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September 4, 2022

Steps of a BumbleBee Intrusion to a Cobalt Strike

Discover the steps of a Bumblebee intrusion, from initial detection to Cobalt Strike deployment. Learn how Darktrace defends against evolving threats with AI.
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
Sam Lister
SOC Analyst
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04
Sep 2022

Introduction

Throughout April 2022, Darktrace observed several cases in which threat actors used the loader known as ‘BumbleBee’ to install Cobalt Strike Beacon onto victim systems. The threat actors then leveraged Cobalt Strike Beacon to conduct network reconnaissance, obtain account password data, and write malicious payloads across the network. In this article, we will provide details of the actions threat actors took during their intrusions, as well as details of the network-based behaviours which served as evidence of the actors’ activities.  

BumbleBee 

In March 2022, Google’s Threat Analysis Group (TAG) provided details of the activities of an Initial Access Broker (IAB) group dubbed ‘Exotic Lily’ [1]. Before March 2022, Google’s TAG observed Exotic Lily leveraging sophisticated impersonation techniques to trick employees of targeted organisations into downloading ISO disc image files from legitimate file storage services such as WeTransfer. These ISO files contained a Windows shortcut LNK file and a BazarLoader Dynamic Link Library (i.e, DLL). BazarLoader is a member of the Bazar family — a family of malware (including both BazarLoader and BazarBackdoor) with strong ties to the Trickbot malware, the Anchor malware family, and Conti ransomware. BazarLoader, which is typically distributed via email campaigns or via fraudulent call campaigns, has been known to drop Cobalt Strike as a precursor to Conti ransomware deployment [2]. 

In March 2022, Google’s TAG observed Exotic Lily leveraging file storage services to distribute an ISO file containing a DLL which, when executed, caused the victim machine to make HTTP requests with the user-agent string ‘bumblebee’. Google’s TAG consequently called this DLL payload ‘BumbleBee’. Since Google’s discovery of BumbleBee back in March, several threat research teams have reported BumbleBee samples dropping Cobalt Strike [1]/[3]/[4]/[5]. It has also been reported by Proofpoint [3] that other threat actors such as TA578 and TA579 transitioned to BumbleBee in March 2022.  

Interestingly, BazarLoader’s replacement with BumbleBee seems to coincide with the leaking of the Conti ransomware gang’s Jabber chat logs at the end of February 2022. On February 25th, 2022, the Conti gang published a blog post announcing their full support for the Russian state’s invasion of Ukraine [6]. 

Figure 1: The Conti gang's public declaration of their support for Russia's invasion of Ukraine

Within days of sharing their support for Russia, logs from a server hosting the group’s Jabber communications began to be leaked on Twitter by @ContiLeaks [7]. The leaked logs included records of conversations among nearly 500 threat actors between Jan 2020 and March 2022 [8]. The Jabber logs were supposedly stolen and leaked by a Ukrainian security researcher [3]/[6].

Affiliates of the Conti ransomware group were known to use BazarLoader to deliver Conti ransomware [9]. BumbleBee has now also been linked to the Conti ransomware group by several threat research teams [1]/[10]/[11]. The fact that threat actors’ transition from BazarLoader to BumbleBee coincides with the leak of Conti’s Jabber chat logs may indicate that the transition occurred as a result of the leaks [3]. Since the transition, BumbleBee has become a significant tool in the cyber-crime ecosystem, with links to several ransomware operations such as Conti, Quantum, and Mountlocker [11]. The rising use of BumbleBee by threat actors, and particularly ransomware actors, makes the early detection of BumbleBee key to identifying the preparatory stages of ransomware attacks.  

Intrusion Kill Chain 

In April 2022, Darktrace observed the following pattern of threat actor activity within the networks of several Darktrace clients: 

1.     Threat actor socially engineers user via email into running a BumbleBee payload on their device

2.     BumbleBee establishes HTTPS communication with a BumbleBee C2 server

3.     Threat actor instructs BumbleBee to download and execute Cobalt Strike Beacon

4.     Cobalt Strike Beacon establishes HTTPS communication with a Cobalt Strike C2 server

5.     Threat actor instructs Cobalt Strike Beacon to scan for open ports and to enumerate network shares

6.     Threat actor instructs Cobalt Strike Beacon to use the DCSync technique to obtain password account data from an internal domain controller

7.     Threat actor instructs Cobalt Strike Beacon to distribute malicious payloads to other internal systems 

With limited visibility over affected clients’ email environments, Darktrace was unable to determine how the threat actors interacted with users to initiate the BumbleBee infection. However, based on open-source reporting on BumbleBee [3]/[4]/[10]/[11]/[12]/[13]/[14]/[15]/[16]/[17], it is likely that the actors tricked target users into running BumbleBee by sending them emails containing either a malicious zipped ISO file or a link to a file storage service hosting the malicious zipped ISO file. These ISO files typically contain a LNK file and a BumbleBee DLL payload. The properties of these LNK files are set in such a way that opening them causes the corresponding DLL payload to run. 

In several cases observed by Darktrace, devices contacted a file storage service such as Microsoft OneDrive or Google Cloud Storage immediately before they displayed signs of BumbleBee infection. In these cases, it is likely that BumbleBee was executed on the users’ devices as a result of the users interacting with an ISO file which they were tricked into downloading from a file storage service. 

Figure 2: The above figure, taken from the event log for an infected device, shows that the device contacted a OneDrive endpoint immediately before making HTTPS connections to the BumbleBee C2 server, 45.140.146[.]244
Figure 3: The above figure, taken from the event log for an infected device, shows that the device contacted a Google Cloud Storage endpoint and then the malicious endpoint ‘marebust[.]com’ before making HTTPS connections to the  BumbleBee C2 servers, 108.62.118[.]61 and 23.227.198[.]217

After users ran a BumbleBee payload, their devices immediately initiated communications with BumbleBee C2 servers. The BumbleBee samples used HTTPS for their C2 communication, and all presented a common JA3 client fingerprint, ‘0c9457ab6f0d6a14fc8a3d1d149547fb’. All analysed samples excluded domain names in their ‘client hello’ messages to the C2 servers, which is unusual for legitimate HTTPS communication. External SSL connections which do not specify a destination domain name and whose JA3 client fingerprint is ‘0c9457ab6f0d6a14fc8a3d1d149547fb’ are potential indicators of BumbleBee infection. 

Figure 4:The above figure, taken from Darktrace's Advanced Search interface, depicts an infected device's spike in HTTPS connections with the JA3 client fingerprint ‘0c9457ab6f0d6a14fc8a3d1d149547fb’

Once the threat actors had established HTTPS communication with the BumbleBee-infected systems, they instructed BumbleBee to download and execute Cobalt Strike Beacon. This behaviour resulted in the infected systems making HTTPS connections to Cobalt Strike C2 servers. The Cobalt Strike Beacon samples all had the same JA3 client fingerprint ‘a0e9f5d64349fb13191bc781f81f42e1’ — a fingerprint associated with previously seen Cobalt Strike samples [18]. The domain names ‘fuvataren[.]com’ and ‘cuhirito[.]com’ were observed in the samples’ HTTPS communications. 

Figure 5:The above figure, taken from Darktrace's Advanced Search interface, depicts the Cobalt Strike C2 communications which immediately followed a device's BumbleBee C2 activity

Cobalt Strike Beacon payloads call home to C2 servers for instructions. In the cases observed, threat actors first instructed the Beacon payloads to perform reconnaissance tasks, such as SMB port scanning and SMB enumeration. It is likely that the threat actors performed these steps to inform the next stages of their operations.  The SMB enumeration activity was evidenced by the infected devices making NetrShareEnum and NetrShareGetInfo requests to the srvsvc RPC interface on internal systems.

Figure 6: The above figure, taken from Darktrace’s Advanced Search interface, depicts a spike in srvsvc requests coinciding with the infected device's Cobalt Strike C2 activity

After providing Cobalt Strike Beacon with reconnaissance tasks, the threat actors set out to obtain account password data in preparation for the lateral movement phase of their operation. To obtain account password data, the actors instructed Cobalt Strike Beacon to use the DCSync technique to replicate account password data from an internal domain controller. This activity was evidenced by the infected devices making DRSGetNCChanges requests to the drsuapi RPC interface on internal domain controllers. 

Figure 7: The above figure, taken from Darktrace’s Advanced Search interface, depicts a spike in DRSGetNCChanges requests coinciding with the infected device’s Cobalt Strike C2 activity

After leveraging the DCSync technique, the threat actors sought to broaden their presence within the targeted networks.  To achieve this, they instructed Cobalt Strike Beacon to get several specially selected internal systems to run a suspiciously named DLL (‘f.dll’). Cobalt Strike first established SMB sessions with target systems using compromised account credentials. During these sessions, Cobalt Strike uploaded the malicious DLL to a hidden network share. To execute the DLL, Cobalt Strike abused the Windows Service Control Manager (SCM) to remotely control and manipulate running services on the targeted internal hosts. Cobalt Strike first opened a binding handle to the svcctl interface on the targeted destination systems. It then went on to make an OpenSCManagerW request, a CreateServiceA request, and a StartServiceA request to the svcctl interface on the targeted hosts: 

·      Bind request – opens a binding handle to the relevant RPC interface (in this case, the svcctl interface) on the destination device

·      OpenSCManagerW request – establishes a connection to the Service Control Manager (SCM) on the destination device and opens a specified SCM database

·      CreateServiceA request – creates a service object and adds it to the specified SCM database 

·      StartServiceA request – starts a specified service

Figure 8: The above figure, taken from Darktrace’s Advanced Search interface, outlines an infected system’s lateral movement activities. After writing a file named ‘f.dll’ to the C$ share on an internal server, the infected device made several RPC requests to the svcctl interface on the targeted server

It is likely that the DLL file which the threat actors distributed was a Cobalt Strike payload. In one case, however, the threat actor was also seen distributing and executing a payload named ‘procdump64.exe’. This may suggest that the threat actor was seeking to use ProcDump to obtain authentication material stored in the process memory of the Local Security Authority Subsystem Service (LSASS). Given that ProcDump is a legitimate Windows Sysinternals tool primarily used for diagnostics and troubleshooting, it is likely that threat actors leveraged it in order to evade detection. 

In all the cases which Darktrace observed, threat actors’ attempts to conduct follow-up activities after moving laterally were thwarted with the help of Darktrace’s SOC team. It is likely that the threat actors responsible for the reported activities were seeking to deploy ransomware within the targeted networks. The steps which the threat actors took to make progress towards achieving this objective resulted in highly unusual patterns of network traffic. Darktrace’s detection of these unusual network activities allowed security teams to prevent these threat actors from achieving their disruptive objectives. 

Darktrace Coverage

Once threat actors succeeded in tricking users into running BumbleBee on their devices, Darktrace’s Self-Learning AI immediately detected the command-and-control (C2) activity generated by the loader. BumbleBee’s C2 activity caused the following Darktrace models to breach:

·      Anomalous Connection / Anomalous SSL without SNI to New External

·      Anomalous Connection / Suspicious Self-Signed SSL

·      Anomalous Connection / Rare External SSL Self-Signed

·      Compromise / Suspicious TLS Beaconing To Rare External

·      Compromise / Beacon to Young Endpoint

·      Compromise / Beaconing Activity To External Rare

·      Compromise / Sustained SSL or HTTP Increase

·      Compromise / Suspicious TLS Beaconing To Rare External

·      Compromise / SSL Beaconing to Rare Destination

·      Compromise / Large Number of Suspicious Successful Connections

·      Device / Multiple C2 Model Breaches 

BumbleBee’s delivery of Cobalt Strike Beacon onto target systems resulted in those systems communicating with Cobalt Strike C2 servers. Cobalt Strike Beacon’s C2 communications resulted in breaches of the following models: 

·      Compromise / Beaconing Activity To External Rare

·      Compromise / High Volume of Connections with Beacon Score

·      Compromise / Large Number of Suspicious Successful Connections

·      Compromise / Sustained SSL or HTTP Increase

·      Compromise / SSL or HTTP Beacon

·      Compromise / Slow Beaconing Activity To External Rare

·      Compromise / SSL Beaconing to Rare Destination 

The threat actors’ subsequent port scanning and SMB enumeration activities caused the following models to breach:

·      Device / Network Scan

·      Anomalous Connection / SMB Enumeration

·      Device / Possible SMB/NTLM Reconnaissance

·      Device / Suspicious Network Scan Activity  

The threat actors’ attempts to obtain account password data from domain controllers using the DCSync technique resulted in breaches of the following models: 

·      Compromise / Unusual SMB Session and DRS

·      Anomalous Connection / Anomalous DRSGetNCChanges Operation

Finally, the threat actors’ attempts to internally distribute and execute payloads resulted in breaches of the following models:

·      Compliance / SMB Drive Write

·      Device / Lateral Movement and C2 Activity

·      Device / SMB Lateral Movement

·      Device / Multiple Lateral Movement Model Breaches

·      Anomalous File / Internal / Unusual SMB Script Write

·      Anomalous File / Internal / Unusual Internal EXE File Transfer

·      Anomalous Connection / High Volume of New or Uncommon Service Control

If Darktrace/Network had been configured in the targeted environments, then it would have blocked BumbleBee’s C2 communications, which would have likely prevented the threat actors from delivering Cobalt Strike Beacon into the target networks. 

Figure 9: Attack timeline

Conclusion

Threat actors use loaders to smuggle more harmful payloads into target networks. Prior to March 2022, it was common to see threat actors using the BazarLoader loader to transfer their payloads into target environments. However, since the public disclosure of the Conti gang’s Jabber chat logs at the end of February, the cybersecurity world has witnessed a shift in tradecraft. Threat actors have seemingly transitioned from using BazarLoader to using a novel loader known as ‘BumbleBee’. Since BumbleBee first made an appearance in March 2022, a growing number of threat actors, in particular ransomware actors, have been observed using it.

It is likely that this trend will continue, which makes the detection of BumbleBee activity vital for the prevention of ransomware deployment within organisations’ networks. During April, Darktrace’s SOC team observed a particular pattern of threat actor activity involving the BumbleBee loader. After tricking users into running BumbleBee on their devices, threat actors were seen instructing BumbleBee to drop Cobalt Strike Beacon. Threat actors then leveraged Cobalt Strike Beacon to conduct network reconnaissance, obtain account password data from internal domain controllers, and distribute malicious payloads internally.  Darktrace’s detection of these activities prevented the threat actors from achieving their likely harmful objectives.  

Thanks to Ross Ellis for his contributions to this blog.

Appendices 

References 

[1] https://blog.google/threat-analysis-group/exposing-initial-access-broker-ties-conti/ 

[2] https://securityintelligence.com/posts/trickbot-gang-doubles-down-enterprise-infection/ 

[3] https://www.proofpoint.com/us/blog/threat-insight/bumblebee-is-still-transforming

[4] https://www.cynet.com/orion-threat-alert-flight-of-the-bumblebee/ 

[5] https://research.nccgroup.com/2022/04/29/adventures-in-the-land-of-bumblebee-a-new-malicious-loader/ 

[6] https://www.bleepingcomputer.com/news/security/conti-ransomwares-internal-chats-leaked-after-siding-with-russia/ 

[7] https://therecord.media/conti-leaks-the-panama-papers-of-ransomware/ 

[8] https://www.secureworks.com/blog/gold-ulrick-leaks-reveal-organizational-structure-and-relationships 

[9] https://www.prodaft.com/m/reports/Conti_TLPWHITE_v1.6_WVcSEtc.pdf 

[10] https://www.kroll.com/en/insights/publications/cyber/bumblebee-loader-linked-conti-used-in-quantum-locker-attacks 

[11] https://symantec-enterprise-blogs.security.com/blogs/threat-intelligence/bumblebee-loader-cybercrime 

[12] https://isc.sans.edu/diary/TA578+using+thread-hijacked+emails+to+push+ISO+files+for+Bumblebee+malware/28636 

[13] https://isc.sans.edu/diary/rss/28664 

[14] https://www.logpoint.com/wp-content/uploads/2022/05/buzz-of-the-bumblebee-a-new-malicious-loader-threat-report-no-3.pdf 

[15] https://ghoulsec.medium.com/mal-series-23-malware-loader-bumblebee-6ab3cf69d601 

[16]  https://blog.cyble.com/2022/06/07/bumblebee-loader-on-the-rise/  

[17]  https://asec.ahnlab.com/en/35460/ 

[18] https://thedfirreport.com/2021/07/19/icedid-and-cobalt-strike-vs-antivirus/

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
Sam Lister
SOC Analyst

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September 4, 2025

Rethinking Signature-Based Detection for Power Utility Cybersecurity

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Lessons learned from OT cyber attacks

Over the past decade, some of the most disruptive attacks on power utilities have shown the limits of signature-based detection and reshaped how defenders think about OT security. Each incident reinforced that signatures are too narrow and reactive to serve as the foundation of defense.

2015: BlackEnergy 3 in Ukraine

According to CISA, on December 23, 2015, Ukrainian power companies experienced unscheduled power outages affecting a large number of customers — public reports indicate that the BlackEnergy malware was discovered on the companies’ computer networks.

2016: Industroyer/CrashOverride

CISA describes CrashOverride malwareas an “extensible platform” reported to have been used against critical infrastructure in Ukraine in 2016. It was capable of targeting industrial control systems using protocols such as IEC‑101, IEC‑104, and IEC‑61850, and fundamentally abused legitimate control system functionality to deliver destructive effects. CISA emphasizes that “traditional methods of detection may not be sufficient to detect infections prior to the malware execution” and recommends behavioral analysis techniques to identify precursor activity to CrashOverride.

2017: TRITON Malware

The U.S. Department of the Treasury reports that the Triton malware, also known as TRISIS or HatMan, was “designed specifically to target and manipulate industrial safety systems” in a petrochemical facility in the Middle East. The malware was engineered to control Safety Instrumented System (SIS) controllers responsible for emergency shutdown procedures. During the attack, several SIS controllers entered a failed‑safe state, which prevented the malware from fully executing.

The broader lessons

These events revealed three enduring truths:

  • Signatures have diminishing returns: BlackEnergy showed that while signatures can eventually identify adapted IT malware, they arrive too late to prevent OT disruption.
  • Behavioral monitoring is essential: CrashOverride demonstrated that adversaries abuse legitimate industrial protocols, making behavioral and anomaly detection more effective than traditional signature methods.
  • Critical safety systems are now targets: TRITON revealed that attackers are willing to compromise safety instrumented systems, elevating risks from operational disruption to potential physical harm.

The natural progression for utilities is clear. Static, file-based defenses are too fragile for the realities of OT.  

These incidents showed that behavioral analytics and anomaly detection are far more effective at identifying suspicious activity across industrial systems, regardless of whether the malicious code has ever been seen before.

Strategic risks of overreliance on signatures

  • False sense of security: Believing signatures will block advanced threats can delay investment in more effective detection methods.
  • Resource drain: Constantly updating, tuning, and maintaining signature libraries consumes valuable staff resources without proportional benefit.
  • Adversary advantage: Nation-state and advanced actors understand the reactive nature of signature defenses and design attacks to circumvent them from the start.

Recommended Alternatives (with real-world OT examples)

 Alternative strategies for detecting cyber attacks in OT
Figure 1: Alternative strategies for detecting cyber attacks in OT

Behavioral and anomaly detection

Rather than relying on signatures, focusing on behavior enables detection of threats that have never been seen before—even trusted-looking devices.

Real-world insight:

In one OT setting, a vendor inadvertently left a Raspberry Pi on a customer’s ICS network. After deployment, Darktrace’s system flagged elastic anomalies in its HTTPS and DNS communication despite the absence of any known indicators of compromise. The alerting included sustained SSL increases, agent‑beacon activity, and DNS connections to unusual endpoints, revealing a possible supply‑chain or insider risk invisible to static tools.  

Darktrace’s AI-driven threat detection aligns with the zero-trust principle of assuming the risk of a breach. By leveraging AI that learns an organization’s specific patterns of life, Darktrace provides a tailored security approach ideal for organizations with complex supply chains.

Threat intelligence sharing & building toward zero-trust philosophy

Frameworks such as MITRE ATT&CK for ICS provide a common language to map activity against known adversary tactics, helping teams prioritize detections and response strategies. Similarly, information-sharing communities like E-ISAC and regional ISACs give utilities visibility into the latest tactics, techniques, and procedures (TTPs) observed across the sector. This level of intel can help shift the focus away from chasing individual signatures and toward building resilience against how adversaries actually operate.

Real-world insight:

Darktrace’s AI embodies zero‑trust by assuming breach potential and continually evaluating all device behavior, even those deemed trusted. This approach allowed the detection of an anomalous SharePoint phishing attempt coming from a trusted supplier, intercepted by spotting subtle patterns rather than predefined rules. If a cloud account is compromised, unauthorized access to sensitive information could lead to extortion and lateral movement into mission-critical systems for more damaging attacks on critical-national infrastructure.

This reinforces the need to monitor behavioral deviations across the supply chain, not just known bad artifacts.

Defense-in-Depth with OT context & unified visibility

OT environments demand visibility that spans IT, OT, and IoT layers, supported by risk-based prioritization.

Real-world insight:

Darktrace / OT offers unified AI‑led investigations that break down silos between IT and OT. Smaller teams can see unusual outbound traffic or beaconing from unknown OT devices, swiftly investigate across domains, and get clear visibility into device behavior, even when they lack specialized OT security expertise.  

Moreover, by integrating contextual risk scoring, considering real-world exploitability, device criticality, firewall misconfiguration, and legacy hardware exposure, utilities can focus on the vulnerabilities that genuinely threaten uptime and safety, rather than being overwhelmed by CVE noise.  

Regulatory alignment and positive direction

Industry regulations are beginning to reflect this evolution in strategy. NERC CIP-015 requires internal network monitoring that detects anomalies, and the standard references anomalies 15 times. In contrast, signature-based detection is not mentioned once.

This regulatory direction shows that compliance bodies understand the limitations of static defenses and are encouraging utilities to invest in anomaly-based monitoring and analytics. Utilities that adopt these approaches will not only be strengthening their resilience but also positioning themselves for regulatory compliance and operational success.

Conclusion

Signature-based detection retains utility for common IT malware, but it cannot serve as the backbone of security for power utilities. History has shown that major OT attacks are rarely stopped by signatures, since each campaign targets specific systems with customized tools. The most dangerous adversaries, from insiders to nation-states, actively design their operations to avoid detection by signature-based tools.

A more effective strategy prioritizes behavioral analytics, anomaly detection, and community-driven intelligence sharing. These approaches not only catch known threats, but also uncover the subtle anomalies and novel attack techniques that characterize tomorrow’s incidents.

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About the author
Daniel Simonds
Director of Operational Technology

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August 21, 2025

From VPS to Phishing: How Darktrace Uncovered SaaS Hijacks through Virtual Infrastructure Abuse

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What is a VPS and how are they abused?

A Virtual Private Server (VPS) is a virtualized server that provides dedicated resources and control to users on a shared physical device.  VPS providers, long used by developers and businesses, are increasingly misused by threat actors to launch stealthy, scalable attacks. While not a novel tactic, VPS abuse is has seen an increase in Software-as-a-Service (SaaS)-targeted campaigns as it enables attackers to bypass geolocation-based defenses by mimicking local traffic, evade IP reputation checks with clean, newly provisioned infrastructure, and blend into legitimate behavior [3].

VPS providers like Hyonix and Host Universal offer rapid setup and minimal open-source intelligence (OSINT) footprint, making detection difficult [1][2]. These services are not only fast to deploy but also affordable, making them attractive to attackers seeking anonymous, low-cost infrastructure for scalable campaigns. Such attacks tend to be targeted and persistent, often timed to coincide with legitimate user activity, a tactic that renders traditional security tools largely ineffective.

Darktrace’s investigation into Hyonix VPS abuse

In May 2025, Darktrace’s Threat Research team investigated a series of incidents across its customer base involving VPS-associated infrastructure. The investigation began with a fleet-wide review of alerts linked to Hyonix (ASN AS931), revealing a noticeable spike in anomalous behavior from this ASN in March 2025. The alerts included brute-force attempts, anomalous logins, and phishing campaign-related inbox rule creation.

Darktrace identified suspicious activity across multiple customer environments around this time, but two networks stood out. In one instance, two internal devices exhibited mirrored patterns of compromise, including logins from rare endpoints, manipulation of inbox rules, and the deletion of emails likely used in phishing attacks. Darktrace traced the activity back to IP addresses associated with Hyonix, suggesting a deliberate use of VPS infrastructure to facilitate the attack.

On the second customer network, the attack was marked by coordinated logins from rare IPs linked to multiple VPS providers, including Hyonix. This was followed by the creation of inbox rules with obfuscated names and attempts to modify account recovery settings, indicating a broader campaign that leveraged shared infrastructure and techniques.

Darktrace’s Autonomous Response capability was not enabled in either customer environment during these attacks. As a result, no automated containment actions were triggered, allowing the attack to escalate without interruption. Had Autonomous Response been active, Darktrace would have automatically blocked connections from the unusual VPS endpoints upon detection, effectively halting the compromise in its early stages.

Case 1

Timeline of activity for Case 1 - Unusual VPS logins and deletion of phishing emails.
Figure 1: Timeline of activity for Case 1 - Unusual VPS logins and deletion of phishing emails.

Initial Intrusion

On May 19, 2025, Darktrace observed two internal devices on one customer environment initiating logins from rare external IPs associated with VPS providers, namely Hyonix and Host Universal (via Proton VPN). Darktrace recognized that these logins had occurred within minutes of legitimate user activity from distant geolocations, indicating improbable travel and reinforcing the likelihood of session hijacking. This triggered Darktrace / IDENTITY model “Login From Rare Endpoint While User Is Active”, which highlights potential credential misuse when simultaneous logins occur from both familiar and rare sources.  

Shortly after these logins, Darktrace observed the threat actor deleting emails referring to invoice documents from the user’s “Sent Items” folder, suggesting an attempt to hide phishing emails that had been sent from the now-compromised account. Though not directly observed, initial access in this case was likely achieved through a similar phishing or account hijacking method.

 Darktrace / IDENTITY model "Login From Rare Endpoint While User Is Active", which detects simultaneous logins from both a common and a rare source to highlight potential credential misuse.
Figure 2: Darktrace / IDENTITY model "Login From Rare Endpoint While User Is Active", which detects simultaneous logins from both a common and a rare source to highlight potential credential misuse.

Case 2

Timeline of activity for Case 2 – Coordinated inbox rule creation and outbound phishing campaign.
Figure 3: Timeline of activity for Case 2 – Coordinated inbox rule creation and outbound phishing campaign.

In the second customer environment, Darktrace observed similar login activity originating from Hyonix, as well as other VPS providers like Mevspace and Hivelocity. Multiple users logged in from rare endpoints, with Multi-Factor Authentication (MFA) satisfied via token claims, further indicating session hijacking.

Establishing control and maintaining persistence

Following the initial access, Darktrace observed a series of suspicious SaaS activities, including the creation of new email rules. These rules were given minimal or obfuscated names, a tactic often used by attackers to avoid drawing attention during casual mailbox reviews by the SaaS account owner or automated audits. By keeping rule names vague or generic, attackers reduce the likelihood of detection while quietly redirecting or deleting incoming emails to maintain access and conceal their activity.

One of the newly created inbox rules targeted emails with subject lines referencing a document shared by a VIP at the customer’s organization. These emails would be automatically deleted, suggesting an attempt to conceal malicious mailbox activity from legitimate users.

Mirrored activity across environments

While no direct lateral movement was observed, mirrored activity across multiple user devices suggested a coordinated campaign. Notably, three users had near identical similar inbox rules created, while another user had a different rule related to fake invoices, reinforcing the likelihood of a shared infrastructure and technique set.

Privilege escalation and broader impact

On one account, Darktrace observed “User registered security info” activity was shortly after anomalous logins, indicating attempts to modify account recovery settings. On another, the user reset passwords or updated security information from rare external IPs. In both cases, the attacker’s actions—including creating inbox rules, deleting emails, and maintaining login persistence—suggested an intent to remain undetected while potentially setting the stage for data exfiltration or spam distribution.

On a separate account, outbound spam was observed, featuring generic finance-related subject lines such as 'INV#. EMITTANCE-1'. At the network level, Darktrace / NETWORK detected DNS requests from a device to a suspicious domain, which began prior the observed email compromise. The domain showed signs of domain fluxing, a tactic involving frequent changes in IP resolution, commonly used by threat actors to maintain resilient infrastructure and evade static blocklists. Around the same time, Darktrace detected another device writing a file named 'SplashtopStreamer.exe', associated with the remote access tool Splashtop, to a domain controller. While typically used in IT support scenarios, its presence here may suggest that the attacker leveraged it to establish persistent remote access or facilitate lateral movement within the customer’s network.

Conclusion

This investigation highlights the growing abuse of VPS infrastructure in SaaS compromise campaigns. Threat actors are increasingly leveraging these affordable and anonymous hosting services to hijack accounts, launch phishing attacks, and manipulate mailbox configurations, often bypassing traditional security controls.

Despite the stealthy nature of this campaign, Darktrace detected the malicious activity early in the kill chain through its Self-Learning AI. By continuously learning what is normal for each user and device, Darktrace surfaced subtle anomalies, such as rare login sources, inbox rule manipulation, and concurrent session activity, that likely evade traditional static, rule-based systems.

As attackers continue to exploit trusted infrastructure and mimic legitimate user behavior, organizations should adopt behavioral-based detection and response strategies. Proactively monitoring for indicators such as improbable travel, unusual login sources, and mailbox rule changes, and responding swiftly with autonomous actions, is critical to staying ahead of evolving threats.

Credit to Rajendra Rushanth (Cyber Analyst), Jen Beckett (Cyber Analyst) and Ryan Traill (Analyst Content Lead)

References

·      1: https://cybersecuritynews.com/threat-actors-leveraging-vps-hosting-providers/

·      2: https://threatfox.abuse.ch/asn/931/

·      3: https://www.cyfirma.com/research/vps-exploitation-by-threat-actors/

Appendices

Darktrace Model Detections

•   SaaS / Compromise / Unusual Login, Sent Mail, Deleted Sent

•   SaaS / Compromise / Suspicious Login and Mass Email Deletes

•   SaaS / Resource / Mass Email Deletes from Rare Location

•   SaaS / Compromise / Unusual Login and New Email Rule

•   SaaS / Compliance / Anomalous New Email Rule

•   SaaS / Resource / Possible Email Spam Activity

•   SaaS / Unusual Activity / Multiple Unusual SaaS Activities

•   SaaS / Unusual Activity / Multiple Unusual External Sources For SaaS Credential

•   SaaS / Access / Unusual External Source for SaaS Credential Use

•   SaaS / Compromise / High Priority Login From Rare Endpoint

•   SaaS / Compromise / Login From Rare Endpoint While User Is Active

List of Indicators of Compromise (IoCs)

Format: IoC – Type – Description

•   38.240.42[.]160 – IP – Associated with Hyonix ASN (AS931)

•   103.75.11[.]134 – IP – Associated with Host Universal / Proton VPN

•   162.241.121[.]156 – IP – Rare IP associated with phishing

•   194.49.68[.]244 – IP – Associated with Hyonix ASN

•   193.32.248[.]242 – IP – Used in suspicious login activity / Mullvad VPN

•   50.229.155[.]2 – IP – Rare login IP / AS 7922 ( COMCAST-7922 )

•   104.168.194[.]248 – IP – Rare login IP / AS 54290 ( HOSTWINDS )

•   38.255.57[.]212 – IP – Hyonix IP used during MFA activity

•   103.131.131[.]44 – IP – Hyonix IP used in login and MFA activity

•   178.173.244[.]27 – IP – Hyonix IP

•   91.223.3[.]147 – IP – Mevspace Poland, used in multiple logins

•   2a02:748:4000:18:0:1:170b[:]2524 – IPv6 – Hivelocity VPS, used in multiple logins and MFA activity

•   51.36.233[.]224 – IP – Saudi ASN, used in suspicious login

•   103.211.53[.]84 – IP – Excitel Broadband India, used in security info update

MITRE ATT&CK Mapping

Tactic – Technique – Sub-Technique

•   Initial Access – T1566 – Phishing

                       T1566.001 – Spearphishing Attachment

•   Execution – T1078 – Valid Accounts

•   Persistence – T1098 – Account Manipulation

                       T1098.002 – Exchange Email Rules

•   Command and Control – T1071 – Application Layer Protocol

                       T1071.001 – Web Protocols

•   Defense Evasion – T1036 – Masquerading

•   Defense Evasion – T1562 – Impair Defenses

                       T1562.001 – Disable or Modify Tools

•   Credential Access – T1556 – Modify Authentication Process

                       T1556.004 – MFA Bypass

•   Discovery – T1087 – Account Discovery

•      Impact – T1531 – Account Access Removal

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About the author
Rajendra Rushanth
Cyber Analyst
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