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May 1, 2025

SocGholish: From loader and C2 activity to RansomHub deployment

In early 2025, Darktrace uncovered SocGholish-to-RansomHub intrusion chains, including loader and C2 activity, alongside credential harvesting via WebDAV and SCF abuse. Learn more about SocGholish and its kill chain here!
Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Written by
Christina Kreza
Cyber Analyst
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01
May 2025

Over the past year, a clear pattern has emerged across the threat landscape: ransomware operations are increasingly relying on compartmentalized affiliate models. In these models, initial access brokers (IABs) [6], malware loaders, and post-exploitation operators work together.

Due to those specialization roles, a new generation of loader campaigns has risen. Threat actors increasingly employ loader operators to quietly establish footholds on the target network. These entities then hand off access to ransomware affiliates. One loader that continues to feature prominently in such campaigns is SocGholish.

What is SocGholish?

SocGholish is a loader malware that has been utilized since at least 2017 [7].  It has long been associated with fake browser updates and JavaScript-based delivery methods on infected websites.

Threat actors often target outdated or poorly secured CMS-based websites like WordPress. Through unpatched plugins, or even remote code execution flaws, they inject malicious JavaScript into the site’s HTML, templates or external JS resources [8].  Historically, SocGholish has functioned as a first-stage malware loader, ultimately leading to deployment of Cobalt Strike beacons [9], and further facilitating access persistence to corporate environments. More recently, multiple security vendors have reported that infections involving SocGholish frequently lead to the deployment of RansomHub ransomware [3] [5].

This blog explores multiple instances within Darktrace's customer base where SocGholish deployment led to subsequent network compromises. Investigations revealed indicators of compromise (IoCs) similar to those identified by external security researchers, along with variations in attacker behavior post-deployment. Key innovations in post-compromise activities include credential access tactics targeting authentication mechanisms, particularly through the abuse of legacy protocols like WebDAV and SCF file interactions over SMB.

Initial access and execution

Since January 2025, Darktrace’s Threat Research team observed multiple cases in which threat actors leveraged the SocGholish loader for initial access. Malicious actors commonly deliver SocGholish by compromising legitimate websites by injecting malicious scripts into the HTML of the affected site. When the visitor lands on an infected site, they are typically redirected to a fake browser update page, tricking them into downloading a ZIP file containing a JavaScript-based loader [1] [2]. In one case, a targeted user appears to have visited the compromised website garagebevents[.]com (IP: 35.203.175[.]30), from which around 10 MB of data was downloaded.

Device Event Log showing connections to the compromised website, following by connections to the identified Keitaro TDS instances.
Figure 1: Device Event Log showing connections to the compromised website, following by connections to the identified Keitaro TDS instances.

Within milliseconds of the connection establishment, the user’s device initiated several HTTPS sessions over the destination port 443 to the external endpoint 176.53.147[.]97, linked to the following Keitaro TDS domains:

  • packedbrick[.]com
  • rednosehorse[.]com
  • blackshelter[.]org
  • blacksaltys[.]com

To evade detection, SocGholish uses highly obfuscated code and relies on traffic distribution systems (TDS) [3].  TDS is a tool used in digital and affiliate marketing to manage and distribute incoming web traffic based on predefined rules. More specifically, Keitaro is a premium self-hosted TDS frequently utilized by attackers as a payload repository for malicious scripts following redirects from compromised sites. In the previously noted example, it appears that the device connected to the compromised website, which then retrieved JavaScript code from the aforementioned Keitaro TDS domains. The script served by those instances led to connections to the endpoint virtual.urban-orthodontics[.]com (IP: 185.76.79[.]50), successfully completing SocGholish’s distribution.

Advanced Search showing connections to the compromised website, following by those to the identified Keitaro TDS instances.
Figure 2: Advanced Search showing connections to the compromised website, following by those to the identified Keitaro TDS instances.

Persistence

During some investigations, Darktrace researchers observed compromised devices initiating HTTPS connections to the endpoint files.pythonhosted[.]org (IP: 151.101.1[.]223), suggesting Python package downloads. External researchers have previously noted how attackers use Python-based backdoors to maintain access on compromised endpoints following initial access via SocGholish [5].

Credential access and lateral movement

Credential access – external

Darktrace researchers identified observed some variation in kill chain activities following initial access and foothold establishment. For example, Darktrace detected interesting variations in credential access techniques. In one such case, an affected device attempted to contact the rare external endpoint 161.35.56[.]33 using the Web Distributed Authoring and Versioning (WebDAV) protocol. WebDAV is an extension of the HTTP protocol that allows users to collaboratively edit and manage files on remote web servers. WebDAV enables remote shares to be mounted over HTTP or HTTPS, similar to how SMB operates, but using web-based protocols. Windows supports WebDAV natively, which means a UNC path pointing to an HTTP or HTTPS resource can trigger system-level behavior such as authentication.

In this specific case, the system initiated outbound connections using the ‘Microsoft-WebDAV-MiniRedir/10.0.19045’ user-agent, targeting the URI path of /s on the external endpoint 161.35.56[.]33. During these requests, the host attempted to initiate NTML authentication and even SMB sessions over the web, both of which failed. Despite the session failures, these attempts also indicate a form of forced authentication. Forced authentication exploits a default behavior in Windows where, upon encountering a UNC path, the system will automatically try to authenticate to the resource using NTML – often without any user interaction. Although no files were directly retrieved, the WebDAV server was still likely able to retrieve the user’s NTLM hash during the session establishment requests, which can later be used by the adversary to crack the password offline.

Credential access – internal

In another investigated incident, Darktrace observed a related technique utilized for credential access and lateral movement. This time, the infected host uploaded a file named ‘Thumbs.scf’ to multiple internal SMB network shares. Shell Command File ( SCF) is a legacy Windows file format used primarily for Windows Explorer shortcuts. These files contain instructions for rendering icons or triggering shell commands, and they can be executed implicitly when a user simply opens a folder containing the file – no clicks required.

The ‘Thumbs.scf’ file dropped by the attacker was crafted to exploit this behavior. Its contents included a [Shell] section with the Command=2 directive and an IconFile path pointing to a remote UNC resource on the same external endpoint, 161.35.56[.]33, seen in the previously described case – specifically, ‘\\161.35.56[.]33\share\icon.ico’. When a user on the internal network navigates to the folder containing the SCF file, their system will automatically attempt to load the icon. In doing so, the system issues a request to the specified UNC path, which again prompts Windows to initiate NTML authentication.

This pattern of activity implies that the attacker leveraged passive internal exposure; users who simply browsed a compromised share would unknowingly send their NTML hashes to an external attacker-controlled host. Unlike the WebDAV approach, which required initiating outbound communication from the infected host, this SCF method relies on internal users to interact with poisoned folders.

Figure 3: Contents of the file 'Thumbs.scf' showing the UNC resource hosted on the external endpoint.
Figure 3: Contents of the file 'Thumbs.scf' showing the UNC resource hosted on the external endpoint.

Command-and-control

Following initial compromise, affected devices would then attempt outbound connections using the TLS/SSL protocol over port 443 to different sets of command-and-control (C2) infrastructure associated with SocGholish. The malware frequently uses obfuscated JavaScript loaders to initiate its infection chain, and once dropped, the malware communicates back to its infrastructure over standard web protocols, typically using HTTPS over port 443. However, this set of connections would precede a second set of outbound connections, this time to infrastructure linked to RansomHub affiliates, possibly facilitating the deployed Python-based backdoor.

Connectivity to RansomHub infrastructure relied on defense evasion tactics, such as port-hopping. The idea behind port-hopping is to disguise C2 traffic by avoiding consistent patterns that might be caught by firewalls, and intrusion detection systems. By cycling through ephemeral ports, the malware increases its chances of slipping past basic egress filtering or network monitoring rules that only scrutinize common web traffic ports like 443 or 80. Darktrace analysts identified systems connecting to destination ports such as 2308, 2311, 2313 and more – all on the same destination IP address associated with the RansomHub C2 environment.

Figure 4: Advanced Search connection logs showing connections over destination ports that change rapidly.

Conclusion

Since the beginning of 2025, Darktrace analysts identified a campaign whereby ransomware affiliates leveraged SocGholish to establish network access in victim environments. This activity enabled multiple sets of different post exploitation activity. Credential access played a key role, with affiliates abusing WebDAV and NTML over SMB to trigger authentication attempts. The attackers were also able to plant SCF files internally to expose NTML hashes from users browsing shared folders. These techniques evidently point to deliberate efforts at early lateral movement and foothold expansion before deploying ransomware. As ransomware groups continue to refine their playbooks and work more closely with sophisticated loaders, it becomes critical to track not just who is involved, but how access is being established, expanded, and weaponized.

Credit to Chrisina Kreza (Cyber Analyst) and Adam Potter (Senior Cyber Analyst)

[related-resource]

Appendices

Darktrace / NETWORK model alerts

·       Anomalous Connection / SMB Enumeration

·       Anomalous Connection / Multiple Connections to New External TCP Port

·       Anomalous Connection / Multiple Failed Connections to Rare Endpoint

·       Anomalous Connection / New User Agent to IP Without Hostname

·       Compliance / External Windows Communication

·       Compliance / SMB Drive Write

·       Compromise / Large DNS Volume for Suspicious Domain

·       Compromise / Large Number of Suspicious Failed Connections

·       Device / Anonymous NTML Logins

·       Device / External Network Scan

·       Device / New or Uncommon SMB Named Pipe

·       Device / SMB Lateral Movement

·       Device / Suspicious SMB Activity

·       Unusual Activity / Unusual External Activity

·       User / Kerberos Username Brute Force

MITRE ATT&CK mapping

·       Credential Access – T1187 Forced Authentication

·       Credential Access – T1110 Brute Force

·       Command and Control – T1071.001 Web Protocols

·       Command and Control – T1571 Non-Standard Port

·       Discovery – T1083 File and Directory Discovery

·       Discovery – T1018 Remote System Discovery

·       Discovery – T1046 Network Service Discovery

·       Discovery – T1135 Network Share Discovery

·       Execution – T1059.007 JavaScript

·       Lateral Movement – T1021.002 SMB/Windows Admin Shares

·       Resource Deployment – T1608.004 Drive-By Target

List of indicators of compromise (IoCs)

·       garagebevents[.]com – 35.203.175[.]30 – Possibly compromised website

·       packedbrick[.]com – 176.53.147[.]97 – Keitaro TDS Domains used for SocGholish Delivery

·       rednosehorse[.]com – 176.53.147[.]97 – Keitaro TDS Domains used for SocGholish Delivery

·       blackshelter[.]org – 176.53.147[.]97 – Keitaro TDS Domains used for SocGholish Delivery

·       blacksaltys[.]com – 176.53.147[.]97 – Keitaro TDS Domains used for SocGholish Delivery

·       virtual.urban-orthodontics[.]com – 185.76.79[.]50

·       msbdz.crm.bestintownpro[.]com – 166.88.182[.]126 – SocGholish C2

·       185.174.101[.]240 – RansomHub Python C2

·       185.174.101[.]69 – RansomHub Python C2

·       108.181.182[.]143 – RansomHub Python C2

References

[1] https://www.checkpoint.com/cyber-hub/threat-prevention/what-is-malware/socgholish-malware/

[2] https://intel471.com/blog/threat-hunting-case-study-socgholish

[3] https://www.trendmicro.com/en_us/research/25/c/socgholishs-intrusion-techniques-facilitate-distribution-of-rans.html

[4] https://www.proofpoint.com/us/blog/threat-insight/update-fake-updates-two-new-actors-and-new-mac-malware

[5] https://www.guidepointsecurity.com/blog/ransomhub-affiliate-leverage-python-based-backdoor/

[6] https://www.cybereason.com/blog/how-do-initial-access-brokers-enable-ransomware-attacks

[7] https://attack.mitre.org/software/S1124/

[8] https://expel.com/blog/incident-report-spotting-socgholish-wordpress-injection/

[9] https://www.esentire.com/blog/socgholish-to-cobalt-strike-in-10-minutes

Get the latest insights on emerging cyber threats

This report explores the latest trends shaping the cybersecurity landscape and what defenders need to know in 2025

Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Written by
Christina Kreza
Cyber Analyst

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December 18, 2025

Why organizations are moving to label-free, behavioral DLP for outbound email

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Why outbound email DLP needs reinventing

In 2025, the global average cost of a data breach fell slightly — but remains substantial at USD 4.44 million (IBM Cost of a Data Breach Report 2025). The headline figure hides a painful reality: many of these breaches stem not from sophisticated hacks, but from simple human error: mis-sent emails, accidental forwarding, or replying with the wrong attachment. Because outbound email is a common channel for sensitive data leaving an organization, the risk posed by everyday mistakes is enormous.

In 2025, 53% of data breaches involved customer PII, making it the most commonly compromised asset (IBM Cost of a Data Breach Report 2025). This makes “protection at the moment of send” essential. A single unintended disclosure can trigger compliance violations, regulatory scrutiny, and erosion of customer trust –consequences that are disproportionate to the marginal human errors that cause them.

Traditional DLP has long attempted to mitigate these impacts, but it relies heavily on perfect labelling and rigid pattern-matching. In reality, data loss rarely presents itself as a neat, well-structured pattern waiting to be caught – it looks like everyday communication, just slightly out of context.

How data loss actually happens

Most data loss comes from frustratingly familiar scenarios. A mistyped name in auto-complete sends sensitive data to the wrong “Alex.” A user forwards a document to a personal Gmail account “just this once.” Someone shares an attachment with a new or unknown correspondent without realizing how sensitive it is.

Traditional, content-centric DLP rarely catches these moments. Labels are missing or wrong. Regexes break the moment the data shifts formats. And static rules can’t interpret the context that actually matters – the sender-recipient relationship, the communication history, or whether this behavior is typical for the user.

It’s the everyday mistakes that hurt the most. The classic example: the Friday 5:58 p.m. mis-send, when auto-complete selects Martin, a former contractor, instead of Marta in Finance.

What traditional DLP approaches offer (and where gaps remain)

Most email DLP today follows two patterns, each useful but incomplete.

  • Policy- and label-centric DLP works when labels are correct — but content is often unlabeled or mislabeled, and maintaining classification adds friction. Gaps appear exactly where users move fastest
  • Rule and signature-based approaches catch known patterns but miss nuance: human error, new workflows, and “unknown unknowns” that don’t match a rule

The takeaway: Protection must combine content + behavior + explainability at send time, without depending on perfect labels.

Your technology primer: The three pillars that make outbound DLP effective

1) Label-free (vs. data classification)

Protects all content, not just what’s labeled. Label-free analysis removes classification overhead and closes gaps from missing or incorrect tags. By evaluating content and context at send time, it also catches misdelivery and other payload-free errors.

  • No labeling burden; no regex/rule maintenance
  • Works when tags are missing, wrong, or stale
  • Detects misdirected sends even when labels look right

2) Behavioral (vs. rules, signatures, threat intelligence)

Understands user behavior, not just static patterns. Behavioral analysis learns what’s normal for each person, surfacing human error and subtle exfiltration that rules can’t. It also incorporates account signals and inbound intel, extending across email and Teams.

  • Flags risk without predefined rules or IOCs
  • Catches misdelivery, unusual contacts, personal forwards, odd timing/volume
  • Blends identity and inbound context across channels

3) Proprietary DSLM (vs. generic LLM)

Optimized for precise, fast, explainable on-send decisions. A DSLM understands email/DLP semantics, avoids generative risks, and stays auditable and privacy-controlled, delivering intelligence reliably without slowing mail flow.

  • Low-latency, on-send enforcement
  • Non-generative for predictable, explainable outcomes
  • Governed model with strong privacy and auditability

The Darktrace approach to DLP

Darktrace / EMAIL – DLP stops misdelivery and sensitive data loss at send time using hold/notify/justify/release actions. It blends behavioral insight with content understanding across 35+ PII categories, protecting both labeled and unlabeled data. Every action is paired with clear explainability: AI narratives show exactly why an email was flagged, supporting analysts and helping end-users learn. Deployment aligns cleanly with existing SOC workflows through mail-flow connectors and optional Microsoft Purview label ingestion, without forcing duplicate policy-building.

Deployment is simple: Microsoft 365 routes outbound mail to Darktrace for real-time, inline decisions without regex or rule-heavy setup.

A buyer’s checklist for DLP solutions

When choosing your DLP solution, you want to be sure that it can deliver precise, explainable protection at the moment it matters – on send – without operational drag.  

To finish, we’ve compiled a handy list of questions you can ask before choosing an outbound DLP solution:

  • Can it operate label free when tags are missing or wrong? 
  • Does it truly learn per user behavior (no shortcuts)? 
  • Is there a domain specific model behind the content understanding (not a generic LLM)? 
  • Does it explain decisions to both analysts and end users? 
  • Will it integrate with your label program and SOC workflows rather than duplicate them? 

For a deep dive into Darktrace’s DLP solution, check out the full solution brief.

[related-resource]

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Carlos Gray
Senior Product Marketing Manager, Email

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December 17, 2025

Beyond MFA: Detecting Adversary-in-the-Middle Attacks and Phishing with Darktrace

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What is an Adversary-in-the-middle (AiTM) attack?

Adversary-in-the-Middle (AiTM) attacks are a sophisticated technique often paired with phishing campaigns to steal user credentials. Unlike traditional phishing, which multi-factor authentication (MFA) increasingly mitigates, AiTM attacks leverage reverse proxy servers to intercept authentication tokens and session cookies. This allows attackers to bypass MFA entirely and hijack active sessions, stealthily maintaining access without repeated logins.

This blog examines a real-world incident detected during a Darktrace customer trial, highlighting how Darktrace / EMAILTM and Darktrace / IDENTITYTM identified the emerging compromise in a customer’s email and software-as-a-service (SaaS) environment, tracked its progression, and could have intervened at critical moments to contain the threat had Darktrace’s Autonomous Response capability been enabled.

What does an AiTM attack look like?

Inbound phishing email

Attacks typically begin with a phishing email, often originating from the compromised account of a known contact like a vendor or business partner. These emails will often contain malicious links or attachments leading to fake login pages designed to spoof legitimate login platforms, like Microsoft 365, designed to harvest user credentials.

Proxy-based credential theft and session hijacking

When a user clicks on a malicious link, they are redirected through an attacker-controlled proxy that impersonates legitimate services.  This proxy forwards login requests to Microsoft, making the login page appear legitimate. After the user successfully completes MFA, the attacker captures credentials and session tokens, enabling full account takeover without the need for reauthentication.

Follow-on attacks

Once inside, attackers will typically establish persistence through the creation of email rules or registering OAuth applications. From there, they often act on their objectives, exfiltrating sensitive data and launching additional business email compromise (BEC) campaigns. These campaigns can include fraudulent payment requests to external contacts or internal phishing designed to compromise more accounts and enable lateral movement across the organization.

Darktrace’s detection of an AiTM attack

At the end of September 2025, Darktrace detected one such example of an AiTM attack on the network of a customer trialling Darktrace / EMAIL and Darktrace / IDENTITY.

In this instance, the first indicator of compromise observed by Darktrace was the creation of a malicious email rule on one of the customer’s Office 365 accounts, suggesting the account had likely already been compromised before Darktrace was deployed for the trial.

Darktrace / IDENTITY observed the account creating a new email rule with a randomly generated name, likely to hide its presence from the legitimate account owner. The rule marked all inbound emails as read and deleted them, while ignoring any existing mail rules on the account. This rule was likely intended to conceal any replies to malicious emails the attacker had sent from the legitimate account owner and to facilitate further phishing attempts.

Darktrace’s detection of the anomalous email rule creation.
Figure 1: Darktrace’s detection of the anomalous email rule creation.

Internal and external phishing

Following the creation of the email rule, Darktrace / EMAIL observed a surge of suspicious activity on the user’s account. The account sent emails with subject lines referencing payment information to over 9,000 different external recipients within just one hour. Darktrace also identified that these emails contained a link to an unusual Google Drive endpoint, embedded in the text “download order and invoice”.

Darkrace’s detection of an unusual surge in outbound emails containing suspicious content, shortly following the creation of a new email rule.
Figure 2: Darkrace’s detection of an unusual surge in outbound emails containing suspicious content, shortly following the creation of a new email rule.
Darktrace / EMAIL’s detection of the compromised account sending over 9,000 external phishing emails, containing an unusual Google Drive link.
Figure 3: Darktrace / EMAIL’s detection of the compromised account sending over 9,000 external phishing emails, containing an unusual Google Drive link.

As Darktrace / EMAIL flagged the message with the ‘Compromise Indicators’ tag (Figure 2), it would have been held automatically if the customer had enabled default Data Loss Prevention (DLP) Action Flows in their email environment, preventing any external phishing attempts.

Figure 4: Darktrace / EMAIL’s preview of the email sent by the offending account.
Figure 4: Darktrace / EMAIL’s preview of the email sent by the offending account.

Darktrace analysis revealed that, after clicking the malicious link in the email, recipients would be redirected to a convincing landing page that closely mimicked the customer’s legitimate branding, including authentic imagery and logos, where prompted to download with a PDF named “invoice”.

Figure 5: Download and login prompts presented to recipients after following the malicious email link, shown here in safe view.

After clicking the “Download” button, users would be prompted to enter their company credentials on a page that was likely a credential-harvesting tool, designed to steal corporate login details and enable further compromise of SaaS and email accounts.

Darktrace’s Response

In this case, Darktrace’s Autonomous Response was not fully enabled across the customer’s email or SaaS environments, allowing the compromise to progress,  as observed by Darktrace here.

Despite this, Darktrace / EMAIL’s successful detection of the malicious Google Drive link in the internal phishing emails prompted it to suggest ‘Lock Link’, as a recommended action for the customer’s security team to manually apply. This action would have automatically placed the malicious link behind a warning or screening page blocking users from visiting it.

Autonomous Response suggesting locking the malicious Google Drive link sent in internal phishing emails.
Figure 6: Autonomous Response suggesting locking the malicious Google Drive link sent in internal phishing emails.

Furthermore, if active in the customer’s SaaS environment, Darktrace would likely have been able to mitigate the threat even earlier, at the point of the first unusual activity: the creation of a new email rule. Mitigative actions would have included forcing the user to log out, terminating any active sessions, and disabling the account.

Conclusion

AiTM attacks represent a significant evolution in credential theft techniques, enabling attackers to bypass MFA and hijack active sessions through reverse proxy infrastructure. In the real-world case we explored, Darktrace’s AI-driven detection identified multiple stages of the attack, from anomalous email rule creation to suspicious internal email activity, demonstrating how Autonomous Response could have contained the threat before escalation.

MFA is a critical security measure, but it is no longer a silver bullet. Attackers are increasingly targeting session tokens rather than passwords, exploiting trusted SaaS environments and internal communications to remain undetected. Behavioral AI provides a vital layer of defense by spotting subtle anomalies that traditional tools often miss

Security teams must move beyond static defenses and embrace adaptive, AI-driven solutions that can detect and respond in real time. Regularly review SaaS configurations, enforce conditional access policies, and deploy technologies that understand “normal” behavior to stop attackers before they succeed.

Credit to David Ison (Cyber Analyst), Bertille Pierron (Solutions Engineer), Ryan Traill (Analyst Content Lead)

Appendices

Models

SaaS / Anomalous New Email Rule

Tactic – Technique – Sub-Technique  

Phishing - T1566

Adversary-in-the-Middle - T1557

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About the author
David Ison
Cyber Analyst
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