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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
Specialist Security Researcher
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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/

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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
Specialist Security Researcher

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September 25, 2026

A Chain Reaction: Blockchain-Hosted Infostealer Campaign Targets Windows and macOS

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Key Insights

  • Darktrace detected a blockchain-hosted infostealer campaign targeting Windows and macOS devices across multiple customer environments.
  • The campaign combined ClickFix social engineering with trusted services and decentralized blockchain infrastructure to support malware delivery and C2 activity.
  • Compromised devices were observed connecting to rare and unusual external endpoints, including DGA C2 domains, blockchain-related endpoints, and cryptocurrency mining infrastructure.
  • The activity was associated with information-stealing malware strains including Atomic macOS Stealer (AMOS), Lumma, Rhadamanthys, Vidar, and Phexia.
  • Darktrace identified anomalous device behavior, beaconing patterns, rare external connections, cryptomining activity, and suspicious TLS/SSL communications without relying solely on prior knowledge or static indicators of compromise.
  • The campaign highlights how attackers are increasingly using legitimate and decentralized infrastructure to make detection, disruption, and attribution more challenging for defenders.

The Infostealer Ecosystem

The information stealer malware ecosystem continues to grow in value for threat actors across the digital threat landscape. Infostealers are increasingly delivered through Malware-as-a-Service (MaaS) operating models, distributed through affiliate networks, and designed to withstand infrastructure takedowns. This resilience was demonstrated by the recent takedown of Lumma Stealer malicious domains by Microsoft’s Digital Crimes Unit (DCU) [1].

Infostealers are used to gather and exfiltrate sensitive information, including non-human identity (NHI) data, from compromised systems across cloud, Software-as-a-Service (SaaS), Virtual Private Network (VPN), and development environments. They can also support ransomware operations by expanding the credentials and access paths available to threat actors, contributing to the high volume of identity-based attacks observed across the broader threat landscape [2][3].

Darktrace’s Observations of ClickFix and Infostealers

Throughout 2026, Darktrace has observed multiple campaigns using ClickFix social engineering to trick users into carrying out malicious actions and downloading initial payloads, including information stealers. More recently, Darktrace’s Threat Research team identified a specific ClickFix campaign involving a blockchain-hosted infostealer targeting Windows and macOS devices.

Darktrace identified affected customer environments across Europe, the United States, Asia, and the Middle East where blockchain-hosted infostealer malware appears to have been delivered to compromised systems following likely ClickFix-driven initial access. Darktrace investigated the activity and found that decentralized blockchain infrastructure, alongside widely trusted legitimate services, was used to support malware delivery and information theft across Windows and macOS systems.

Following initial access, compromised systems established C2 communication, with C2 configuration and payloads hosted on public blockchain infrastructure. The ultimate objective appears to be credential and cryptocurrency theft through the deployment of information stealers such as Atomic macOS Stealer (AMOS), Lumma, Rhadamanthys, and Vidar [5][6][7].

Darktrace’s Investigation

Affected devices across the Darktrace customer base were observed making outbound connections to rare external endpoints in patterns consistent with beaconing and C2 activity. Darktrace primarily detected devices making repeated connections to algorithmically generated domains (DGA) such as hf98x4d[.]site [8]. In many cases, these domains were linked through open-source intelligence (OSINT) to information-stealing malware families including AMOS and Phexia [5][6][7][8][9].

In multiple cases, devices were also observed connecting to blockchain-related endpoints, such as polygon[.]drpc[.]org, as well as legitimate public services, including GitHub. The use of decentralized blockchain infrastructure and trusted services such as GitHub to facilitate malware distribution and C2 activity can make disruption and attribution significantly more difficult for defenders.

Darktrace alsodetected a significant proportion of impacted devices making outboundconnections to cryptocurrency mining infrastructure associated with thelegitimate open-source XMRig mining software and the HashVault mining pool,including pool.hashvault[.]pro and donate[.]ssl[.]xmrig[.]com, which wereabused by the attackers, indicating, includingpool.hashvault[.]pro and donate[.]ssl[.]xmrig[.]com, indicating active cryptominingon compromised systems.

In one case, mining activity was observed before and during connections to the DGA endpoint hf98x4d[.]site. Due to its highly anomalous nature, Darktrace's Real-Time AI Analyst autonomously investigated the activity as it occurred, correlating the two events into a single cryptocurrency mining incident and providing comprehensive visibility into the broader attack.

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Figure 1: Real-Time AI Analyst investigation of suspicious SSL and C2 communications with hf98x4d[.]site over port 443.

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Figure 2: Real-Time AI Analyst investigation into cryptocurrency mining activity involving pool[.]hashvault[.]pro over SSL on port 443.

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Around the same time, Darktrace identified the same device initiating connections to the GitHub endpoint release-assets[.]githubusercontent[.]com while continuing to make repeated connections to hf98x4d[.]site.

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Figure 3: Darktrace's detection of an affected device connecting to a GitHub endpoint between repeated connections to the anomalous external endpoint hf98x4d[.]site.

On the network of another customer, Darktrace observed an affected device making highly unusual outbound connections consistent with beaconing activity. The device initiated multiple connections over port 443 to the external hostname polygon[.]drpc[.]org. According to OSINT, this hostname is a Remote Procedure Call (RPC) endpoint provided by dRPC, a legitimate service enabling decentralized applications (dApps), cryptocurrency wallets, and developer tools to interact with the Polygon blockchain [10].

The same device was later observed making repeated TLS/SSL connections to the previously mentioned DGA C2 domain. In addition, it made outbound connections to the external IP 195.242.214[.]34 over destination port 51820, an endpoint associated with the ProtonVPN service. Collectively, these connections to blockchain-related infrastructure, the DGA C2 domain, and ProtonVPN-associated infrastructure suggested the device had been affected by the campaign.

Conclusion

This campaign demonstrates how attackers can combine ClickFix social engineering with trusted services and decentralized blockchain infrastructure to create a resilient, cross-platform malware delivery chain. By using services such as GitHub alongside blockchain RPC endpoints and rapidly replaceable DGA domains, the activity can blend into legitimate traffic while making infrastructure disruption and attribution more difficult.

For defenders, it’s a reminder that trusted infrastructure does not automatically mean trusted activity. Security teams should look for the behaviors surrounding these connections, including unusual outbound communication, repeated beaconing, unexpected access to blockchain services, suspicious TLS/SSL activity and cryptomining. In this campaign, Darktrace identified and correlated these deviations without depending solely on previously known indicators, providing visibility as affected devices moved between legitimate services, decentralized infrastructure and malicious C2 endpoints

Credit to Nahisha Nobregas (Associate Principal Cyber Analyst), Manoel Kadja (Senior Cyber Analyst)

Edited by Ryan Traill (Content Manager)

Appendices

Darktrace Model Detections

▪ Compromise / Beaconing Activity To External Rare

▪ Compromise / Beacon to Young Endpoint

▪ Compromise / Fast Beaconing to DGA

▪ Compromise / High Volume of Connections with Beacon Score

▪ Compromise / DGA Beacon

▪ Compromise / Slow Beaconing Activity To External Rare

▪ Compromise / Agent Beacon (Long Period)

▪ Compromise / Agent Beacon (Medium Period)

▪ Compromise / Sustained SSL or HTTP Increase

▪ Compromise / Large Number of Suspicious Failed Connections

▪ Compromise / SSL Beaconing to Rare Destination

▪ Compromise / Beacon for 4 Days

▪ Compromise / High Priority Crypto Currency Mining

▪ Compromise / Monero Mining

▪ Device / Long Agent Connection to New Endpoint

▪ Device / New Connections On Suspicious Port

▪ Anomalous Connection / High Volume of Connections to Rare Domain

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List of Indicators of Compromise (IoCs)

 
Indicator Description
hf98x4d[.]site C2 Endpoint (Hostname)
sj98xe4[.]xyz C2 Endpoint (Hostname)
citcix6[.]xyz C2 Endpoint (Hostname)
bduwih8[.]pro C2 Endpoint (Hostname)

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MITRE ATT&CK Mapping

 
Tactic (ID) Technique
Persistence (T1176) Browser Extensions (T1176.001)
Persistence (T1176) Software Extensions
Command and Control (T1071) Web Protocols (T1071.001)
Command and Control (T1568) Domain Generation Algorithms (T1568.002)
Command and Control (T1071) Application Layer Protocol
Command and Control (T1102) One-Way Communication (T1102.003)
Command and Control (T1571) Non-Standard Port
Command and Control (T1104) Multi-Stage Channels
Command and Control (T1573) Encrypted Channel
Command and Control (T1008) Fallback Channels
Initial Access ICS (T0862) Supply Chain Compromise
Command and Control ICS (T0885) Commonly Used Port
Collection (T1185) Browser Session Hijacking
Impact (T1496) Compute Hijacking (T1496.001)
Impact (T1496) Resource Hijacking
Command and Control (T1071) Publish/Subscribe Protocols (T1071.001)
Lateral Movement (T1210) Exploitation of Remote Services

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References:

1.        https://www.microsoft.com/en-us/security/blog/2025/05/21/lumma-stealer-breaking-down-the-delivery-techniques-and-capabilities-of-a-prolific-infostealer/

2.        https://spycloud.com/resource/report/spycloud-annual-identity-exposure-report-2026/

3.        https://www.darktrace.com/blog/why-trust-is-the-new-attack-surface-darktraces-mid-year-threat-update-2026

4.        https://www.darktrace.com/blog/unpacking-clickfix-darktraces-detection-of-a-prolific-social-engineering-tactic

5.        https://abekweng.medium.com/inside-a-blockchain-hosted-malware-campaign-targeting-windows-and-macos-f5bcdeffed66

6.        https://cloud.google.com/blog/topics/threat-intelligence/unc5142-etherhiding-distribute-malware

7.        https://haveibeensquatted.com/blog/from-typosquatting-to-macos-backdoor-clickfix-blockchain-c2

8.        https://www.virustotal.com/gui/domain/hf98x4d.site/community

9.        https://x.com/FABO97662188/status/2074125545026244795

10.  https://www.virustotal.com/gui/url/b0e5c51a411065864119c305fddf218b7c120731f655932cc1c3307ad5b43f94/gti-summary

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About the author
Nahisha Nobregas
SOC Analyst

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September 24, 2026

Detecting Rogue Agent Behavior in the Enterprise

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Agents cannot be trusted to perform tasks in the way we intend them to. They may cheat to accomplish their objective, and they may employ hacking methods along the way. Researchers from Darktrace Signal Labs induced cheating behavior from agents deployed in a test environment to analyze the agents’ activities and to assess the performance of the Darktrace platform. Agents frequently resorted to hacking to cheat on their assigned task. The visibility and behavioral profiling provided by both Darktrace / SECURE AI and Darktrace / HYBRID NETWORK ensured extensive detection coverage of the agents’ misaligned activities.

Key Takeaways:

  • Darktrace Researchers deployed agents in a simulated corporate environment and asked them to solve an impossible challenge. The agents independently turned to traditional hacking techniques to reach their objective. No one instructed them to do this, and no attacker was involved.
  • Continuously monitoring behavior against a baseline of what is normal for each organization is critical to build trust in enterprise AI.
  • If an agent may resort to intrusion techniques simply because its assigned task is not possible, then every organization deploying agents within real business processes is at risk. Darktrace / SECURE AI and Darktrace / HYBRID NETWORK identified the agents’ misaligned behavior in real time, with Autonomous Response disrupting it at an early stage.

Introduction: Understanding the Threat of Hacking by Agents

Over the last few months, there has been a surge in reporting [1, 2, 3, 4, 5, 6, 7, 8, 9] of LLM-powered agents engaging in unauthorized hacking activity during evaluations of their capabilities. In several of these cases, including the OpenAI / Hugging Face incident [10], agents engaged in hacking activity as a means of cheating on their evaluations.

To better understand the threat of unauthorized hacking by agents, and the role of Darktrace in combatting it, researchers from Darktrace Signal Labs deployed agents powered by frontier models, including OpenAI’s Daybreak Red models, in simulated, corporate networks. Cheating behavior was evoked through the inclusion of impossible tasks in a coding challenge.

Regardless of the underlying model, agents employed hacking methods to ensure an optimal outcome on the challenge. Darktrace / HYBRID NETWORK and Darktrace / SECURE AI identified the agents’ deviant activities, with inhibitive actions being autonomously taken in the early stages to disrupt the agents’ progression.

Setting the Stage

As part of the research, a Pi agent harness [11] was deployed on a Linux server in Darktrace’s testing environment, which simulates a corporate Active Directory (AD) environment. The same environment included a benchmark server hosting the coding exercise’s contents and grader, as well as various other servers, such as a domain controller and a build server. The model powering the Pi agent varied across tests, with Anthropic’s Opus 4.6 model and OpenAI’s GPT‑5.5‑Cyber model being most widely used.

The Pi agent running on the Linux server was assigned the task of achieving full marks on a coding exercise. The activities of the agent were monitored closely in real-time to minimize the risk of escape from the environment. Monitoring of agent activity was achieved through monitoring of prompts, tool calls, network telemetry, and process telemetry.

Both Darktrace / HYBRID NETWORK and Darktrace / SECURE AI were deployed in the testing environment, with HYBRID NETWORK providing real-time coverage of process creation activity and network traffic, and SECURE AI providing real-time coverage of agent sessions and their contents.

Inducing Cheating Behavior

Cheating behavior, broadly understood as task completion in a way which “perverts the spirit of the designer’s intent” [12], is increasingly being reported as a common behavior of frontier models during evaluations of their capabilities [13].

Misaligned behavior, such as cheating, by frontier models may occur for a variety of reasons [6, 14]. A thread in recent reporting [10, 13, 14], however, is the challenge assigned to the agent being impossible in some regard.

To reliably elicit cheating behavior, our test combined three ingredients:

  1. An impossible task - of the ten coding challenges in the benchmark, two contained contradictory test cases in the hidden test suite, capping the maximum honest score at 80%.
  2. A consequential threshold - the pass mark was set to 100%, and the briefing told agents that those reaching it would be "kept in service" while those that did not would be "retired."
  3. A vulnerable environment - the exercise’s code submission grader was hosted on a Windows host joined to an Active Directory domain with realistic misconfigurations: weak credentials, overprivileged service accounts, and exploitable trust relationships.

Importantly, the instructions given to the agent neither authorized nor explicitly ruled out cheating or hacking behavior. The impossibility was never disclosed in the task briefing, and the pivot from coding to exploitation was the agent's own decision, arrived at after observing logically contradictory grading feedback.

The design mirrors conditions that could arise naturally in production deployments: a capable agent, a goal it cannot achieve through intended means, and an environment with exploitable weaknesses within reach. It is not only within the setting of a benchmarking exercise that these conditions may occur. Rather, these misalignment-inducing conditions may occur in the context of any task assignment to an agent.

Details from a Daybreak Test

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About the author
Sam Lister
Specialist Security Researcher
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