Proactive OT Security: Lessons on Supply Chain Risk Management from a Rogue Raspberry Pi
Darktrace detected a rogue Raspberry PI device that had been left by a Manufacturing customer’s vendor in the customer’s ICS network. The convergence between supply chain risk and insider risk highlights how important it is to implement continuous monitoring of the internal ICS network for proactive risk management.
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
Nicole Wong
Cyber Security Analyst
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11
Jun 2025
Understanding supply chain risk in manufacturing
For industries running Industrial Control Systems (ICS) such as manufacturing and fast-moving consumer goods (FMCG), complex supply chains mean that disruption to one weak node can have serious impacts to the entire ecosystem. However, supply chain risk does not always originate from outside an organization’s ICS network.
The implicit trust placed on software or shared services for maintenance within an ICS can be considered a type of insider threat [1], where defenders also need to look ‘from within’ to protect against supply chain risk. Attackers have frequently mobilised this form of insider threat:
Many ICS and SCADA systems were compromised during the 2014 Havex Watering Hole attack, where via operators’ implicit trust in the trojanized versions of legitimate applications, on legitimate but compromised websites [2].
In 2018, the world’s largest manufacturer of semiconductors and processers shut down production for three days after a supplier installed tainted software that spread to over 10,000 machines in the manufacturer’s network [3].
During the 2020 SolarWinds supply chain attack, attackers compromised a version of Orion software that was deployed from SolarWinds’ own servers during a software update to thousands of customers, including tech manufacturing companies such as Intel and Nvidia [4].
Traditional approaches to ICS security have focused on defending against everything from outside the castle walls, or outside of the ICS network. As ICS attacks become more sophisticated, defenders must not solely rely on static perimeter defenses and prevention.
A critical part of active defense is understanding the ICS environment and how it operates, including all possible attack paths to the ICS including network connections, remote access points, the movement of data across zones and conduits and access from mobile devices. For instance, original equipment manufacturers (OEMs) and vendors often install remote access software or third-party equipment in ICS networks to facilitate legitimate maintenance and support activities, which can unintentionally expand the ICS’ attack surface.
This blog describes an example of the convergence between supply chain risk and insider risk, when a vendor left a Raspberry Pi device in a manufacturing customer’s ICS network without the customer’s knowledge.
Case study: Using unsupervised machine learning to detect pre-existing security issues
Raspberry Pi devices are commonly used in SCADA environments as low-cost, remotely accessible data collectors [5][6][7]. They are often paired with Industrial Internet of Things (IIoT) for monitoring and tracking [8]. However, these devices also represent a security risk because their small physical size and time-consuming nature of physical inspection makes them easy to overlook. This poses a security risk, as these devices have previously been used to carry out USB-based attacks or to emulate Ethernet-over-USB connections to exfiltrate sensitive data [8][9].
In this incident,a Darktrace customer was unaware that their supplier had installed a Raspberry Pi device on their ICS network. Crucially, the installation occurred prior to Darktrace’s deployment on the customer’s network.
For other anomaly detection tools, this order of events meant that this third-party device would likely have been treated as part of the customer’s existing infrastructure. However, after Darktrace was deployed, it analyzed the metadata from the encrypted HTTPS and DNS connections that the Raspberry Pi made to ‘call home’ to the supplier and determined that these connections were unusual compared to the rest of the devices in the network, even in the absence of any malicious indicators of compromise (IoCs).
Darktrace triggered the following alerts for this unusual activity that consequently notified the customer to the pre-existing threat of an unmanaged device already present in their network:
Compromise / Sustained SSL or HTTP Increase
Compromise / Agent Beacon (Short Period)
Compromise / Agent Beacon (Medium Period)
Compromise / Agent Beacon (Long Period)
Tags / New Raspberry Pi Device
Device / DNS Requests to Unusual Server
Device / Anomaly Indicators / Spike in Connections to Rare Endpoint Indicator
Figure 1: Darktrace’s External Sites Summary showing the rarity of the external endpoint that the Raspberry Pi device ‘called home’ to and the model alerts triggered.
Darktrace’s Cyber AI Analyst launched an autonomous investigation into the activity, correlating related events into a broader incident and generating a report outlining the potential threat along with supporting technical details.
Darktrace’s anomaly-based detection meant that the Raspberry Pi device did not need to be observed performing clearly malicious behavior to alert the customer to the security risk, and neither can defenders afford to wait for such escalation.
Why is this significant?
In 2021 a similar attack took place. Aiming to poison a Florida water treatment facility, attackers leveraged a TeamViewer instance that had been dormant on the system for six months, effectively allowing the attacker to ‘live off the land’ [10].
The Raspberry Pi device in this incident also remained outside the purview of the customer’s security team at first. It could have been leveraged by a persistent attacker to pivot within the internal network and communicate externally.
A proactive approach to active defense that seeks to minimize and continuously monitor the attack surface and network is crucial.
The growing interest in manufacturing from attackers and policymakers
Significant motivations for targeting the manufacturing sector and increasing regulatory demands make the convergence of supply chain risk, insider risk, and the prevalence of stealthy living-off-the-land techniques particularly relevant to this sector.
Manufacturing is consistently targeted by cybercriminals [11], and the sector’s ‘just-in-time’ model grants attackers the opportunity for high levels of disruption. Furthermore, under NIS 2, manufacturing and some food and beverage processing entities are now designated as ‘important’ entities. This means stricter incident reporting requirements within 24 hours of detection, and enhanced security requirements such as the implementation of zero trust and network segmentation policies, as well as measures to improve supply chain resilience [12][13][14].
How can Darktrace help?
Ultimately, Darktrace successfully assisted a manufacturing organization in detecting a potentially disruptive 'near-miss' within their OT environment, even in the absence of traditional IoCs. Through passive asset identification techniques and continuous network monitoring, the customer improved their understanding of their network and supply chain risk.
While the swift detection of the rogue device allowed the threat to be identified before it could escalate, the customer could have reduced their time to respond by using Darktrace’s built-in response capabilities, had Darktrace’s Autonomous Response capability been enabled. Darktrace’s Autonomous Response can be configured to target specific connections on a rogue device either automatically upon detection or following manual approval from the security team, to stop it communicating with other devices in the network while allowing other approved devices to continue operating. Furthermore, the exportable report generated by Cyber AI Analyst helps security teams to meet NIS 2’s enhanced reporting requirements.
Sophisticated ICS attacks often leverage insider access to perform in-depth reconnaissance for the development of tailored malware capabilities. This case study and high-profile ICS attacks highlight the importance of mitigating supply chain risk in a similar way to insider risk. As ICS networks adapt to the introduction of IIoT, remote working and the increased convergence between IT and OT, it is important to ensure the approach to secure against these threats is compatible with the dynamic nature of the network.
Credit to Nicole Wong (Principal Cyber Analyst), Matthew Redrup (Senior Analyst and ANZ Team Lead)
[related-resource]
Appendices
MITRE ATT&CK Mapping
Infrastructure / New Raspberry Pi Device - INITIAL ACCESS - T1200 Hardware Additions
Device / DNS Requests to Unusual Server - CREDENTIAL ACCESS, COLLECTION - T1557 Man-in-the-Middle
Compromise / Agent Beacon - COMMAND AND CONTROL - T1071.001 Web Protocols
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.
Announcing Unified OT Security with Dedicated OT Workflows, Segmentation-Aware Risk Insights, and Next-Gen Endpoint Visibility for Industrial Teams
The latest release of Darktrace / OT brings powerful new innovations to security teams defending industrial infrastructure. With a dedicated OT dashboard, segmentation-aware risk modeling, and expanded visibility into edge assets and automation protocols, Darktrace / OT empowers engineers and defenders with the context and control they need to protect today’s converged environments, from IT to the industrial edge.
Pallavi Singh
Product Marketing Manager, OT Security & Compliance
Rethinking Signature-Based Detection for Power Utility Cybersecurity
Signature-based detection has been a mainstay of IT security for decades, but the unique realities of operational technology (OT) environments make it a poor return on investment for power utilities. While signatures can still provide value for commodity IT malware, they fail against insider misuse, zero-day exploits, and custom-built malware designed for grid operations. Historical evidence shows signatures rarely generalize across different utilities, and overreliance creates strategic blind spots. To effectively defend critical energy infrastructure, utilities should prioritize behavioral analytics, anomaly detection, and intelligence sharing across the community.
Adapting to new USCG cybersecurity mandates: Darktrace for ports and maritime systems
Darktrace uses AI-led OT, IoT, and IT Network Security to help secure maritime transportation systems. This blog describes some of the new mandated requirements by the USCG and demonstrates Darktrace’s security capabilities.
How a Major Civil Engineering Company Reduced MTTR across Network, Email and the Cloud with Darktrace
Asking more of the information security team
“What more can we be doing to secure the company?” is a great question for any cyber professional to hear from their Board of Directors. After successfully defeating a series of attacks and seeing the potential for AI tools to supercharge incoming threats, a UK-based civil engineering company’s security team had the answer: Darktrace.
“When things are coming at you at machine speed, you need machine speed to fight it off – it’s as simple as that,” said their Information Security Manager. “There were incidents where it took us a few hours to get to the bottom of what was going on. Darktrace changed that.”
Prevention was also the best cure. A peer organization in the same sector was still in business continuity measures 18 months after an attack, and the security team did not want to risk that level of business disruption.
Legacy tools were not meeting the team’s desired speed or accuracy
The company’s native SaaS email platform took between two and 14 days to alert on suspicious emails, with another email security tool flagging malicious emails after up to 24 days. After receiving an alert, responses often took a couple of days to coordinate. The team was losing precious time.
Beyond long detection and response times, the old email security platform was no longer performing: 19% of incoming spam was missed. Of even more concern: 6% of phishing emails reached users’ inboxes, and malware and ransomware email was also still getting through, with 0.3% of such email-borne payloads reaching user inboxes.
Choosing Darktrace
“When evaluating tools in 2023, only Darktrace had what I was looking for: an existing, mature, AI-based cybersecurity solution. ChatGPT had just come out and a lot of companies were saying ‘AI this’ and ‘AI that’. Then you’d take a look, and it was all rules- and cases-based, not AI at all,” their Information Security Manager.
The team knew that, with AI-enabled attacks on the horizon, a cybersecurity company that had already built, fielded, and matured an AI-powered cyber defense would give the security team the ability to fend off machine-speed attacks at the same pace as the attackers.
Darktrace accomplishes this with multi-layered AI that learns each organization’s normal business operations. With this detailed level of understanding, Darktrace’s Self-Learning AI can recognize unusual activity that may indicate a cyber-attack, and works to neutralize the threat with precise response actions. And it does this all at machine speed and with minimal disruption.
On the morning the team was due to present its findings, the session was cancelled – for a good reason. The Board didn’t feel further discussion was necessary because the case for Darktrace was so conclusive. The CEO described the Darktrace option as ‘an insurance policy we can’t do without’.
Saving time with Darktrace / EMAIL
Darktrace / EMAIL reduced the discovery, alert, and response process from days or weeks to seconds .
Darktrace / EMAIL automates what was originally a time-consuming and repetitive process. The team has recovered between eight and 10 working hours a week by automating much of this process using / EMAIL.
Today, Darktrace / EMAIL prevents phishing emails from reaching employees’ inboxes. The volume of hostile and unsolicited email fell to a third of its original level after Darktrace / EMAIL was set up.
Further savings with Darktrace / NETWORK and Darktrace / IDENTITY
Since its success with Darktrace / EMAIL, the company adopted two more products from the Darktrace ActiveAI Security Platform – Darktrace / NETWORK and Darktrace / IDENTITY.
These have further contributed to cost savings. An initial plan to build a 24/7 SOC would have required hiring and retaining six additional analysts, rather than the two that currently use Darktrace, costing an additional £220,000 per year in salary. With Darktrace, the existing analysts have the tools needed to become more effective and impactful.
An additional benefit: Darktrace adoption has lowered the company’s cyber insurance premiums. The security team can reallocate this budget to proactive projects.
Detection of novel threats provides reassurance
Darktrace’s unique approach to cybersecurity added a key benefit. The team’s previous tool took a rules-based approach – which was only good if the next attack featured the same characteristics as the ones on which the tool was trained.
“Darktrace looks for anomalous behavior, and we needed something that detected and responded based on use cases, not rules that might be out of date or too prescriptive,” their Information Security Manager. “Our existing provider could take a couple of days to update rules and signatures, and in this game, speed is of the essence. Darktrace just does everything we need - without delay.”
Where rules-based tools must wait for a threat to emerge before beginning to detect and respond to it, Darktrace identifies and protects against unknown and novel threats, speeding identification, response, and recovery, minimizing business disruption as a result.
Looking to the future
With Darktrace in place, the UK-based civil engineering company team has reallocated time and resources usually spent on detection and alerting to now tackle more sophisticated, strategic challenges. Darktrace has also equipped the team with far better and more regularly updated visibility into potential vulnerabilities.
“One thing that frustrates me a little is penetration testing; our ISO accreditation mandates a penetration test at least once a year, but the results could be out of date the next day,” their Information Security Manager. “Darktrace / Proactive Exposure Management will give me that view in real time – we can run it daily if needed - and that’s going to be a really effective workbench for my team.”
As the company looks to further develop its security posture, Darktrace remains poised to evolve alongside its partner.
Inside Akira’s SonicWall Campaign: Darktrace’s Detection and Response
Introduction: Background on Akira SonicWall campaign
Between July and August 2025, security teams worldwide observed a surge in Akira ransomware incidents involving SonicWall SSL VPN devices [1]. Initially believed to be the result of an unknown zero-day vulnerability, SonicWall later released an advisory announcing that the activity was strongly linked to a previously disclosed vulnerability, CVE-2024-40766, first identified over a year earlier [2].
On August 20, 2025, Darktrace observed unusual activity on the network of a customer in the US. Darktrace detected a range of suspicious activity, including network scanning and reconnaissance, lateral movement, privilege escalation, and data exfiltration. One of the compromised devices was later identified as a SonicWall virtual private network (VPN) server, suggesting that the incident was part of the broader Akira ransomware campaign targeting SonicWall technology.
As the customer was subscribed to the Managed Detection and Response (MDR) service, Darktrace’s Security Operations Centre (SOC) team was able to rapidly triage critical alerts, restrict the activity of affected devices, and notify the customer of the threat. As a result, the impact of the attack was limited - approximately 2 GiB of data had been observed leaving the network, but any further escalation of malicious activity was stopped.
Threat Overview
CVE-2024-40766 and other misconfigurations
CVE-2024-40766 is an improper access control vulnerability in SonicWall’s SonicOS, affecting Gen 5, Gen 6, and Gen 7 devices running SonicOS version 7.0.1 5035 and earlier [3]. The vulnerability was disclosed on August 23, 2024, with a patch released the same day. Shortly after, it was reported to be exploited in the wild by Akira ransomware affiliates and others [4].
Almost a year later, the same vulnerability is being actively targeted again by the Akira ransomware group. In addition to exploiting unpatched devices affected by CVE-2024-40766, security researchers have identified three other risks potentially being leveraged by the group [5]:
*The Virtual Office Portal can be used to initially set up MFA/TOTP configurations for SSLVPN users.
Thus, even if SonicWall devices were patched, threat actors could still target them for initial access by reusing previously stolen credentials and exploiting other misconfigurations.
Akira Ransomware
Akira ransomware was first observed in the wild in March 2023 and has since become one of the most prolific ransomware strains across the threat landscape [6]. The group operates under a Ransomware-as-a-Service (RaaS) model and frequently uses double extortion tactics, pressuring victims to pay not only to decrypt files but also to prevent the public release of sensitive exfiltrated data.
The ransomware initially targeted Windows systems, but a Linux variant was later observed targeting VMware ESXi virtual machines [7]. In 2024, it was assessed that Akira would continue to target ESXi hypervisors, making attacks highly disruptive due to the central role of virtualisation in large-scale cloud deployments. Encrypting the ESXi file system enables rapid and widespread encryption with minimal lateral movement or credential theft. The lack of comprehensive security protections on many ESXi hypervisors also makes them an attractive target for ransomware operators [8].
Victimology
Akira is known to target organizations across multiple sectors, most notably those in manufacturing, education, and healthcare. These targets span multiple geographic regions, including North America, Latin America, Europe and Asia-Pacific [9].
Figure 1: Geographical distribution of organization’s affected by Akira ransomware in 2025 [9].
Common Tactics, Techniques and Procedures (TTPs) [7][10]
Initial Access Targets remote access services such as RDP and VPN through vulnerability exploitation or stolen credentials.
Reconnaissance Uses network scanning tools like SoftPerfect and Advanced IP Scanner to map the environment and identify targets.
Lateral Movement Moves laterally using legitimate administrative tools, typically via RDP.
Persistence Employs techniques such as Kerberoasting and pass-the-hash, and tools like Mimikatz to extract credentials. Known to create new domain accounts to maintain access.
Command and Control Utilizes remote access tools including AnyDesk, RustDesk, Ngrok, and Cloudflare Tunnel.
Exfiltration Uses tools such as FileZilla, WinRAR, WinSCP, and Rclone. Data is exfiltrated via protocols like FTP and SFTP, or through cloud storage services such as Mega.
Darktrace’s Coverage of Akira ransomware
Reconnaissance
Darktrace first detected of unusual network activity around 05:10 UTC, when a desktop device was observed performing a network scan and making an unusual number of DCE-RPC requests to the endpoint mapper (epmapper) service. Network scans are typically used to identify open ports, while querying the epmapper service can reveal exposed RPC services on the network.
Multiple other devices were also later seen with similar reconnaissance activity, and use of the Advanced IP Scanner tool, indicated by connections to the domain advanced-ip-scanner[.]com.
Lateral movement
Shortly after the initial reconnaissance, the same desktop device exhibited unusual use of administrative tools. Darktrace observed the user agent “Ruby WinRM Client” and the URI “/wsman” as the device initiated a rare outbound Windows Remote Management (WinRM) connection to two domain controllers (REDACTED-dc1 and REDACTED-dc2). WinRM is a Microsoft service that uses the WS-Management (WSMan) protocol to enable remote management and control of network devices.
Darktrace also observed the desktop device connecting to an ESXi device (REDACTED-esxi1) via RDP using an LDAP service credential, likely with administrative privileges.
Credential access
At around 06:26 UTC, the desktop device was seen fetching an Active Directory certificate from the domain controller (REDACTED-dc1) by making a DCE-RPC request to the ICertPassage service. Shortly after, the device made a Kerberos login using the administrative credential.
Figure 3: Darktrace’s detection of the of anomalous certificate download and subsequent Kerberos login.
Further investigation into the device’s event logs revealed a chain of connections that Darktrace’s researchers believe demonstrates a credential access technique known as “UnPAC the hash.”
This method begins with pre-authentication using Kerberos’ Public Key Cryptography for Initial Authentication (PKINIT), allowing the client to use an X.509 certificate to obtain a Ticket Granting Ticket (TGT) from the Key Distribution Center (KDC) instead of a password.
The next stage involves User-to-User (U2U) authentication when requesting a Service Ticket (ST) from the KDC. Within Darktrace's visibility of this traffic, U2U was indicated by the client and service principal names within the ST request being identical. Because PKINIT was used earlier, the returned ST contains the NTLM hash of the credential, which can then be extracted and abused for lateral movement or privilege escalation [11].
Figure 4: Flowchart of Kerberos PKINIT pre-authentication and U2U authentication [12]
Figure 5: Device event log showing the Kerberos Login and Kerberos Ticket events
Analysis of the desktop device’s event logs revealed a repeated sequence of suspicious activity across multiple credentials. Each sequence included a DCE-RPC ICertPassage request to download a certificate, followed by a Kerberos login event indicating PKINIT pre-authentication, and then a Kerberos ticket event consistent with User-to-User (U2U) authentication.
Darktrace identified this pattern as highly unusual. Cyber AI Analyst determined that the device used at least 15 different credentials for Kerberos logins over the course of the attack.
By compromising multiple credentials, the threat actor likely aimed to escalate privileges and facilitate further malicious activity, including lateral movement. One of the credentials obtained via the “UnPAC the hash” technique was later observed being used in an RDP session to the domain controller (REDACTED-dc2).
C2 / Additional tooling
At 06:44 UTC, the domain controller (REDACTED-dc2) was observed initiating a connection to temp[.]sh, a temporary cloud hosting service. Open-source intelligence (OSINT) reporting indicates that this service is commonly used by threat actors to host and distribute malicious payloads, including ransomware [13].
Shortly afterward, the ESXi device was observed downloading an executable named “vmwaretools” from the rare external endpoint 137.184.243[.]69, using the user agent “Wget.” The repeated outbound connections to this IP suggest potential command-and-control (C2) activity.
Figure 6: Cyber AI Analyst investigation into the suspicious file download and suspected C2 activity between the ESXI device and the external endpoint 137.184.243[.]69.
Figure 7: Packet capture (PCAP) of connections between the ESXi device and 137.184.243[.]69.
Data exfiltration
The first signs of data exfiltration were observed at around 7:00 UTC. Both the domain controller (REDACTED-dc2) and a likely SonicWall VPN device were seen uploading approximately 2 GB of data via SSH to the rare external endpoint 66.165.243[.]39 (AS29802 HVC-AS). OSINT sources have since identified this IP as an indicator of compromise (IoC) associated with the Akira ransomware group, known to use it for data exfiltration [14].
Figure 8: Cyber AI Analyst incident view highlighting multiple unusual events across several devices on August 20. Notably, it includes the “Unusual External Data Transfer” event, which corresponds to the anomalous 2 GB data upload to the known Akira-associated endpoint 66.165.243[.]39.
Cyber AI Analyst
Throughout the course of the attack, Darktrace’s Cyber AI Analyst autonomously investigated the anomalous activity as it unfolded and correlated related events into a single, cohesive incident. Rather than treating each alert as isolated, Cyber AI Analyst linked them together to reveal the broader narrative of compromise. This holistic view enabled the customer to understand the full scope of the attack, including all associated activities and affected assets that might otherwise have been dismissed as unrelated.
Figure 9: Overview of Cyber AI Analyst’s investigation, correlating all related internal and external security events across affected devices into a single pane of glass.
Containing the attack
In response to the multiple anomalous activities observed across the network, Darktrace's Autonomous Response initiated targeted mitigation actions to contain the attack. These included:
Blocking connections to known malicious or rare external endpoints, such as 137.184.243[.]69, 66.165.243[.]39, and advanced-ip-scanner[.]com.
Blocking internal traffic to sensitive ports, including 88 (Kerberos), 3389 (RDP), and 49339 (DCE-RPC), to disrupt lateral movement and credential abuse.
Enforcing a block on all outgoing connections from affected devices to contain potential data exfiltration and C2 activity.
Figure 10: Autonomous Response actions taken by Darktrace on an affected device, including the blocking of malicious external endpoints and internal service ports.
Managed Detection and Response
As this customer was an MDR subscriber, multiple Enhanced Monitoring alerts—high-fidelity models designed to detect activity indicative of compromise—were triggered across the network. These alerts prompted immediate investigation by Darktrace’s SOC team.
Upon determining that the activity was likely linked to an Akira ransomware attack, Darktrace analysts swiftly acted to contain the threat. At around 08:05 UTC, devices suspected of being compromised were quarantined, and the customer was promptly notified, enabling them to begin their own remediation procedures without delay.
A wider campaign?
Darktrace’s SOC and Threat Research teams identified at least three additional incidents likely linked to the same campaign. All targeted organizations were based in the US, spanning various industries, and each have indications of using SonicWall VPN, indicating it had likely been targeted for initial access.
Across these incidents, similar patterns emerged. In each case, a suspicious executable named “vmwaretools” was downloaded from the endpoint 85.239.52[.]96 using the user agent “Wget”, bearing some resemblance to the file downloads seen in the incident described here. Data exfiltration was also observed via SSH to the endpoints 107.155.69[.]42 and 107.155.93[.]154, both of which belong to the same ASN also seen in the incident described in this blog: S29802 HVC-AS. Notably, 107.155.93[.]154 has been reported in OSINT as an indicator associated with Akira ransomware activity [15]. Further recent Akira ransomware cases have been observed involving SonicWall VPN, where no similar executable file downloads were observed, but SSH exfiltration to the same ASN was. These overlapping and non-overlapping TTPs may reflect the blurring lines between different affiliates operating under the same RaaS.
Lessons from the campaign
This campaign by Akira ransomware actors underscores the critical importance of maintaining up-to-date patching practices. Threat actors continue to exploit previously disclosed vulnerabilities, not just zero-days, highlighting the need for ongoing vigilance even after patches are released. It also demonstrates how misconfigurations and overlooked weaknesses can be leveraged for initial access or privilege escalation, even in otherwise well-maintained environments.
Darktrace’s observations further reveal that ransomware actors are increasingly relying on legitimate administrative tools, such as WinRM, to blend in with normal network activity and evade detection. In addition to previously documented Kerberos-based credential access techniques like Kerberoasting and pass-the-hash, this campaign featured the use of UnPAC the hash to extract NTLM hashes via PKINIT and U2U authentication for lateral movement or privilege escalation.
Credit to Emily Megan Lim (Senior Cyber Analyst), Vivek Rajan (Senior Cyber Analyst), Ryan Traill (Analyst Content Lead), and Sam Lister (Specialist Security Researcher)
Appendices
Darktrace Model Detections
Anomalous Connection / Active Remote Desktop Tunnel
Anomalous Connection / Data Sent to Rare Domain
Anomalous Connection / New User Agent to IP Without Hostname
Anomalous Connection / Possible Data Staging and External Upload
Anomalous Connection / Rare WinRM Incoming
Anomalous Connection / Rare WinRM Outgoing
Anomalous Connection / Uncommon 1 GiB Outbound
Anomalous Connection / Unusual Admin RDP Session
Anomalous Connection / Unusual Incoming Long Remote Desktop Session
Anomalous Connection / Unusual Incoming Long SSH Session
Anomalous Connection / Unusual Long SSH Session
Anomalous File / EXE from Rare External Location
Anomalous Server Activity / Anomalous External Activity from Critical Network Device
Anomalous Server Activity / Outgoing from Server
Anomalous Server Activity / Rare External from Server
Compliance / Default Credential Usage
Compliance / High Priority Compliance Model Alert
Compliance / Outgoing NTLM Request from DC
Compliance / SSH to Rare External Destination
Compromise / Large Number of Suspicious Successful Connections
Compromise / Sustained TCP Beaconing Activity To Rare Endpoint
Device / Anomalous Certificate Download Activity
Device / Anomalous SSH Followed By Multiple Model Alerts
Device / Anonymous NTLM Logins
Device / Attack and Recon Tools
Device / ICMP Address Scan
Device / Large Number of Model Alerts
Device / Network Range Scan
Device / Network Scan
Device / New User Agent To Internal Server
Device / Possible SMB/NTLM Brute Force
Device / Possible SMB/NTLM Reconnaissance
Device / RDP Scan
Device / Reverse DNS Sweep
Device / Suspicious SMB Scanning Activity
Device / UDP Enumeration
Unusual Activity / Unusual External Data to New Endpoint
Unusual Activity / Unusual External Data Transfer
User / Multiple Uncommon New Credentials on Device
User / New Admin Credentials on Client
User / New Admin Credentials on Server
Enhanced Monitoring Models
Compromise / Anomalous Certificate Download and Kerberos Login
Device / Initial Attack Chain Activity
Device / Large Number of Model Alerts from Critical Network Device
Device / Multiple Lateral Movement Model Alerts
Device / Suspicious Network Scan Activity
Unusual Activity / Enhanced Unusual External Data Transfer
Antigena/Autonomous Response Models
Antigena / Network / External Threat / Antigena File then New Outbound Block
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![Geographical distribution of organization’s affected by Akira ransomware in 2025 [9].](https://cdn.prod.website-files.com/626ff4d25aca2edf4325ff97/68e684a90c107be86fa5c2e0_Screenshot%202025-10-08%20at%207.53.20%E2%80%AFAM.png)
![Flowchart of Kerberos PKINIT pre-authentication and U2U authentication [12].](https://cdn.prod.website-files.com/626ff4d25aca2edf4325ff97/68e53a2b5e9394d5a5cbebb7_Screenshot%202025-10-07%20at%209.04.43%E2%80%AFAM.png)
![Cyber AI Analyst investigation into the suspicious file download and suspected C2 activity between the ESXI device and the external endpoint 137.184.243[.]69.](https://cdn.prod.website-files.com/626ff4d25aca2edf4325ff97/68e53a8ea01e348d5a62a6c3_Screenshot%202025-10-07%20at%209.06.31%E2%80%AFAM.png)
![Packet capture (PCAP) of connections between the ESXi device and 137.184.243[.]69.](https://cdn.prod.website-files.com/626ff4d25aca2edf4325ff97/68e53ae5304f84a4192967db_Screenshot%202025-10-07%20at%209.07.53%E2%80%AFAM.png)
![Cyber AI Analyst incident view highlighting multiple unusual events across several devices on August 20. Notably, it includes the “Unusual External Data Transfer” event, which corresponds to the anomalous 2 GB data upload to the known Akira-associated endpoint 66.165.243[.]39.](https://cdn.prod.website-files.com/626ff4d25aca2edf4325ff97/68e53b26d90343a0a6ab3bb2_Screenshot%202025-10-07%20at%209.09.02%E2%80%AFAM.png)
