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February 11, 2021

Detecting IoT Threats in Control Systems

Discover how Darktrace uncovers pre-existing threats in Industrial IoT systems. Learn about advanced detection techniques in industrial control systems.
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
David Masson
VP, Field CISO
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11
Feb 2021

Industrial IoT (IIoT) devices are a pressing concern for security teams. Companies invest large sums of money to keep cyber-criminals out of industrial systems, but what happens when the hacker is already inside? Gateways and legacy security tools generally sit at the border of an organization and are designed to stop external threats, but are less effective once the threat is already inside. During this period, cyber-criminals carry out further reconnaissance, tamper with PLC settings, and subtly disrupt the production process.

Darktrace recently detected a series of pre-existing infections in Industrial IoT (IIoT) devices at a manufacturing firm in the EMEA region. The organization already had Darktrace in place in one area of the environment, but after seeing how the AI could successfully detect zero-day vulnerabilities and threats, they expanded the deployment, allowing Darktrace to actively monitor and defend interactions among its 5,000 devices, and dramatically improving visibility.

An unknown emerging threat was identified by Darktrace / OT omultiple machines within hours of Darktrace being active in the environment. By casting light on this previously unknown threat, Darktrace enabled the customer to perform full incident response and threat investigation, before the attacker was able to cause any serious damage to the company.

Though it is unclear how long the devices had been infected, it is likely to have been first introduced manually via an infected USB. The affected endpoints were being used as part of a continuous production process and could not be installed with endpoint protection.

Darktrace / OT; however, easily detects infections across the digital estate, regardless of the type of environment or technology. Darktrace AI does not rely on signature-based methods but instead continuously updates its understanding of what constitutes ‘normal’ in an industrial environment. This self-learning approach allows the AI to contain zero-days that have never been seen before in the wild, as well as detecting the new appearance of pre-existing attacks.

Industrial IoT attacked

Only a few hours after Darktrace AI had begun defending the wider connections and interactions across the manufacturing firm, Darktrace detected a highly unusual network scan. A timeline of events, from first scan to full incident response results and conclusions, is shown below:

Figure 1: Timeline of incident response across 28 hours

Darktrace’s AI recognized that the device was exploiting an SMBv1 protocol in order to attempt lateral movement. In addition to anonymous SMBv1 authentication, Darktrace detected the device abusing default vendor credentials for device enumeration.

The device made a large number of unusual connections, including connections to internal endpoints which the company had previously been unaware of. As these occurred, the Threat Visualizer, Darktrace’s user interface, provided a graphical visualization of the incident, illuminating the unusual activity’s spread from the infected device across the infrastructure in question.

Figure 2: The Darktrace Threat Visualizer

Darktrace identified that the infected Industrial IoT device was making an unusually large number of internal connections, suggesting an effort to perform reconnaissance.

Darktrace’s Cyber AI Analyst launched an immediate investigation into the alert, surfacing an incident summary at machine speed with all the information the security team needed to act.

Figure 3: An example of an AI Analyst Report on a network scan

The Cyber AI Analyst further identified two other devices behaving in a similar way, and these were removed from the network by the customer in response. When investigated by the security team, these devices were shown to be infected with the Yalove and Renocide worms, and the Autoit trojan-dropper. Open source intelligence suggests these infections are often spread via removable media such as USB drives.

Using Darktrace’s Advanced Search function, the customer was able to investigate related model breaches to build a list of similar indicators of compromise (IoCs), including failed external connections to www.whatismyip[.]com and DYNDNS IP addresses on HTTP port 80.

Recurring infections: How to deal with a persistent attack

In total, Darktrace was used to identify 13 infected production devices. The customer contacted the equipment owner, whose response confirmed that they had seen similar attacks on other networks in the past, including recurring infections.

Recurring infections imply one of two things: either, that the malware has a persistence mechanism, where it uses a range of techniques to remain undetected on the exploited machine and achieve persistent access to the system. Alternatively, a recurring infection could mean that the IoT manufacturer was not able to find all infected devices when they were first alerted to the compromise, and thus did not shut down the attack in its entirety.

As the infected machines are owned by a third party, they could not be immediately remediated. Darktrace AI, however, contained this threat with minimal business disruption. The customer was able to leave the infected devices active, which were still needed for production, confident that Darktrace would alert them if the infection spread or changed in behavior.

Industrial IoT: Shining a light on pre-existing threats

The mass adoption of Industrial IoT devices has made industrial environments more complex and more vulnerable than ever. This blog demonstrates the prevalent threat that attackers are already on the inside, and the importance for security teams to expand visibility over their full industrial system. In this case, the customer was able to use Darktrace’s AI to illuminate a previous blind spot and contain a persistent attack, while minimizing disruption to operations. Crucially, this ‘unknown known’ threat was detected without any prior knowledge of the devices, their supplier, or patch history, and without using malware signatures or IoCs.

The customer was made aware of the infection via the Darktrace SOC service. Yet the same outcome could have been obtained with other workflows provided by Darktrace, such as email alerting, notifications through the Darktrace mobile app, seamlessly integrating Darktrace with a SIEM solution, or alerting via an internal SOC.

Cyber AI Analyst enabled the customer to perform immediate incident response. While waiting for a reinstallation date with the equipment owner, the customer could keep the production devices online, knowing Darktrace would be monitoring the outstanding risk. In an industrial setting, trade-offs like this are often necessary to sustain production. Darktrace helps organizations maintain the vigilance they need to do this securely, and when remediation does become possible, Darktrace can be used to reliably locate the full extent of the infection.

Thanks to Darktrace analyst Oakley Cox for his insights on the above threat find.

Darktrace model detections:

  • Device / Suspicious Network Scan Activity [Enhanced Monitoring]
  • Device / ICMP Address Scan
  • ICS / Anomalous IT to ICS Connection
  • Anomalous Connection / SMB Enumeration
  • Device / Network Scan

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
David Masson
VP, Field CISO

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May 6, 2025

Combatting the Top Three Sources of Risk in the Cloud

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With cloud computing, organizations are storing data like intellectual property, trade secrets, Personally Identifiable Information (PII), proprietary code and statistics, and other sensitive information in the cloud. If this data were to be accessed by malicious actors, it could incur financial loss, reputational damage, legal liabilities, and business disruption.

Last year data breaches in solely public cloud deployments were the most expensive type of data breach, with an average of $5.17 million USD, a 13.1% increase from the year before.

So, as cloud usage continues to grow, the teams in charge of protecting these deployments must understand the associated cybersecurity risks.

What are cloud risks?

Cloud threats come in many forms, with one of the key types consisting of cloud risks. These arise from challenges in implementing and maintaining cloud infrastructure, which can expose the organization to potential damage, loss, and attacks.

There are three major types of cloud risks:

1. Misconfigurations

As organizations struggle with complex cloud environments, misconfiguration is one of the leading causes of cloud security incidents. These risks occur when cloud settings leave gaps between cloud security solutions and expose data and services to unauthorized access. If discovered by a threat actor, a misconfiguration can be exploited to allow infiltration, lateral movement, escalation, and damage.

With the scale and dynamism of cloud infrastructure and the complexity of hybrid and multi-cloud deployments, security teams face a major challenge in exerting the required visibility and control to identify misconfigurations before they are exploited.

Common causes of misconfiguration come from skill shortages, outdated practices, and manual workflows. For example, potential misconfigurations can occur around firewall zones, isolated file systems, and mount systems, which all require specialized skill to set up and diligent monitoring to maintain

2. Identity and Access Management (IAM) failures

IAM has only increased in importance with the rise of cloud computing and remote working. It allows security teams to control which users can and cannot access sensitive data, applications, and other resources.

Cybersecurity professionals ranked IAM skills as the second most important security skill to have, just behind general cloud and application security.

There are four parts to IAM: authentication, authorization, administration, and auditing and reporting. Within these, there are a lot of subcomponents as well, including but not limited to Single Sign-On (SSO), Two-Factor Authentication (2FA), Multi-Factor Authentication (MFA), and Role-Based Access Control (RBAC).

Security teams are faced with the challenge of allowing enough access for employees, contractors, vendors, and partners to complete their jobs while restricting enough to maintain security. They may struggle to track what users are doing across the cloud, apps, and on-premises servers.

When IAM is misconfigured, it increases the attack surface and can leave accounts with access to resources they do not need to perform their intended roles. This type of risk creates the possibility for threat actors or compromised accounts to gain access to sensitive company data and escalate privileges in cloud environments. It can also allow malicious insiders and users who accidentally violate data protection regulations to cause greater damage.

3. Cross-domain threats

The complexity of hybrid and cloud environments can be exploited by attacks that cross multiple domains, such as traditional network environments, identity systems, SaaS platforms, and cloud environments. These attacks are difficult to detect and mitigate, especially when a security posture is siloed or fragmented.  

Some attack types inherently involve multiple domains, like lateral movement and supply chain attacks, which target both on-premises and cloud networks.  

Challenges in securing against cross-domain threats often come from a lack of unified visibility. If a security team does not have unified visibility across the organization’s domains, gaps between various infrastructures and the teams that manage them can leave organizations vulnerable.

Adopting AI cybersecurity tools to reduce cloud risk

For security teams to defend against misconfigurations, IAM failures, and insecure APIs, they require a combination of enhanced visibility into cloud assets and architectures, better automation, and more advanced analytics. These capabilities can be achieved with AI-powered cybersecurity tools.

Such tools use AI and automation to help teams maintain a clear view of all their assets and activities and consistently enforce security policies.

Darktrace / CLOUD is a Cloud Detection and Response (CDR) solution that makes cloud security accessible to all security teams and SOCs by using AI to identify and correct misconfigurations and other cloud risks in public, hybrid, and multi-cloud environments.

It provides real-time, dynamic architectural modeling, which gives SecOps and DevOps teams a unified view of cloud infrastructures to enhance collaboration and reveal possible misconfigurations and other cloud risks. It continuously evaluates architecture changes and monitors real-time activity, providing audit-ready traceability and proactive risk management.

Real-time visibility into cloud assets and architectures built from network, configuration, and identity and access roles. In this unified view, Darktrace / CLOUD reveals possible misconfigurations and risk paths.
Figure 1: Real-time visibility into cloud assets and architectures built from network, configuration, and identity and access roles. In this unified view, Darktrace / CLOUD reveals possible misconfigurations and risk paths.

Darktrace / CLOUD also offers attack path modeling for the cloud. It can identify exposed assets and highlight internal attack paths to get a dynamic view of the riskiest paths across cloud environments, network environments, and between – enabling security teams to prioritize based on unique business risk and address gaps to prevent future attacks.  

Darktrace’s Self-Learning AI ensures continuous cloud resilience, helping teams move from reactive to proactive defense.

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About the author
Pallavi Singh
Product Marketing Manager, OT Security & Compliance

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May 2, 2025

SocGholish: From loader and C2 activity to RansomHub deployment

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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)

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

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About the author
Christina Kreza
Cyber Analyst
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