, enabling the browser to perform its own internal decryption and returns plaintext cookie values. To handle privilege issues (SYSTEM-launched browsers inherit the wrong token), the malware creates transient scheduled tasks with TASK_LOGON_INTERACTIVE_TOKEN to launch the browser under the signed-in user’s session. After extraction, the browser is stopped and relaunched with –restore-last-session to avoid alerting the user.

For Firefox family browsers (Firefox, Waterfox, LibreWolf, Floorp, Zen), the malware copies unencrypted cookies.sqlite databases from each profile. Additionally, ChocoShell collects Microsoft 365 and Azure Active Directory (AD) access tokens, refresh tokens, and Web Account Manager (WAM) tokens from .tbres files in the Token Broker cache. Collection of these tokens represents a significant threat to enterprise environments, as threat actors could replay SSO sessions without browser cookies. Additionally, Wi-Fi credentials are harvested via netsh wlan show profile with key=clear.

Exfiltration and cleanup. All collected data is aggregated into a JSON structure, GZip-compressed, Base64-encoded, and sent by POST to the C2’s /t/event endpoint. After exfiltration, all collected data variables are nulled, garbage collection is forced, VSS shadow copies are deleted via Windows Management Instrumentation (WMI), temporary elevation scripts are removed, and all UAC bypass registry keys (already cleaned during escalation) are verified removed.

FruitStone: Operator C2 panel

FruitStone is the web-based C2 panel that Storm-2945 operators use to manage the entire CaptiveCrunch campaign infrastructure. Implemented as a single-page application (HTML and JavaScript) serving as the front-end of the C2 server with all functionality exposed without authentication, FruitStone provides a centralized dashboard for managing compromised endpoints, building and deploying new campaign payloads, and reviewing all collected data (such as screenshots, keystrokes, browser credentials).

Operational cover. The panel is branded as “CloudSync Console” with a footer reading “Acuity Systems, Inc. — Cloud Infrastructure Portal v3.2.1,” designed to appear as legitimate enterprise cloud management software if the panel URL is discovered by defenders or hosting providers. This masquerading extends to the CornFlake agent’s service name (Cloud Sync Service) and description (“Synchronizes files with the cloud storage provider”), creating a consistent cover story across the toolchain.

The CloudSync Console masquerading as Acuity Systems, Inc. sign-in panel.
Figure 5. CloudSync Console panel masquerade

Session management and multi-operator support. FruitStone uses JSON Web Token (JWT)-based authentication, session revocation, and rate limiting with IP blocking to prevent brute force attacks against the panel sign in. Multiple operators could be provisioned with individual accounts, and all active sessions are visible with IP address, user-agent, and creation time to enable operational security awareness across the operators.

Agent management. The panel displays all registered CornFlake agents in a dashboard with real-time status updates via Server-Sent Events (SSE). Each agent card shows comprehensive system information including hostname, username, OS version, CPU, RAM, disk usage, screen resolution, timezone, domain membership, and camera/microphone presence, all collected during the CornFlake posture sweep. Agents are grouped by country and subnet, with geographic distribution visualized on a map.

Operators could interact with individual agents through:

  • Remote shell — Interactive cmd.exe or PowerShell command execution with command history
  • File system browser — Live directory traversal and arbitrary file download from compromised hosts
  • Collection tasking — On-demand screenshot, process list, keylog buffer flush, clipboard dump, security posture survey, ChromeKatz cookie/password extraction, camera capture, and audio recording
  • Configuration push — Live runtime reconfiguration of C2 servers, watch paths, and C2 beacon timing
  • Agent update — In-place implant update by pushing a new CornFlake build to a running agent
  • Agent kill — Remote termination of the CornFlake implant

Campaign builder. A step-by-step wizard enables operators to configure and build new CornFlake payloads directly from the panel:

  1. Identity — Campaign ID, C2 host and port, HTTP base URL, executable file name (svchost32.exe by default), and dropper type (C dropper at ~19 KB, Go stub at ~8 MB, or standalone self-installer)
Figure 6. Identity tab
  1. Capabilities — Toggle individual collection modules: screenshots, process enumeration, keylogging, clipboard monitoring, posture survey, file exfiltration, and ChromeKatz browser credential theft
Figure 7. Capabilities tab
  1. File Paths — Configure targeted directories and file extensions by category (documents, archives, images, code, data, emails, encryption keys)
Figure 8. File paths tab
  1. Evasion — Enable garble symbol randomization (for GoLang payloads), XOR string encoding, GZip upload compression, and debug mode
Figure 9. Evasion tab

Infrastructure management. FruitStone provides management interfaces for three layers of supporting infrastructure:

  • Proxy relays — Multi-proxy C2 relay architecture with TLS certificate tracking (fingerprint, expiry), health checks, connection counts, bytes forwarded, and rotation capabilities that push updated server lists to all online agents
  • Beacon profiles — Configurable timing profiles controlling agent sleep intervals, reconnection delays, TLS Server Name Indication (SNI) spoofing (like teams.microsoft.com), and DNS fallback domains
  • Staging servers — External payload hosting infrastructure with push-to-deploy, file listing, and health monitoring
Figure 10. View of the CloudSync staging servers interface

Device code abuse for cloud access

Since July 16, Microsoft has observed a portion of CaptiveCrunch landing pages redirecting users to device code authentication flow experiences. In these cases, users served these landings might be instructed to enter a device code into a legitimate Microsoft sign-in page, a technique commonly referred to as device code phishing.

Device code authentication is a legitimate OAuth workflow designed for devices that cannot support a traditional sign-in experience. However, threat actors could abuse this flow by initiating an authentication request on behalf of a user then convincing the user to enter an actor-controlled device code into a legitimate Microsoft authentication page. When successful, the victim authenticates the threat actor’s session rather than their own.

This activity is consistent with previously reported device code phishing operations conducted by Midnight Blizzard since August 2024. The observed technique does not appear fundamentally novel; however, integrating device code phishing into captive portal and traffic manipulation operations might increase the likelihood that users perceive the authentication request as legitimate. For additional details on Midnight Blizzard-related device code phishing techniques, see: Storm-2372 conducts device code phishing campaign. To understand other threat actors’ use of device code phishing and associated mitigations, see Inside an AI‑enabled device code phishing campaign.

How to protect against CaptiveCrunch activity

Minimize trust in hospitality and guest networks

When traveling, users should treat hotel, conference, airport, and other guest wireless networks as untrustworthy.

  • Prefer private connectivity (including mobile hotspots, satellite, and eSIM-based cellular data connections) over public Wi‑Fi whenever practical.
  • Consider using enterprise-managed travel routers or hotspot devices that establish encrypted tunnels back to trusted corporate infrastructure before accessing sensitive resources.
  • Avoid downloading software updates, certificates, browser updates, network troubleshooting tools, or security utilities presented through captive portals or other unexpected web prompts.
  • Verify update requests through trusted operating system mechanisms rather than pop-up messages or website prompts.

Strengthen identity and access controls

Organizations should assume that public and hospitality network infrastructure might not be trustworthy and should adopt controls that limit exposure to traffic manipulation, credential theft, and device code phishing.

  • Educate users to recognize ClickFix-style prompts, fake verification checks, and paste-and-run instructions as malicious, especially when they invoke command interpreters or script hosts such as cmd.exe, PowerShell, rundll32.exe, or mshta.exe.
  • Use passwordless solutions like passkeys and implement multifactor authentication (MFA).
  • Only allow device code flow where necessary. Microsoft recommends blocking device code flow wherever possible. Where necessary, configure Microsoft Entra ID’s device code flow in your Conditional Access policies.
  • Implement a sign-in risk policy to automate response to risky sign-ins. A sign-in risk represents the probability that a given authentication request is not authorized by the identity owner. A sign-in risk-based policy can be implemented by adding a sign-in risk condition to Conditional Access policies that evaluates the risk level of a specific user or group. Based on the risk level (high/medium/low), a policy can be configured to block access or force MFA.
    • When a user is a high risk and Conditional access evaluation is enabled, the user’s access is revoked, and they are forced to re-authenticate.
    • For regular activity monitoring, use Risky sign-in reports, which surface attempted and successful user access activities where the legitimate owner might not have performed the sign-in. 
  • Use a Security Service Edge (SSE) solution like Global Secure Access to secure access to any app or resource using network, identity, and endpoint access controls.

Reduce exposure during captive portal registration

Organizations should review what information employees provide to hospitality providers when connecting to guest networks.

  • Do not reuse corporate credentials on hotel, conference, or guest-network registration pages.
  • Where possible, organizations should evaluate whether venue-provided wireless is required for corporate events and conferences.
  • Organizations should minimize unnecessary disclosure of employee identities, organizational affiliations, and travel details when booking accommodations or registering for guest network access, consistent with corporate policy and applicable local requirements.

Microsoft Defender detections and hunting guidance

Microsoft Defender customers can refer to the list of applicable detections below. Microsoft Defender coordinates detection, prevention, investigation, and response across endpoints, identities, email, apps to provide integrated protection against attacks like the threat discussed in this blog.

Microsoft Defender for Endpoint detects Storm-2945 activity under the detection Suspicious activity linked to a Russian state-sponsored threat actor has been detected. However, these alerts might be triggered by unrelated threat actor activity. The following chart lists Microsoft Defender detections specific to the TTPs utilized by Storm-2945 in this attack.

Tactic Observed activity Microsoft Defender coverage 
Initial accessFile download via captive portal redirection Microsoft Defender for Endpoint – Suspicious downloaded file
Initial accessClickFix technique, fake browser or OS update, initial file downloadMicrosoft Defender for Endpoint
– Possible initial access from an emerging threat
– Possible ClickFix activity
PersistenceCornFlake registers a Windows service, a Registry Run key, a scheduled taskMicrosoft Defender for Endpoint
– Suspicious Scheduled Task Process Launched  
– Suspicious scheduled task
– Suspicious file added to run key
– Suspicious service registration

Microsoft Entra ID Protection
– Microsoft Entra threat intelligence
– Verified threat actor IP
Stealth/Defense evasionChocoShell disables AMSIMicrosoft Defender for Endpoint
– Possible Antimalware Scan Interface (AMSI) tampering
Credential accessChocoShell’s theft of browser session cookies, saved passwords, Microsoft 365 SSO tokens, and Wi-Fi credentials.   Device code abuse.Microsoft Defender for Endpoint
– Possible theft of passwords and other sensitive web browser information
– Suspicious DPAPI activity

Microsoft Defender For Identity
– Anomalous OAuth device code authentication activity

Microsoft Defender XDR
– User account compromise via OAuth device code phishing
– Malicious sign in from an IP address associated with recognized attacker infrastructure
– Suspicious Azure authentication through possible device code phishing
CollectionCornFlake monitoring and loggingMicrosoft Defender for Endpoint
– Activity that might lead to information stealer
Privilege escalationChocoShell UAC bypass techniquesMicrosoft Defender for Endpoint
– UAC bypass was detected
– Possible Component Object Model (COM) hijacking

Microsoft Security Copilot

Microsoft Security Copilot is embedded in Microsoft Defender and provides security teams with AI-powered capabilities to summarize incidents, analyze files and scripts, summarize identities, use guided responses, and generate device summaries, hunting queries, and incident reports.

Customers can also deploy AI agents, including the following Microsoft Security Copilot agents, to perform security tasks efficiently:

Security Copilot is also available as a standalone experience where customers can perform specific security-related tasks, such as incident investigation, user analysis, and vulnerability impact assessment. In addition, Security Copilot offers developer scenarios that allow customers to build, test, publish, and integrate AI agents and plugins to meet unique security needs.

Threat intelligence reports

Microsoft Defender XDR customers can use the following threat analytics reports in the Defender portal (requires license for at least one Defender XDR product) to get the most up-to-date information about the threat actor, malicious activity, and techniques discussed in this blog. These reports provide the intelligence, protection information, and recommended actions to prevent, mitigate, or respond to associated threats found in customer environments.

Microsoft Security Copilot customers can also use the Microsoft Security Copilot integration in Microsoft Defender Threat Intelligence, either in the Security Copilot standalone portal or in the embedded experience in the Microsoft Defender portal to get more information about this threat actor.

Hunting queries

Microsoft Defender XDR

Microsoft Defender XDR customers can run the following advanced hunting queries to find related activity in their networks:

Detect file creation after Wi-Fi connectivity test on devices

The following query checks for a file creation on a device within two minutes of the device performing built‑in Network Connectivity Status Indicator (NCSI) test, which occurs when network connectivity is established to a Wi-Fi network with a captive portal. This activity might indicate an attacker’s initial access file presence on a device.

Please note that not all files discovered through this query might be malicious or related to this threat activity.

let ncsi_endpoints = dynamic(["msftconnecttest.com","edge-http.microsoft.com","msftncsi.com","captive.apple.com","clients1.google.com",
    "clients3.google.com","clients4.google.com","clients6.google.com","connectivitycheck.gstatic.com","connectivitycheck.android.com",
    "android.clients.google.com","www.gstatic.com","detectportal.firefox.com","detectportal.brave-http-only.com","cloudflareportal.com",
    "cloudflarecp.com","cloudflareok.com","connectivity-check.warp-svc","connectivity.cloudflareclient.com","spectrum.s3.amazonaws.com",
    "nmcheck.gnome.org"]);
let NCSIEvents = DeviceNetworkEvents
    | where Timestamp > ago(7d)
    | where RemoteUrl has_any (ncsi_endpoints)
    | project NCSI_Timestamp = Timestamp, DeviceId, DeviceName, RemoteUrl, NCSI_ReportId = ReportId, NCSI_InitiatingProcessFileName = InitiatingProcessFileName, NCSI_InitiatingProcessCommandLine = InitiatingProcessCommandLine, NCSI_AccountName = InitiatingProcessAccountName;
let FileDownloadEvents = DeviceFileEvents
    | where Timestamp > ago(7d)
    | where ActionType == "FileCreated"
    | where FileName has_any (".exe",".msi",".zip",".rar",".7z")
    | project Download_Timestamp = Timestamp, DeviceId, FileName, FolderPath, Download_ReportId = ReportId, Download_InitiatingProcessFileName = InitiatingProcessFileName, Download_InitiatingProcessCommandLine = InitiatingProcessCommandLine, Download_AccountName = InitiatingProcessAccountName;
NCSIEvents
| join kind=inner (
    FileDownloadEvents
) on DeviceId
| where Download_Timestamp >= NCSI_Timestamp and Download_Timestamp 

Detect connectivity to Storm-2945 infrastructure

The following query checks for connectivity to Storm-2945 infrastructure observed in this attack activity.

let target_domains = dynamic(["ms365-device.com", "ms365-live.com", "m365-owa.com", "owa-ms365.com"]);
let target_ips = dynamic(["31.57.243.154", "38.146.28.75", "38.146.28.132", "104.194.159.150", "107.189.26.194", "213.145.86.112"]);
DeviceNetworkEvents
| where RemoteUrl has_any(target_domains) or RemoteIP in (target_ips)
| project
    Timestamp,
    DeviceName,
    DeviceId,
    RemoteUrl,
    RemoteIP,
    LocalIP,
    InitiatingProcessFileName,
    InitiatingProcessCommandLine,
    AccountName = InitiatingProcessAccountName,
    ReportId

Detect CornFlake RAT presence on affected systems

The following query checks for the presence of the CornFlake RAT binary.

DeviceProcessEvents
| where FolderPath == "%APPDATA%\\svchost32\\svchost32.exe"
   or FolderPath endswith @"\svchost32\svchost32.exe"
| project Timestamp, DeviceName, DeviceId, FileName, FolderPath, InitiatingProcessFileName, InitiatingProcessCommandLine, AccountName, ReportId

Detect CornFlake RAT Windows service registration

The following query checks for the CornFlake RAT Windows service registration.

DeviceRegistryEvents
| where RegistryKey has @"\SYSTEM\CurrentControlSet\Services\svchost32"
| where ActionType == "RegistryValueSet"
| where (RegistryValueName == "DisplayName" and RegistryValueData == "Cloud Sync Service")
    or (RegistryValueName == "Description" and RegistryValueData == "Synchronizes files with the cloud storage provider")
| project
    Timestamp,
    DeviceName,
    DeviceId,
    RegistryKey,
    RegistryValueName,
    RegistryValueData,
    ActionType,
    InitiatingProcessFileName,
    InitiatingProcessCommandLine,
    InitiatingProcessAccountName,
    ReportId

Microsoft Sentinel

Microsoft Sentinel customers can use the TI Mapping analytics (a series of analytics all prefixed with ‘TI map’) to automatically match the malicious domain indicators mentioned in this blog post with data in their workspace. If the TI Map analytics are not currently deployed, customers can install the Threat Intelligence solution from the Microsoft Sentinel Content Hub to have the analytics rule deployed in their Sentinel workspace.

Detect network IP and domain indicators of compromise using ASIM

The following query checks IP addresses and domain IOCs across data sources supported by ASIM network session parser:

//IP list and domain list- _Im_NetworkSession
let lookback = 30d;
let ioc_ip_addr = dynamic(["213.145.86.112"]);
let ioc_domains = dynamic(["213.145.86.112/t/pixel.gif", "213.145.86.112/cdn/chunks/polyfill-7e2b.min.js", "213.145.86.112/t/event"]);
_Im_NetworkSession(starttime=todatetime(ago(lookback)), endtime=now())
| where DstIpAddr in (ioc_ip_addr) or DstDomain has_any (ioc_domains)
| summarize imNWS_mintime=min(TimeGenerated), imNWS_maxtime=max(TimeGenerated),
  EventCount=count() by SrcIpAddr, DstIpAddr, DstDomain, Dvc, EventProduct, EventVendor

Detect web sessions IP and file hash indicators of compromise using ASIM

The following query checks IP addresses, domains, and file hash IOCs across data sources supported by ASIM web session parser:

//IP list - _Im_WebSession
let lookback = 30d;
let ioc_ip_addr = dynamic(["213.145.86.112"]);
let ioc_sha_hashes =dynamic([“918fa52ae45ed60ba7cc8bdc99c3cbe9ab92e0375ec31fc05d0d4513be11c593”, “be99857449d2856dd5a84e21c8a3d5e0e01456adb44062ddec5a6b4970d8d42c”]);
_Im_WebSession(starttime=todatetime(ago(lookback)), endtime=now())
| where DstIpAddr in (ioc_ip_addr) or FileSHA256 in (ioc_sha_hashes)
| summarize imWS_mintime=min(TimeGenerated), imWS_maxtime=max(TimeGenerated),
  EventCount=count() by SrcIpAddr, DstIpAddr, Url, Dvc, EventProduct, EventVendor

Detect domain and URL indicators of compromise using ASIM

The following query checks domain and URL IOCs across data sources supported by ASIM web session parser:

// file hash list - imFileEvent
// Domain list - _Im_WebSession
let ioc_domains = dynamic(["https://213.145.86.112/t/pixel.gif", "https://213.145.86.112/cdn/chunks/polyfill-7e2b.min.js", "https://213.145.86.112/t/event"]);
_Im_WebSession (url_has_any = ioc_domains)

ChocoShell C2 communications

The following query detects ChocoShell communications with its C2 server using HTTPS with URI paths designed to blend in with legitimate web traffic. Beacons use /t/pixel.gif?m=<status>, mimicking an image tracking pixel.

let lookback = 30d;
let ioc_url_artifacts = dynamic(["/t/pixel.gif?m="]);
_Im_WebSession(starttime=todatetime(ago(lookback)), endtime=now())
| where DstDomain  in (ioc_url_artifacts)
| summarize imWS_mintime=min(TimeGenerated), imWS_maxtime=max(TimeGenerated),
  EventCount=count() by SrcIpAddr, DstIpAddr, Url, Dvc, EventProduct, EventVendor

Indicators of compromise

IndicatorTypeDescriptionFirst seen
ms365-device[.]comDomainCaptiveCrunch DCF redirect2026-07-23
ms365-live[.]comDomainCaptiveCrunch DCF redirect2026-05-14
m365-owa[.]comDomainCaptiveCrunch AitM infrastructure2026-07-20
owa-ms365[.]comDomainCaptiveCrunch AitM infrastructure2026-07-16
31.57.243[.]154  IP addressCaptiveCrunch AitM infrastructure2026-07-16
38.146.28[.]75  IP addressCaptiveCrunch AitM infrastructure2026-07-01
38.146.28[.]132IP addressCaptiveCrunch DNS Resolver2026-07-15
104.194.159[.]150  IP addressCaptiveCrunch AitM infrastructure2026-04-28
107.189.26[.]194IP addressChocoShell C2 / CaptiveCrunch DNS Resolver2026-02-27
213.145.86[.]112  IP addressChocoShell C22026-07-01
918fa52ae45ed60ba7cc8bdc99c3cbe9ab92e0375ec31fc05d0d4513be11c593  File hashCornFlake2026-07-03
be99857449d2856dd5a84e21c8a3d5e0e01456adb44062ddec5a6b4970d8d42cFile hashChocoShell2026-07-10

References

Learn more

For the latest security research from the Microsoft Threat Intelligence community, check out the Microsoft Threat Intelligence Blog.

To get notified about new publications and to join discussions on social media, follow us on LinkedIn, X (formerly Twitter), and Bluesky.

To hear stories and insights from the Microsoft Threat Intelligence community about the ever-evolving threat landscape, listen to the Microsoft Threat Intelligence podcast.

The post CaptiveCrunch: Midnight Blizzard targets travelers worldwide for malware delivery and credential theft appeared first on Microsoft Security Blog.

Understanding Illicit Ecosystems: How Dark Web Forums Structure Cybercrime

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Understanding Illicit Ecosystems: How Dark Web Forums Structure Cybercrime

As part of our ongoing series, we analyze how dark web forums operate, breaking down Flashpoint’s tiered classification system and examining how specialized, hybrid platforms function together as an interconnected cybercrime supply chain.

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July 13, 2026

When a high-profile data breach hits headlines, the default assumption is often to view the dark web as a single, centralized marketplace where any illicit service or tool can be bought. While many illicit forums aspire to be seen as a “one-stop shop,” the reality is that the underground economy relies on an interconnected network of specialized hubs that each align with distinct phases of the cybercrime lifecycle.

To understand how cybercrime thrives, it is vital to learn how these online spaces survive and how they play their parts in graduating threat actors from entry-level novices to sophisticated adversaries.

Navigating the Cybercrime Ecosystem: Entry Barriers and Forum Tiering

An illicit community’s survival hinges on its operational value and culture, which is ultimately created by its supporters. In a low-trust environment filled with cybercriminals, hidden law enforcement, and security researchers, these digital spaces are inherently defensive. To protect their communities from competitors’ attacks, surveillance, and eventual takedowns, forums implement rigorous gatekeeping mechanisms.

As such, Flashpoint organizes the cybercrime ecosystem into a tiered structure, separating them into low, mid, or top-tier forums, defined by several key factors such as: 

  • Entry Barriers: The financial or reputational requirements for a user to join the community, indicating the forum’s exclusivity.
  • Technical Expertise: The collective technical skills and proficiency of the forum’s members.
  • Trade Quality: The quality and value of illicit goods and services exchanged, such as advanced hacking tools or high-value data leaks.
  • Operational Security (OP SEC): The extent to which the community upholds strict security protocols and practices.

By analyzing these vectors, the ecosystem naturally separates into three distinct operational tiers.

Low-Tier Forums

These communities are easily accessible, often requiring a small fee or completely free registration with little to no vetting. They host less sophisticated users, beginner hackers, and minor data brokers seeking free material. Because the technical barrier is low, these spaces primarily share low-cost, high-volume data, including large data leaks, generic phishing guides, unchecked stolen accounts, and cracked software.

Consequently, these environments face a persistently high risk of scams and poor quality data. Within low-tier forums, reputation is often built by sharing free data or purchasing a rank or upgrade which is viewable by other users.

Mid-Tier Forums

Moderately accessible via both Tor and the clearnet, entry into these spaces typically require a vouch from an existing member, a minimal registration fee, or an initial deposit. These platforms concentrate on large-scale fraudulent activity and the exchange of various datasets—including bulk carding data, stolen credentials, stealer logs, phishing kits, botnets, and various malware.

The user base includes a mix of vendors, experienced threat actors, affiliates of larger groups, and aspiring cybercriminals looking for training. To protect users from internal fraud, these forums heavily prioritize integrated escrow services and reputation systems, which can be improved by purchasing an internal high-tier status.

Top-Tier Forums

These are highly exclusive platforms dedicated to high-value, highly technical, and targeted criminal operations. New applicants face a stringent vetting process, typically demanding either a formal invitation or a substantial registration payment. This exclusive layer hosts highly skilled, professional threat actors, malware developers, and key decision-makers within major illicit groups.

This is the ecosystem where adversaries build trust through valuable technical contributions or community reputation points and execute complex money laundering schemes, trade zero-day exploits, facilitate ransomware-as-a-service (RaaS) partnerships, and conduct large-scale initial access broker sales.

What Are the Different Types of Dark Web Forums?

Once a community establishes its tier, it usually functions as a specialized hub linked to a specific stage in the overall cybercrime lifecycle. They do this to cultivate talent and expertise, which naturally bridges communities together, creating a supply chain where different forums handle distinct operational and structural needs.

General Information and Community Boards

Modeled after surface-web sites like Reddit, these platforms serve as social and informational hubs. Discussions prioritize coordination, reputation management, and the propagation of best practices regarding OPSEC. Users share news about cybercriminal arrests, look for advice on how to remain anonymous, report potential exit-scams, and provide detailed reviews of specific vendors, particularly those selling illicit drugs.

Financial Theft and Carding Forums

These semi-structured environments blend marketplaces with social networks, utilizing a professionalized supply chain for selling stolen cards, dumps, and fullz. To reduce internal fraud, they rely heavily on reputation-building tools like verified seller statuses and integrated refund systems for invalid data. To ensure operational longevity, they are typically hosted on bulletproof infrastructure located in states that do not comply with international takedown requests, such as the Russian Federation.

Data Leak Forums

Depositories for stolen databases where raw breach information is structured into a tradeable commodity. Leaks are listed by victim name and sector, allowing actors to quickly find credentials or corporate records to repurpose for credential stuffing, extortion, or identity fraud.

Cracking and Hacking Tutorials (Knowledge Bases)

Existing entirely for knowledge exchange and offensive techniques, threat actors share methods, tutorials, fraudulent schemes, and bypass techniques, often encouraging educational sharing through competitions.

High-Skill Exploit and Access Forums

Top-tier platforms hosting the “upper echelons” of the community, such as initial access brokers, exploit developers, and malware creators. They rely heavily on strict arbitration systems, mandatory vendor deposits, and escrow mechanisms to safely conduct high-impact transactions and corporate intrusions.

Low-Barrier Retail Forum

High-traffic segments trading mass-market digital goods like cracked subscription accounts, premium software, and online gaming assets. Characterized by an exceedingly low barrier to entry and a relatively young user base seeking quick profit without the capability for advanced, complex operations.

Map the Illicit Pipeline Using Flashpoint

What makes the cybercriminal ecosystem truly cohesive is that the lines between these various types of forums and communities constantly blur. Most illicit communities are hybrid and transitional, intentionally or naturally blending categories to cater to each other’s needs and boost monetization.

Hybrid forums frequently connect the how-to tutorials with actual stolen data and network access, effectively creating a structural pipeline for threat actor progression. Platforms like BreachForums combine the attention-grabbing aspect of a data leak site with a structured marketplace for selling logs and other sensitive data. This type of hybridization allows a threat actor to progress from a beginner reading tutorials to an active criminal deploying stolen data.

Monitoring these fluid structures and transitions is the only way to understand how threat actors develop, and how the interconnected cybercrime landscape shifts over time. Therefore, it is essential for security teams to look beyond cyber threats as isolated, and recognize the multi-platform strategies these actors employ. Request a demo to gain visibility into these threat actor communities and proactively defend your organization from across the entire cybercrime supply chain.

Check out the rest of our “Understanding Illicit Ecosystems” series:
Understanding Illicit Ecosystems: The Hybrid Threat of “The Com”
Understanding Illicit Ecosystems: XSS and the Current State of the Russian-Speaking Underground
Understanding Illicit Ecosystems: Weaponizing Mainstream Apps and Social Infrastructure

See Flashpoint in Action

The post Understanding Illicit Ecosystems: How Dark Web Forums Structure Cybercrime appeared first on Flashpoint.

Unmasking the Digital Trail: Essential Techniques for Vetting AI-Generated Content

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Unmasking the Digital Trail: Essential Techniques for Vetting AI-Generated Content

In our latest on-demand webinar, we outline the practical, human-driven techniques threat intelligence teams must deploy to detect synthetic media, protect corporate RAG ecosystems, and filter through the noise of AI-polluted networks.

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June 29, 2026

In the era of generative artificial intelligence (AI), threat intelligence is facing a profound signal-to-noise challenge. AI has introduced a massive paradigm shift to threat actor operations—making execution extremely easy while simultaneously dramatically complicating the task of verification for security teams.

In our latest on-demand webinar, Matt Edmonson, SANS Senior Instructor and founder of Argelius Labs, joined Flashpoint to discuss the intersection of Open Source Intelligence (OSINT) and AI. Drawing from his vast federal law enforcement experience, he shared actionable, human-driven techniques for detecting and vetting AI-generated online content.

Neutralizing the Automated RAG and Vector Database Trap

Before deploying any human-driven vetting techniques, an analyst must understand the specific structural trap threat actors are laying. Adversaries are no longer just using AI to spin up isolated phishing copy; they are using it to corrupt the automated defense pipelines that security teams rely on.

Modern threat intelligence workflows utilize automated ingestion to feed open-source data directly into local vector databases and Retrieval-Augmented Generation (RAG) models. Aware of this, sophisticated threat actors deploy a coordinated infrastructure strategy: they register multiple lookalike domains simultaneously to broadcast the exact same AI-generated disinformation narrative.

When automated security tools ingest this data, the system flags multiple distinct “sources” confirming the story as truth. This structural echo chamber completely bypasses automated verification safeguards, polluting corporate databases with validated lies. We have seen this play out via:

  • Long-Game Credibility Building: Edmonson highlighted an active Foreign Malicious Influence (FMI) campaign utilizing a French lookalike news site called Verite Cache (“The Hidden Truth”). The threat actors scrape legitimate Western news, use AI to rewrite it to build structural domain authority over time, and then manipulate narrative outcomes the moment a critical geopolitical event or election occurs.
  • Simultaneous Infrastructure Deployment: This pattern was mirrored in Southeast Asia, where Singapore recently banned six lookalike news sites targeting regional discourse. Upon technical inspection, five of those six distinct domains had been registered on the exact same day to broadcast a unified narrative.
  • Organic-Looking Algorithmic Surges: The scale of these operations can shift political landscapes in a matter of days. Romania recently took the extreme step of canceling and restarting its presidential election due to a covert, highly coordinated Russian-backed social media campaign. The operation used synthetic assets to trigger algorithmic recommendation engines, driving an intense, seemingly organic surge for an underdog candidate.

Triangulating AI Flaws and Anomalies Across Modalities

Vetting AI content relies on compiling a cluster of intersecting indicators across text, images, audio, and video until a definitive analytical confidence level is reached. While generative tools have grown highly sophisticated, they are still bound by mathematical constraints and architectural limitations. Catching these errors and inconsistencies requires analysts to identify a cluster of intersecting indicators across text, images, audio, and video:

  • Textual Analytics (Linguistic Quirks and Filler Text): Large Language Models (LLMs) leave distinct behavioral footprints. Analysts should look for commonly-used AI wordings and “portable sentences”, as well as automated translation leakage that reveals a threat actor’s native language mechanics.
  • Visual Logic Flaws (Physics and Seams): AI models frequently fail to grasp the fundamental physics of the real world. Analysts should closely inspect image logic for anatomical blunders (such as inverted hand structures), impossible geometry, or objects with extreme structural flaws. Additionally, AI struggles with “texture seams”—the exact boundaries where distinct textures meet.
  • Auditory and Video Glitches (Cadence and Duration): Human speech is inherently messy, characterized by breathing pauses, environmental background noise, and shifting cadences. Synthetic speech is often locked into a perfectly uniform, monotone rhythm. Furthermore, high-fidelity deepfakes are incredibly resource-intensive to sustain over long durations. While an actor can fake 10 to 15 seconds of synthetic video convincingly, a five-minute video will almost always display jarring cuts, visual artifacting, or avatars clipping out of frame.

Empowering the Human Layer | Watch the Full Webinar

Human analysts remain the most critical layer of defense against illicit uses of AI. Empowered by comprehensive threat intelligence, OSINT, and AI technologies, security teams can hunt for clusters of intersecting indicators across text, images, audio, and video to assess authenticity. To learn more and to gain more essential techniques, watch the full on-demand webinar. Using Flashpoint, organizations can filter through noise, execute critical data premortems, and neutralize sophisticated disinformation campaigns.

See Flashpoint in Action

The post Unmasking the Digital Trail: Essential Techniques for Vetting AI-Generated Content appeared first on Flashpoint.

Understanding Illicit Ecosystems: Weaponizing Mainstream Apps and Social Infrastructure

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Understanding Illicit Ecosystems: Weaponizing Mainstream Apps and Social Infrastructure

As part of our ongoing series, we focus on the shared infrastructure that fuels threat actors; the intersection of mainstream social media, open-source messaging platforms, and gaming communities.

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Threat actors and their illicit communities do not exist in a vacuum. To scale their operations, coordinate financial fraud, deploy malware, and recruit new talent, threat actors must interface with the broader digital world. This means leveraging everyday, public digital spaces to facilitate illicit activity, effectively hiding in plain sight.

The Clearnet Threat Landscape: Hiding in Plain Sight

When conceptualizing the cybercriminal underground, it is easy to focus exclusively on Tor-based onion sites or restricted-access dark web forums and marketplaces. However, a massive portion of modern illicit activity thrives on the clearnet. Threat actors heavily utilize commercial social media and public messaging networks to coordinate fraud, deploy malware, and run public relations campaigns for their operations.

At first glance, conducting illicit operations on highly monitored, mainstream platforms seems counterintuitive. However, the massive, continuous volume of legitimate traffic on the clearnet provides a form of operational security. By blending into the noise, threat actors can maintain a highly accessible digital presence. This visibility is crucial for their business models: it allows them to maintain a low barrier to entry for potential recruits and targets who know exactly what markers to look for, or who are systematically funneled into these spaces.

How Threat Actors Weaponize Consumer Platforms

The misuse of mainstream communication tools has changed how threat actors interact. Rather than waiting for users to seek out the dark web, cybercriminals are actively meeting their targets or co-conspirators on platforms designed for daily socialization.

Discord

Originally built to connect gaming communities, Discord’s rapid growth and robust infrastructure have inadvertently made it a target for malicious activity. Cybercriminals treat the platform as a multi-functional tool for both technical infrastructure, social engineering, and radicalization.

On a technical level, advanced persistent threats (APTs) and other threat actors exploit Discord’s content delivery network (CDN) to host and distribute malware. Because traffic to Discord domains is generally trusted by corporate networks, threat actors can potentially use it to deliver payloads—such as infostealers and remote access trojans (RATs)—bypassing standard security perimeters.

Beyond hosting malware, extremist groups across various ideological spectrums often target the platform’s demographic, which skews heavily towards younger tech-savvy users. This group provides an impressionable pool of adolescents who may be susceptible to grooming, indoctrination, and recruitment into illicit operations.

Case Study: The Targeting and Recruitment Mechanics of “The Com”

While monitoring The Com, Flashpoint analysts have observed the systematic use of platforms like Discord, Roblox, and Minecraft to run predatory extortion pipelines. The mechanics of this ecosystem takes place through a multi-phase methodology:

  1. Platform Scouting: Recruiters patrol servers on popular youth-centric gaming platforms, such as Discord, Roblox, and Minecraft. They look for minors showing signs of social isolation, depression, disordered eating, or a desire to belong.
  2. Building Trust and “Love Bombing”: Initial engagements are seemingly harmless. However, trust is built quickly to establish a sense of indebtedness. Recruiters offer gifts such as in-game perks/currency, premium subscriptions, or other digital items. In some cases, a romantic facade is used to establish a connection. In either scenario, “love bombing” creates an immediate feeling of psychological obligation in the target.
  3. Platform Migration: Once rapport is established, the recruiter moves the target away from the game and into an encrypted app or private Discord server, following a public-to-private strategy. By moving the interaction away from the original platform’s safety controls, the recruiter can isolate the target in a more controlled environment.

Once isolated, perpetrators coerce victims into sending sensitive imagery or CSAM. This material is immediately compiled and weaponized as leverage for blackmail via doxxing. This creates a severe psychological trap in which the victim feels compelled to partake in escalating illegal activity to keep their previous actions hidden. This drives the victim to transition from a victim into an aggressor to escape their own abuse.

Telegram

While many social media and messaging platforms can serve as an initial funnel for engagement, Telegram has been known to be used from time to time as an operational hub for the broader illicit ecosystem. Since the arrest of Pavel Durov, Telegram has begun working more closely with law enforcement, leading to several key arrests and major disruptions due to their cooperation. 

The platform occupies a unique space in threat intelligence and open source intelligence (OSINT). While the vast majority of its user base is entirely benign, its minimal moderation policy and robust channel architecture have made it vital to public and private intelligence gathering.

Telegram functions as an open marketplace and real-time coordination center for a vast spectrum of threat actors. Flashpoint has observed it being used by:

  1. State-sponsored APT groups and hacktivists
  2. Geopolitical actors and mercenary groups distributing battlefield intelligence and propaganda
  3. Cybercriminal syndicates coordinating financial fraud schemes, check fraud, and the sale of compromised data.

Furthermore, threat actors routinely use other public-facing platforms like X (formerly Twitter) alongside Telegram to amplify their impact. They leverage the broad reach of social media to broadcast proof of their compromises, hype up ransomware leaks, and exert public pressure on corporate victims during extortion cycles. Concurrently, Telegram often acts as the backend repository where the stolen data is hosted, discussed, and monetized.

Monitor the Clearnet Using Flashpoint

The evolution of illicit ecosystems demonstrates that the lines between the dark web and the clearnet have intersected. Whether analyzing the activities of extremist and threat actor groups or tracking the predatory pipelines of The Com, defenders must look beyond traditional intelligence sources.

Because malicious actors rely heavily on consumer messaging apps and social platforms to coordinate attacks, leak data, and target people, monitoring these public-to-private pipelines is an essential component of threat intelligence. Uncovering these physical and cyber threats requires best-in-class threat intelligence and OSINT investigations capable of parsing the massive noise of the clearnet to find the signals of illicit coordination.

Request a demo to see how Flashpoint empowers security teams to monitor these decentralized threat landscapes to proactively protect their critical assets.

Check out the rest of our “Understanding Illicit Ecosystems” series:
Understanding Illicit Ecosystems: The Hybrid Threat of “The Com”
Understanding Illicit Ecosystems: XSS and the Current State of the Russian-Speaking Underground

See Flashpoint in Action

The post Understanding Illicit Ecosystems: Weaponizing Mainstream Apps and Social Infrastructure appeared first on Flashpoint.

Understanding Illicit Ecosystems: XSS and the Current State of the Russian-Speaking Underground

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Understanding Illicit Ecosystems: XSS and the Current State of the Russian-Speaking Underground

In this post, we explore XSS’ shift from a unified forum to a scattered community spread across several competing factions.

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What is XSS?

For more than two decades, XSS was the gathering ground for the Russian-speaking cybercriminal underground. Evolving from its former name, DaMaGeLaB, XSS evolved from a mid-tier message board into a top-tier hacking forum.

XSS is home to vendors of various crime types, including loaders, phishing, scamming, carding, malware development, distributed denial-of-service (DDoS) bots, and related services. It also facilitates the trade of illicit goods and services, while simultaneously serving as a networking and recruitment hub for threat actors.

XSS forum content falls within the following main sections:

  • “Underground”: Includes most noncommercial content, such as sharing information on malware, vulnerabilities, and exploits, phishing, fraud, open source intelligence, artificial intelligence, and machine learning.
  • “Programming, Development”: Includes posts and articles about programming languages and administration.
  • “Library”: Includes news articles, databases, and discussions around software and tools. Users also post about vulnerabilities and exploits.
  • “Business Decisions”: Users discuss different investments, the sale of digital goods, trading, start-ups of fraudulent businesses, and news about cryptocurrencies.
  • “Lounge Zone, Resting”: Content involves lifestyle discussions, hobbies, and cybercriminal community rumors and scandals.
  • “Trading Platform”: Users sell and look to buy network access, malware, counterfeit documents, and advertise their services. This is where users hire and look for work or partners.
  • “People’s Court”: Used for complaints and arbitration and contains lists of phishing forums and scammers.
  • “Ours”: Contains information about the XSS project, discussions on issues, suggestions, and initiatives for forum improvement.
  • “Private: Underground”: Closed section for only forum members.
XSS forum main sections (Source: XSS)

XSS Disruption: July 2025 Takedown

On July 23, 2025, law enforcement organizations reportedly seized XSS as part of a multinational operation with Ukrainian authorities, French police, and Europol. Alongside the domain seizure, French authorities reported the arrest of XSS’s longtime administrator in Ukraine.

This arrest triggered an immediate chain reaction that has had lasting effects on the Russian-speaking underground—with the XSS ecosystem splintering into several competing factions.

The Current State of the Russian-Speaking Underground

While the original XSS architecture was severely disrupted, the surrounding Russian-speaking cybercriminal ecosystem remains intensely active. However, instead of a centralized hub, the XSS ecosystem is spread out through competing environments that emerged directly from the fallout of the takedown.

DamageLib

Launched by the legacy moderators of XSS, DamageLib represents a structural pivot away from standard illicit forums. Concluding that the old XSS site was compromised by law enforcement, the moderators launched a new model that completely abandons commerce—shutting down all buying, selling, and auctions entirely—-to eliminate user tracking and surveillance. Instead, it focuses strictly on technical materials and tutorials.

Rehub

Recognizing that displaced cybercriminals still required a commercial venue to trade, a former XSS moderator launched Rehub quickly after the emergence of DamageLib. Rehub immediately integrated a commercial platform, successfully recruiting prominent threat actors into its moderation team to establish underground credibility.

The forum is still in its development stage, with its content being populated, and an active member base being built.

XSS[.pro]

In early August 2025, an unknown entity launched an alleged resurrection of the forum on a new domain [.pro], utilizing old backups that preserved legacy user data, threads, and forum deposits. However, this new version has been met with significant distrust from Exploit and DamageLib, believing the [.pro] domain to be a honeypot controlled by law enforcement.

XSSF Forum

Started by a pro-Russian Telegram hacking group, this community actively targets EU and Ukrainian digital infrastructure. According to user discussions on DamageLib, this forum is not related to XSS. In addition, Flashpoint analysts note that targeting Ukrainian infrastructure directly contradicts its original community rules. The authenticity of this forum and its ownership has not been verified.

Monitor a Fractured Underground Using Flashpoint

While law enforcement achieved a significant victory over XSS, they did not eliminate the Russian-speaking cybercriminal underground. Instead, they broke the foundational trust mechanics that had kept it centralized for twenty years.

This has left the Russian-speaking underground in a deeply fractured state that is still intensely active and highly adaptive. For defenders and analysts, this threat has not diminished—it has diversified. Tracking this ecosystem no longer means watching a single centralized community, but rather actively mapping out the live migrations, shifting rules, and behavioral patterns across these splintered groups.

Request a demo to learn how Flashpoint helps security teams aggregate intelligence from these scattered factions into a single source of truth, empowering your organization to proactively monitor and intercept emerging threats.

Request a demo today.

The post Understanding Illicit Ecosystems: XSS and the Current State of the Russian-Speaking Underground appeared first on Flashpoint.

The Language of Emojis in Threat Intelligence: How Adversaries Signal, Obfuscate, and Coordinate Online

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The Language of Emojis in Threat Intelligence: How Adversaries Signal, Obfuscate, and Coordinate Online

In this post, we examine how threat actors use emojis across illicit communities, how these symbols function as a form of coded language, and why understanding this form of communication is increasingly critical for threat intelligence teams.

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April 6, 2026

As threat actor activity continues to shift toward informal, fast-moving communication platforms such as Telegram and Discord, the way adversaries communicate is evolving. Emojis, often dismissed as casual or nontechnical, have become a meaningful part of that evolution.

Across illicit forums, messaging apps, and closed communities, emojis are used not just for expression, but for signaling intent, categorizing activity, and, in some cases, obscuring meaning from outsiders. For analysts, this introduces an additional layer of context that can influence how communications are interpreted, prioritized, and actioned.

Emojis as a Functional Layer of Communication

Within threat actor communities, emoji usage is often structured and repeatable.

Rather than replacing language entirely, emojis act as a functional overlay — reinforcing key concepts, highlighting important information, and accelerating communication in high-volume environments.

This is especially common in:

  • Telegram fraud channels
  • Phishing and carding communities
  • Service marketplaces and access broker groups

In these environments, speed and clarity matter. Emojis allow actors to quickly scan messages, identify relevant content, and engage without parsing long text-based posts.

Common Emoji Categories and What They Signal

Flashpoint analysis of illicit communities shows that emoji usage tends to cluster around a set of recurring categories. While meanings can vary slightly by group, several patterns appear consistently.

Financial Activity and Monetization

Emojis related to money are among the most frequently used.

Common examples include:

  • 💰 / 💸 — Profit, successful fraud, or payouts
  • 💳 — Credit cards, carding activity, or stolen payment data
  • 🏦 — Banks or financial institutions
  • 🪙 — Cryptocurrency-related activity

These symbols often appear in sales posts, fraud logs, or success claims, helping actors quickly identify opportunities tied to financial gain.

Access, Credentials, and Compromise

Another cluster of emoji usage centers on access and account compromise, where symbols are used to signal the availability of credentials, successful intrusions, or control over compromised systems.

Examples include:

  • 🔑 — Credentials or account access
  • 🔓 — Successful breach or unlocked account
  • 📥 / 📤 — Data exfiltration or transfer
  • 🗂 — Databases or collections of stolen data

In many cases, these emojis are used in combination with minimal text, allowing actors to advertise access or share results without detailed descriptions.

Tools, Automation, and Services

Emojis are also used to signal tooling and service offerings.

Examples include:

  • 🤖 — Bots, automation tools, or malware
  • ⚙ — Configuration, setup, or infrastructure
  • 🧰 — Toolkits or bundled services
  • 📡 — Infrastructure, communication channels, or delivery mechanisms

These are commonly seen in phishing-as-a-service, SMS gateway services, and malware distribution communities.

Targets and Geography

Threat actors frequently use emojis to represent targets or regions.

Examples include:

  • 🏢 — Corporate or enterprise targets
  • 🎯 — Targeting or “hits”
  • 📍 — Specific targets, drop locations, or points of interest
  • 🌐 — Global campaigns
  • Country flags — Specific geographic targeting

This allows actors to signal targeting scope quickly, particularly in multilingual or international groups.

Urgency, Success, and Status

Some emojis are used to communicate momentum or importance.

Examples include:

  • 🔥 — High-value or trending activity
  • ✅ — Verified success or working method
  • 🚨 — Urgent update or active campaign
  • 📈 — Growth or increased results

These signals are particularly important in fast-moving channels where actors compete for attention.

Emojis as a Tool for Obfuscation

Beyond signaling, emojis are also used to evade detection.

Threat actors may substitute emojis for keywords associated with:

  • Fraud techniques
  • Financial activity
  • Specific platforms or services

For example, replacing “credit card” with 💳 or “bank” with 🏦 can help bypass basic keyword filters or reduce visibility in automated moderation systems.

When combined with slang, abbreviations, and multilingual phrasing, this creates a layered form of obfuscation that complicates large-scale monitoring efforts.

Building Identity and Reputation Through Emoji Patterns

Emoji usage is not just functional. It can also be behavioral.

Over time, actors often develop recognizable patterns in how they use emojis:

  • Consistent combinations in sales posts
  • Repeated formatting styles
  • Unique ways of structuring messages

These patterns can serve as lightweight identifiers, helping analysts:

  • Track the same actor across different channels
  • Identify reposted or syndicated content
  • Link activity between platforms

In ecosystems where aliases frequently change, these subtle patterns can provide additional attribution signals.

Cross-Language Communication in Global Threat Ecosystems

Illicit communities are inherently global, spanning multiple languages and regions.

Emojis provide a shared visual layer that allows actors to communicate core concepts without relying entirely on text. This is particularly valuable in:

  • Large Telegram channels with international membership
  • Cross-border fraud operations
  • Decentralized marketplaces

For example, a combination of 💳 + 💰 + 🌍 can communicate “global carding opportunity” without requiring a shared language.

This ability to compress meaning into visual shorthand helps scale operations and coordination across diverse actor networks.

Context Still Determines Meaning

Despite these patterns, emoji usage is not universal or fixed.

The same emoji can carry different meanings depending on:

  • The platform (Telegram vs. Discord vs. forums)
  • The specific community
  • The surrounding text and context

For example, 🔥 may indicate “high value” in one group, but simply “active discussion” in another.

For analysts, this reinforces the need to treat emojis as contextual signals, not standalone indicators. Accurate interpretation depends on understanding the broader communication environment.

What This Means for Threat Intelligence Teams

Emoji usage reflects a broader shift in how threat actors communicate toward faster, more visual, and more adaptive forms of interaction.

Flashpoint assesses that incorporating emoji analysis into intelligence workflows can enhance:

  • Detection of emerging campaigns
  • Identification of high-value activity
  • Attribution and actor tracking
  • Interpretation of intent and sentiment

While emojis alone are not decisive indicators, they provide an additional layer of signal that can strengthen overall analysis.

Supporting Security Teams with Threat Intelligence

Understanding how threat actors communicate down to the symbols they use provides critical context for identifying and interpreting emerging threats.

Flashpoint delivers intelligence that helps organizations monitor illicit communities, track evolving communication patterns, and translate raw data into actionable insights. Within the Flashpoint platform, analysts can search across environments like Flashpoint Ignite and Echosec using emojis alongside keywords—enabling more precise discovery of relevant conversations, signals, and emerging activity that might otherwise be missed.

This approach allows teams to capture nuance in how threat actors communicate, improving detection, attribution, and overall situational awareness.

To learn how Flashpoint can support your team with real-time intelligence and analysis, request a demo.

Begin your free trial today.

The post The Language of Emojis in Threat Intelligence: How Adversaries Signal, Obfuscate, and Coordinate Online appeared first on Flashpoint.

The Human Element: Turning Threat Actor OPSEC Fails into Investigative Breakthroughs

13 February 2026 at 20:09

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The Human Element: Turning Threat Actor OPSEC Fails into Investigative Breakthroughs

In this post, we explore how the psychological traps of operational security can unmask even the most sophisticated actors.

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February 13, 2026
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The threat intelligence landscape is often dominated with talks of sophisticated TTPs (tactics, tools, and procedures), zero-day vulnerabilities, and ransomware. While these technical threats are formidable, they are still managed by human beings, and it is the human element that often provides the most critical breakthroughs in attributing these attacks and de-anonymizing the threat actors behind them.

In our latest webinar, “OPSEC Fails: The Secret Weapon for People-Centric OSINT”,  Flashpoint was joined by Joshua Richards, founder of OSINT Praxis. Josh shared an intriguing case study where an attacker’s digital breadcrumbs led to a life-saving intervention. 

Here is how OSINT techniques, leveraged by Flashpoint’s expansive data capabilities, can dismantle illegal threat actor campaigns by turning a technical investigation into a human one.

Leveraging OPSEC as a Mindset

In a technical context, OPSEC is a risk management process that identifies seemingly innocuous pieces of information that, when gathered by an adversary, could be pieced together to reveal a larger, sensitive picture.

In the webinar, we break down the OPSEC mindset into three core pillars that every practitioner, and threat actor, must navigate. When these pillars fail, the investigation begins.

  • Analyzing the Signature: Every human has a digital signature, such as the way they type (stylometry), the times they are active, and the tools they prefer.
  • Identity Masking & Persona Management: This involves ensuring that your investigative identity has zero overlap with your real life. A common failure includes using the same browser for personal use and investigative research, which allows cookies to bridge the two identities.
  • Traffic Obfuscation: Even with a VPN, certain behaviors such as posting on a dark web forum and then using that same connection to check personal banking can expose an IP address, linking it to a practitioner or threat actor.

“Effective OPSEC isn’t about the tools you use; it’s about what breadcrumbs you are leaving behind that hackers, investigation subjects, or literally anyone could find about you.”

Joshua Richards, founder of Osint Praxis

Leveraging the Mindset for CTI

Understanding the OPSEC mindset allows security teams to think like the target. When we know the psychological traps attackers fall in, we know exactly where to look for their mistakes.

AssumptionThe Mindset TrapThe Investigative Reality
Insignificant“I’m not a high-value target; no one is looking for me.”Automated Aggression: Hackers use scripts to scan millions of accounts. You aren’t “chosen”; you are “discovered” via automation.
Invisible“I don’t have a LinkedIn or X account, so I don’t have a footprint.”Shadow Data: Public birth records, property taxes, and historical data breaches create a footprint you didn’t even build yourself.
Invincible“I have 2FA and complex passwords; I’m unhackable.”Session Hijacking: Infostealer malware steals “session tokens” (cookies). This allows an actor to be you in a browser without ever needing your 2FA code.

During the webinar, Joshua shares a masterclass in how leveraging these concepts can turn a vague dark web threat into a real-world arrest. Check out the on-demand webinar to see exactly how the investigation started on Torum, a dark web forum, and ended with an arrest that saved the lives of two individuals.

Turn the Tables Using Flashpoint

The insights shared in this session powerfully illustrate that even the most dangerous threat actors are rarely as anonymous as they believe. Their downfall isn’t usually a failure of their technical prowess, but a failure of their mindset. By understanding these OSINT techniques, intelligence practitioners can transform a sea of digital noise into a clear path toward attribution.

The most effective way to dismantle threats is to bridge the gap between technical indicators and human behavior. Whether your teams are conducting high-stakes OSINT or protecting your own organization’s digital footprint, every breadcrumb counts. By leveraging Flashpoint’s expansive threat intelligence collections and real-time data, you can stay one step ahead of adversaries. Request a demo to learn more.

Request a demo today.

The post The Human Element: Turning Threat Actor OPSEC Fails into Investigative Breakthroughs appeared first on Flashpoint.

How China’s “Walled Garden” is Redefining the Cyber Threat Landscape

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How China’s “Walled Garden” is Redefining the Cyber Threat Landscape

In our latest webinar, Flashpoint unpacks the architecture of the Chinese threat actor cyber ecosystem—a parallel offensive stack fueled by government mandates and commercialized hacker-for-hire industry.

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January 30, 2026

For years, the global cybersecurity community has operated under the assumption that technical information was a matter of public record. Security research has always been openly discussed and shared through a culture of global transparency. Today, that reality has fundamentally shifted. Flashpoint is witnessing a growing opacity—a “Walled Garden”—around Chinese data. As a result, the competence of Chinese threat actors and APTs has reached an industrialized scale.

In Flashpoint’s recent on-demand webinar, “Mapping the Adversary: Inside the Chinese Pentesting Ecosystem,” our analysts explain how China’s state policies surrounding zero-day vulnerability research have effectively shut out the cyber communities that once provided a window into Chinese tradecraft. However, they haven’t disappeared. Rather, they have been absorbed by the state to develop a mature, self-sustaining offensive stack capable of targeting global infrastructure.

Understanding the Walled Garden: The Shift from Disclosure to Nationalization

The “Walled Garden” is a direct result of a Chinese regulatory turning point in 2021: the Regulations on the Management of Security Vulnerabilities (RMSV). While the gradual walling off of China’s data is the cumulative result of years of implementing regulatory and policy strategies, the 2021 RMSV marks a critical turning point that effectively nationalized China’s vulnerability research capabilities. Under the RMSV, any individual or organization in China that discovers a new flaw must report it to the Ministry of Industry and Information Technology (MIIT) within 48 hours. Crucially, researchers are prohibited from sharing technical details with third parties—especially foreign entities—or selling them before a patch is issued.

It is important to note that this mandate is not limited to Chinese-based software or hardware; it applies to any vulnerability discovered, as long as the discoverer is a Chinese-based organization or national. This effectively treats software vulnerabilities as a national strategic resource for China. By centralizing this data, the Chinese government ensures it has an early window into zero-day exploits before the global defensive community. 

For defenders, this means that by the time a vulnerability is public, there is a high probability it has already been analyzed and potentially weaponized within China’s state-aligned apparatus.

The Indigenous Kill Chain: Reconnaissance Beyond Shodan

Flashpoint analysts have observed that within this Walled Garden, traditional Western reconnaissance tools are losing their effectiveness. Chinese threat actors are utilizing an indigenous suite of cyberspace search engines that create a dangerous information asymmetry, allowing them to peer at defender infrastructure while shielding their own domestic base from Western scrutiny.

While Shodan remains the go-to resource for security teams, Flashpoint has seen Chinese threat actors favor three IoT search engines that offer them a massive home-field advantage:

  • FOFA: Specializes in deep fingerprinting for middleware and Chinese-specific signatures, often indexing dorks for new vulnerabilities weeks before they appear in the West.
  • Zoomai: Built for high-speed automation, offering APIs that integrate with AI systems to move from discovery to verified target in minutes.
  • 360 Quake: Provides granular, real-time mapping through a CLI with an AI engine for complex asset portraits.

In the full session, we demonstrate exactly how Chinese operators use these tools to fuse reconnaissance and exploitation into a single, automated step—a capability most Western EDRs aren’t yet tuned to detect.

Building a State-Aligned Offensive Stack

Leveraging their knowledge of vulnerabilities and zero-day exploits, the illicit Chinese ecosystem is building tools designed to dismantle the specific technologies that power global corporate data centers and business hubs.

In the webinar, our analysts explain purpose-built cyber weapons designed to hunt VMware vCenter servers that support one-click shell uploads via vulnerabilities like Log4Shell. Beyond the initial exploit, Flashpoint highlights the rising use of Behinder (Ice Scorpion)—a sophisticated web shell management tool. Behinder has become a staple for Chinese operators because it encrypts command-and-control (C2) traffic, allowing attackers to evade conventional inspection and deep packet analytics.

Strengthen Your Defenses Against the Chinese Offensive Stack with Flashpoint

By understanding this “Walled Garden” architecture, defenders can move beyond generic signatures and begin to hunt for the specific TTPs—such as high-entropy C2 traffic and proprietary Chinese scanning patterns—that define the modern Chinese threat actor.

How can Flashpoint help? Flashpoint’s cyber threat intelligence platform cuts through the generic feed overload and delivers unrivaled primary-source data, AI-powered analysis, and expert human context.

Watch the on-demand webinar to learn more, or request a demo today.

Request a demo today.

The post How China’s “Walled Garden” is Redefining the Cyber Threat Landscape appeared first on Flashpoint.

The Top Threat Actor Groups Targeting the Financial Sector

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The Top Threat Actor Groups Targeting the Financial Sector

In this post, we identify and analyze the top threat actors that have been actively targeting the financial sector between 2024 and 2026.

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January 6, 2026

Between 2024 and 2026, Flashpoint analysts have observed the financial sector as a top target of threat actors, with 406 publicly disclosed victims falling prey to ransomware attacks alone—representing seven percent of all ransomware victim listings during that period.

However, ransomware is just one piece of the complex threat actor puzzle. The financial sector is also grappling with threats stemming from sophisticated Advanced Persistent Threat (APT) groups, the risks associated with third-party compromises, the illicit trade in initial access credentials, the ever-present danger of insider threats, and the emerging challenge of deepfake and impersonation fraud.

Why Finance?

The financial sector has long been one of the most attractive targets for threat actors, consistently ranking among the most targeted industries globally.

These institutions manage massive volumes of sensitive data—from high-value financial transactions and confidential customer information to vast sums of capital, making them especially lucrative for threat actors seeking financial gain. Additionally, the urgency and criticality of financial operations increases the chances that victim organizations will succumb to extortion and ransom demands.

Even beyond direct financial incentives, the financial sector remains an attractive target due to its deep interconnectivity with other industries.This means that malicious actors may simply target financial institutions to gain information about another target organization, as a single data breach can have far-reaching and cascading consequences for involved partners and third parties.

The Threat Actors Targeting the Financial Sector

To understand the complexities of the financial threat landscape, organizations need a comprehensive understanding of the key players involved. The following threat actors represent some of the most prominent and active groups targeting the financial sector between April 2024 and April 2025:

RansomHub

Despite being a relatively new Ransomware-as-a-Service (RaaS) group that emerged in February 2024, RansomHub quickly rose to prominence, becoming the second-most active ransomware group in 2024. Notably, they claimed 38 victims in the financial sector between April 2024 and April 2025. Their known TTPs include phishing and exploiting vulnerabilities. RansomHub is also known to heavily target the healthcare sector.

Akira

Active since March 2023, Akira has demonstrated increasingly sophisticated tactics and has targeted a significant number of victims across various sectors. Between April 2024 and April 2025, they targeted 34 organizations within the financial sector. Evidence suggests a potential link to the defunct Conti ransomware group. Akira commonly gains initial access through compromised credentials, Virtual Private Network (VPN) vulnerabilities, and Remote Desktop Protocol (RDP). They employ a double extortion model, exfiltrating data before encryption.

LockBit Ransomware

A long-standing and highly prolific RaaS group operating since at least September 2019, LockBit continued to be a major threat to the financial sector, claiming 29 publicly disclosed victims between April 2024 and April 2025. LockBit utilizes various initial access methods, including phishing, exploitation of known vulnerabilities, and compromised remote services.

Most notably, in June 2024, LockBit claimed it gained access to the US Federal Reserve, stating that they exfiltrated 33 TB of data. However, Flashpoint analysts found that the data posted on the Federal Reserve listing appears to belong to another victim, Evolve Bank & Trust.

FIN7

This financially motivated threat actor group, originating from Eastern Europe and active since at least 2015, focuses on stealing payment card data. They employ social engineering tactics and create elaborate infrastructure to achieve their goals, reportedly generating over $1 billion USD in revenue between 2015 and 2021. Their targets within the financial sector include interbank transfer systems (SWIFT, SAP), ATM infrastructure, and point-of-sale (POS) terminals. Initial access is often gained through phishing and exploiting public-facing applications.

Scattering Spider

Emerging in 2022, Scattered Spider has quickly become known for its rapid exploitation of compromised environments, particularly targeting financial services, cryptocurrency services, and more. They are notorious for using SMS phishing and fake Okta single sign-on pages to steal credentials and move laterally within networks. Their primary motivation is financial gain.

Lazarus Group

This advanced persistent threat (APT) group, backed by the North Korean government, has demonstrated a broad range of targets, including cryptocurrency exchanges and financial institutions. Their campaigns are driven by financial profit, cyberespionage, and sabotage. Lazarus Group employs sophisticated spear-phishing emails, malware disguised in image files, and watering-hole attacks to gain initial access.

Top Attack Vectors Facing the Financial Sector

Between April 2024 and April 2025, our analysts observed 6,406 posts pertaining to financial sector access listings within Flashpoint’s forum collections. How are these prolific threat actor groups gaining a foothold into financial data and systems? Examining Flashpoint intelligence, malicious actors are capitalizing on third-party compromises, initial access brokers, insider threats, amongst other attack vectors:

Third-Party Compromise

Ransomware attacks targeting third-party vendors can have a direct and significant impact on financial institutions through data exposure and compromised credentials. The Clop ransomware gang’s exploitation of the MOVEit vulnerability in December 2024 serves as a stark reminder of this risk.

Initial Access Brokers (IABs)

Initial Access Brokers specialize in gaining initial access to networks and selling these access credentials to other threat groups, including ransomware operators. Their tactics include phishing, the use of information-stealing malware, and exploiting RDP credentials, posing a significant risk to financial entities. Between April 2024 and April 2025, analysts observed 6,406 posts pertaining to financial sector access listings within Flashpoint’s forum collections.

Insider Threat

Malicious insiders, whether recruited or acting independently, can provide direct access to sensitive data and systems within financial institutions. Telegram has emerged as a prominent platform for advertising and recruiting insider services targeting the financial sector.

Deepfake and Impersonation

The increasing sophistication and accessibility of AI tools are enabling new forms of fraud. Deepfakes can bypass traditional security measures by creating convincing audio and video impersonations. While still evolving, this threat vector, along with other impersonation tactics like BEC and vishing, presents a growing concern for the financial sector. Within the past year, analysts observed 1,238 posts across fraud-related Telegram channels discussing impersonation of individuals working for financial institutions.

Defend Against Financial Threats Using Flashpoint

The financial sector remains a high-value target, facing a persistent and evolving array of threats. Understanding the tactics, techniques, and procedures (TTPs) of these top threat actors, as well as the broader threat landscape, is crucial for financial institutions to develop and implement effective security strategies.

Flashpoint is proud to offer a dedicated threat intelligence solution for banks and financial institutions. Our platform combines comprehensive data collection, AI-powered analysis, and expert human insight to deliver actionable intelligence, safeguarding your critical assets and operations. Request a demo today to see how our intelligence can empower your security team.

Request a demo today.

Beyond the Malware: Inside the Digital Empire of a North Korean Threat Actor

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Beyond the Malware: Inside the Digital Empire of a North Korean Threat Actor

In this post Flashpoint reveals how an infostealer infection on a North Korean threat actor’s machine exposed their digital operational security failures and reliance on AI. Leveraging Flashpoint intelligence, we pivot from a single persona to a network of fake identities and companies targeting the Web3 and crypto industry.

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December 10, 2025

Last week, Hudson Rock published a blog on “Trevor Greer,” a persona tied to a North Korean IT Worker. Flashpoint shared additional insights with our clients back in July, and we’re now making those findings public.

Trevor Greer, a North Korean operative, was identified via an infostealer infection on their own machine. Information-stealing malware, also known as Infostealers or stealers, are malware designed to scrape passwords and cookies from unsuspecting victims. Stealers (like LummaC2 or RedLine) are typically used by cybercriminals to steal login credentials from everyday users to sell on the Dark Web. It is rare to see them infect the machines of a state-sponsored advanced persistent threat group (APT).

However, when adversaries unknowingly infect themselves, they can expose valuable insights into the inner workings of their campaigns. Leveraging Flashpoint intelligence sourced from the leaked logs of “Trevor Greer,” our analysts uncovered a myriad of fake identities and companies used by DPRK APTs.

Finding Trevor Greer

Flashpoint analysts have been tracking the Trevor Greer email address since December 2024 in relation to the “Contagious Interview” campaign, in which threat actors operated as LinkedIn recruiters to target Web3 developers, resulting in the deployment of multiple stealers compromising developer Web3 wallets. Flashpoint also identified the specific persona’s involvement in a campaign in which North Korean threat actors posed as IT freelance workers and applied for jobs at legitimate companies before compromising the organizations internally.

ByBit Compromise

The ByBit compromise in late February 2025 further fueled Flashpoint’s investigations into the Trevor Greer email address. Bybit, a cryptocurrency exchange, suffered a critical incident resulting in North Korean actors extorting US $1.5 billion worth of cryptocurrency. In the aftermath, Silent Push researchers identified the persona “Trevor Greer” associated with the email address trevorgreer9312@gmail[.]com, which registered the domain “Bybit-assessment[.]com” prior to the Bybit compromise.

A later report claimed that the domain “getstockprice[.]com” was involved in the compromise. Despite these domain discrepancies, both investigations attributed the attack to North Korean advanced persistent threat (APT) nexus groups.

Tracing the Infection

Using Flashpoint’s vast intelligence collections, we performed a full investigation of compromised virtual private servers (VPS), revealing the actor’s potential involvement in several other operations, including remote IT work, several self-made blockchain and cryptocurrency exchange companies, and a potential crypto scam dating back to 2022.

Flashpoint analysts also discovered that the Trevor Greer email address was linked to domains infected with information-stealing malware.

What the Logs Revealed

Analysts extracted information about the associated infected host from Trevor Greer, revealing possible tradecraft and tools used. Analysts further identified specific indicators of compromise (IOCs) used in the campaigns mentioned above, as well as email addresses used by the actor for remote work.

The data painted a vivid picture of how these threat actors operate:

Preparation for “Contagious Interviews”

The browser history revealed the actor logging into Willo, a legitimate video interview platform. This suggests the actor was conducting reconnaissance to clone the site for the “Contagious Interview” campaign, where they lured Web3 developers into fake job interviews to deploy malware.

Reliance on AI Tools

The logs exposed the actor’s reliance on AI to bridge the language gap. The operator frequently accessed ChatGPT and Quillbot, likely using them to write convincing emails, build resumes, and generate code for their malware.

Pivoting: One Node to a Network

By analyzing the “Trevor Greer” logs, we were able to pivot to other personas and campaigns involved in the operation.

  • Fake Employment: The logs contained credentials for freelance platforms, such as Upwork and Freelancer, associated with other aliases, including “Kenneth Debolt” and “Fabian Klein.” This confirmed the actor was part of a broader scheme to infiltrate Western companies as remote IT workers.
  • Fake Companies: The data linked the actor to fake corporate entities, such as Block Bounce (blockbounce[.]xyz), a sham crypto trading firm set up to appear legitimate to potential victims. 
  • Developer Personas: The infection data linked the actor to the GitHub account svillalobosdev, which had been active in open source projects to build credibility before the attack.
  • Legitimate Platforms & Tools: Analysts observed the actor using job boards such as Dice and HRapply[.]com, freelance platforms such as Upwork and Freelancer, and direct applications through company Workday sites. To improve their resume, the actor used resumeworded[.]com or cakeresume[.]com. For conversing, the threat actor likely relies on a mix of both GPT and Quilbot, as found in infected host logins, to ensure they sound human. During interviews, analysts determined that they potentially used Speechify. 
  • Deep & Dark Web Resources: The actor also likely purchased Social Security numbers (SSNs) from SSNDOB24[.]com, a site for acquiring Social Security data.

Disrupt Threat Actors Using Flashpoint

The “Trevor Greer” case study illustrates a critical shift in modern threat intelligence. We are no longer limited to analyzing the malware adversaries deploy; sometimes, we can analyze the adversaries themselves.

Using their own tools against them, Flashpoint transformed a faceless state-sponsored entity into a tangible user with bad habits, sloppy OPSEC, and a trail of digital breadcrumbs. Behind every sophisticated APT campaign is a human operator, and sometimes, they click the wrong link too. 

Request a demo today to delve deeper into the tactics, techniques, and procedures of advanced persistent threats and learn how Flashpoint’s intelligence strengthens your defenses.

Request a demo today.

The post Beyond the Malware: Inside the Digital Empire of a North Korean Threat Actor appeared first on Flashpoint.

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