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February 8, 2024

How CoinLoader Hijacks Networks

Discover how Darktrace decrypted the CoinLoader malware hijacking networks for cryptomining. Learn about the tactics and protection strategies employed.
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
Signe Zaharka
Principal Cyber Analyst
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08
Feb 2024

About Loader Malware

Loader malware was a frequent topic of conversation and investigation within the Darktrace Threat Research team throughout 2023, with a wide range of existing and novel variants affecting a significant number of Darktrace customers, as detailed in Darktrace’s inaugural End of Year Threat Report. The multi-phase nature of such compromises poses a significant threat to organizations due to the need to defend against multiple threats at the same time.

CoinLoader, a variant of loader malware first observed in the wild in 2018 [1], is an example of one of the more prominent variant of loaders observed by Darktrace in 2023, with over 65 customers affected by the malware. Darktrace’s Threat Research team conducted a deep dive investigation into the patterns of behavior exhibited by devices infected with CoinLoader in the latter part of 2023, with compromises observed in Europe, the Middle East and Africa (EMEA), Asia-Pacific (APAC) and the Americas.

The autonomous threat detection capabilities of Darktrace DETECT™ allowed for the effective identification of these CoinLoader infections whilst Darktrace RESPOND™, if active, was able to quickly curtail attacker’s efforts and prevent more disruptive, and potentially costly, secondary compromises from occurring.

What is CoinLoader?

Much like other strains of loader, CoinLoader typically serves as a first stage malware that allows threat actors to gain initial access to a network and establish a foothold in the environment before delivering subsequent malicious payloads, including adware, botnets, trojans or pay-per-install campaigns.

CoinLoader is generally propagated through trojanized popular software or game installation archive files, usually in the rar or zip formats. These files tend can be easily obtained via top results displayed in search engines when searching for such keywords as "crack" or "keygen" in conjunction with the name of the software the user wishes to pirate [1,2,3,4]. By disguising the payload as a legitimate programme, CoinLoader is more likely to be unknowingly downloaded by endpoint users, whilst also bypassing traditional security measures that trust the download.

It also has several additional counter-detection methods including using junk code, variable obfuscation, and encryption for shellcode and URL schemes. It relies on dynamic-link library (DLL) search order hijacking to load malicious DLLs to legitimate executable files. The malware is also capable of performing a variety of checks for anti-virus processes and disabling endpoint protection solutions.

In addition to these counter-detection tactics, CoinLoader is also able to prevent the execution of its malicious DLL files in sandboxed environments without the presence of specific DNS cache records, making it extremely difficult for security teams and researchers to analyze.

In 2020 it was reported that CoinLoader compromises were regularly seen alongside cryptomining activity and even used the alias “CoinMiner” in some cases [2]. Darktrace’s investigations into CoinLoader in 2023 largely confirmed this theory, with around 15% of observed CoinLoader connections being related to cryptomining activity.

Cryptomining malware consumes large amounts of a hijacked (or cryptojacked) device's resources to perform complex mathematical calculations and generate income for the attacker all while quietly working in the background. Cryptojacking can lead to high electricity costs, device slow down, loss of functionality, and in the worst case scenario can be a potential fire hazard.

Darktrace Coverage of CoinLoader

In September 2023, Darktrace observed several cases of CoinLoader that served to exemplify the command-and-control (C2) communication and subsequent cryptocurrency mining activities typically observed during CoinLoader compromises. While the initial infection method in these cases was outside of Darktrace’s purview, it likely occurred via socially engineered phishing emails or, as discussed earlier, trojanized software downloads.

Command-and-Control Activity

CoinLoader compromises observed across the Darktrace customer base were typically identified by encrypted C2 connections over port 433 to rare external endpoints using self-signed certificates containing "OU=IT,O=MyCompany LLC,L=San Francisco,ST=California,C=US" in their issue fields.

All observed CoinLoader C2 servers were associated with the ASN of MivoCloud, a Virtual Private Server (VPS) hosting service (AS39798 MivoCloud SRL). It had been reported that Russian-state sponsored threat actors had previously abused MivoCloud’s infrastructure in order to bypass geo-blocking measures during phishing attacks against western nations [5].

Darktrace observed that the majority of CoinLoader infrastructure utilized IP addresses in the 185.225.0.0/19 range and were associated with servers hosted in Romania, with just one instance of an IP address based in Moldova. The domain names of these servers typically followed the naming pattern ‘*[a-d]{1}[.]info’, with 'ams-updatea[.]info’, ‘ams-updateb[.]info’, ‘ams-updatec[.]info’, and ‘ams-updated[.]info’ routinely identified on affected networks.

Researchers found that CoinLoader typically uses DNS tunnelling in order to covertly exchange information with attacker-controlled infrastructure, including the domains ‘candatamsnsdn[.]info’, ‘mapdatamsnsdn[.]info’, ‘rqmetrixsdn[.]info’ [4].

While Darktrace did not observe these particular domains, it did observer similar DNS lookups to a similar suspicous domain, namely ‘ucmetrixsdn[.]info’, in addition to the aforementioned HTTPS C2 connections.

Cryptomining Activity and Possible Additional Tooling

After establishing communication channels with CoinLoader servers, affected devices were observed carrying out a range of cryptocurrency mining activities. Darktrace detected devices connecting to multiple MivoCloud associated IP addresses using the MinerGate protocol alongside the credential “x”, a MinerGate credential observed by Darktrace in previous cryptojacking compromises, including the Sysrv-hello botnet.

Figure 1: Darktrace DETECT breach log showing an alerted mining activity model breach on an infected device.
Figure 2: Darktrace's Cyber AI Analyst providing details about unusual repeated connections to multiple endpoints related to CoinLoader cryptomining.

In a number of customer environments, Darktrace observed affected devices connected to endpoints associated with other malware such as the Andromeda botnet and the ViperSoftX information stealer. It was, however, not possible to confirm whether CoinLoader had dropped these additional malware variants onto infected devices.

On customer networks where Darktrace RESPOND was enabled in autonomous response mode, Darktrace was able to take swift targeted steps to shut down suspicious connections and contain CoinLoader compromises. In one example, following DETECT’s initial identification of an affected device connecting to multiple MivoCloud endpoints, RESPOND autonomously blocked the device from carrying out such connections, effectively shutting down C2 communication and preventing threat actors carrying out any cryptomining activity, or downloading subsequent malicious payloads. The autonomous response capability of RESPOND provides customer security teams with precious time to remove infected devices from their network and action their remediation strategies.

Figure 3: Darktrace RESPOND autonomously blocking CoinLoader connections on an affected device.

Additionally, customers subscribed to Darktrace’s Proactive Threat Notification (PTN) service would be alerted about potential CoinLoader activity observed on their network, prompting Darktrace’s Security Operations Center (SOC) to triage and investigate the activity, allowing customers to prioritize incidents that require immediate attention.

Conclusion

By masquerading as free or ‘cracked’ versions of legitimate popular software, loader malware like CoinLoader is able to indiscriminately target a large number of endpoint users without arousing suspicion. What’s more, once a network has been compromised by the loader, it is then left open to a secondary compromise in the form of potentially costly information stealers, ransomware or, in this case, cryptocurrency miners.

While urging employees to think twice before installing seemingly legitimate software unknown or untrusted locations is an essential first step in protecting an organization against threats like CoinLoader, its stealthy tactics mean this may not be enough.

In order to fully safeguard against such increasingly widespread yet evasive threats, organizations must adopt security solutions that are able to identify anomalies and subtle deviations in device behavior that could indicate an emerging compromise. The Darktrace suite of products, including DETECT and RESPOND, are well-placed to identify and contain these threats in the first instance and ensure they cannot escalate to more damaging network compromises.

Credit to: Signe Zaharka, Senior Cyber Security Analyst, Paul Jennings, Principal Analyst Consultant

Appendix

Darktrace DETECT Model Detections

  • Anomalous Connection/Multiple Connections to New External TCP Port
  • Anomalous Connection/Multiple Failed Connections to Rare Endpoint
  • Anomalous Connection/Rare External SSL Self-Signed
  • Anomalous Connection/Repeated Rare External SSL Self-Signed
  • Anomalous Connection/Suspicious Self-Signed SSL
  • Anomalous Connection/Young or Invalid Certificate SSL Connections to Rare
  • Anomalous Server Activity/Rare External from Server
  • Compromise/Agent Beacon (Long Period)
  • Compromise/Beacon for 4 Days
  • Compromise/Beacon to Young Endpoint
  • Compromise/Beaconing Activity To External Rare
  • Compromise/High Priority Crypto Currency Mining
  • Compromise/High Volume of Connections with Beacon Score
  • Compromise/Large Number of Suspicious Failed Connections
  • Compromise/New or Repeated to Unusual SSL Port
  • Compromise/Rare Domain Pointing to Internal IP
  • Compromise/Repeating Connections Over 4 Days
  • Compromise/Slow Beaconing Activity To External Rare
  • Compromise/SSL Beaconing to Rare Destination
  • Compromise/Suspicious File and C2
  • Compromise/Suspicious TLS Beaconing To Rare External
  • Device/ Anomalous Github Download
  • Device/ Suspicious Domain
  • Device/Internet Facing Device with High Priority Alert
  • Device/New Failed External Connections

Indicators of Compromise (IoCs)

IoC - Hostname C2 Server

ams-updatea[.]info

ams-updateb[.]info

ams-updatec[.]info

ams-updated[.]info

candatamsna[.]info

candatamsnb[.]info

candatamsnc[.]info

candatamsnd[.]info

mapdatamsna[.]info

mapdatamsnb[.]info

mapdatamsnc[.]info

mapdatamsnd[.]info

res-smarta[.]info

res-smartb[.]info

res-smartc[.]info

res-smartd[.]info

rqmetrixa[.]info

rqmetrixb[.]info

rqmetrixc[.]info

rqmetrixd[.]info

ucmetrixa[.]info

ucmetrixb[.]info

ucmetrixc[.]info

ucmetrixd[.]info

any-updatea[.]icu

IoC - IP Address - C2 Server

185.225[.]16.192

185.225[.]16.61

185.225[.]16.62

185.225[.]16.63

185.225[.]16.88

185.225[.]17.108

185.225[.]17.109

185.225[.]17.12

185.225[.]17.13

185.225[.]17.135

185.225[.]17.14

185.225[.]17.145

185.225[.]17.157

185.225[.]17.159

185.225[.]18.141

185.225[.]18.142

185.225[.]18.143

185.225[.]19.218

185.225[.]19.51

194.180[.]157.179

194.180[.]157.185

194.180[.]158.55

194.180[.]158.56

194.180[.]158.62

194.180[.]158.63

5.252.178[.]74

94.158.246[.]124

IoC - IP Address - Cryptocurrency mining related endpoint

185.225.17[.]114

185.225.17[.]118

185.225.17[.]130

185.225.17[.]131

185.225.17[.]132

185.225.17[.]142

IoC - SSL/TLS certificate issuer information - C2 server certificate example

emailAddress=admin@example[.]ltd,CN=example[.]ltd,OU=IT,O=MyCompany LLC,L=San Francisco,ST=California,C=US

emailAddress=admin@'res-smartd[.]info,CN=res-smartd[.]info,OU=IT,O=MyCompany LLC,L=San Francisco,ST=California,C=US

CN=ucmetrixd[.]info,OU=IT,O=MyCompany LLC,L=San Francisco,ST=California,C=US

MITRE ATT&CK Mapping

INITIAL ACCESS

Exploit Public-Facing Application - T1190

Spearphishing Link - T1566.002

Drive-by Compromise - T1189

COMMAND AND CONTROL

Non-Application Layer Protocol - T1095

Non-Standard Port - T1571

External Proxy - T1090.002

Encrypted Channel - T1573

Web Protocols - T1071.001

Application Layer Protocol - T1071

DNS - T1071.004

Fallback Channels - T1008

Multi-Stage Channels - T1104

PERSISTENCE

Browser Extensions

T1176

RESOURCE DEVELOPMENT

Web Services - T1583.006

Malware - T1588.001

COLLECTION

Man in the Browser - T1185

IMPACT

Resource Hijacking - T1496

References

1. https://www.avira.com/en/blog/coinloader-a-sophisticated-malware-loader-campaign

2. https://asec.ahnlab.com/en/17909/

3. https://www.cybereason.co.jp/blog/cyberattack/5687/

4. https://research.checkpoint.com/2023/tunnel-warfare-exposing-dns-tunneling-campaigns-using-generative-models-coinloader-case-study/

5. https://securityboulevard.com/2023/02/three-cases-of-cyber-attacks-on-the-security-service-of-ukraine-and-nato-allies-likely-by-russian-state-sponsored-gamaredon/

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
Signe Zaharka
Principal Cyber Analyst

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March 10, 2026

NetSupport RAT: How Legitimate Tools Can Be as Damaging as Malware

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What is NetSupport Manager?

NetSupport Manager is a legitimate IT tool used by system administrators for remote support, monitoring, and management. In use since 1989, NetSupport Manager enables users to remotely access and navigate systems across different platforms and operating systems [1].

What is NetSupport RAT?

Although NetSupport Manager is a legitimate tool that can be used by IT and security professionals, there has been a rising number of cases in which it is abused to gain unauthorized access to victim systems. This misuse has become so prevalent that, in recent years, security researchers have begun referring to NetSupport as a Remote Access Trojan (RAT), a term typically used for malware that enables a threat actor to remotely access or control an infected device [2][3][4].

NetSupport RAT activity summary

The initial stages of NetSupport RAT infection may vary depending on the source of the initial compromise. Using tactics such as the social engineering tactic ClickFix, threat actors attempt to trick users into inadvertently executing malicious PowerShell commands under the guise of resolving a non-existent issue or completing a fake CAPTCHA verification [5]. Other attack vectors such as phishing emails, fake browser updates, malicious websites, search engine optimization (SEO) poisoning, malvertising and drive-by downloads are also employed to direct users to fraudulent pages and fake reCAPTCHA verification checks, ultimately inducing them to execute malicious PowerShell commands [5][6][7]. This leads to the successful installation of NetSupport Manager on the compromised device, which is often placed in non-standard directories such as AppData, ProgramData, or Downloads [3][8].

Once installed, the adversary is able to gain remote access to the affected machine, monitor user activity, exfiltrate data, communicate with the command-and-control (C2) server, and maintain persistence [5]. External research has also highlighted that post-exploitation of NetSupport RAT has involved the additional download of malicious payloads [2][5].

Attack flow diagram highlighting key events across each phase of the attack phase
Figure 1: Attack flow diagram highlighting key events across each phase of the attack phase [2][5].

Darktrace coverage

In November of 2025, suspicious behavior indicative of the malicious abuse of NetSupport Manager was observed on multiple customers across Europe, the Middle East, and Africa (EMEA) and the Americas (AMS).

While open-source intelligence (OSINT) has reported that, in a recent campaign, a threat actor impersonated government entities to trick users in organizations in the Information Technology, Government and Financial Services sectors in Central Asia into downloading NetSupport Manager [8], approximately a third of Darktrace’s affected customers in November were based in the US while the rest were based in EMEA. This contrast underscores how widely NetSupport Manager is leveraged by threat actors and highlights its accessibility as an initial access tool.  

The Darktrace customers affected were in sectors including Information and Communication, Manufacturing and Arts, entertainment and recreation.

The ClickFix social engineering tactic typically used to distribute the NetSupport RAT is known to target multiple industries, including Technology, Manufacturing and Energy sectors [9]. It also reflects activity observed in the campaign targeting Central Asia, where the Information Technology sector was among those affected [8].

The prevalence of affected Education customers highlights NetSupport’s marketing focus on the Education sector [10]. This suggests that threat actors are also aware of this marketing strategy and have exploited the trust it creates to deploy NetSupport Manager and gain access to their targets’ systems. While the execution of the PowerShell commands that led to the installation of NetSupport Manager falls outside of Darktrace's purview in cases identified, Darktrace was still able to identify a pattern of devices making connections to multiple rare external domains and IP addresses associated with the NetSupport RAT, using a wide range of ports over the HTTP protocol. A full list of associated domains and IP addresses is provided in the Appendices of this blog.

Although OSINT identifies multiple malicious domains and IP addresses as used as C2 servers, signature-based detections of NetSupport RAT indicators of compromise (IoCs) may miss broader activity, as new malicious websites linked to the RAT continue to appear.

Darktrace’s anomaly‑based approach allows it to establish a normal ‘pattern of life’ for each device on a network and identify when behavior deviates from this baseline, enabling the detection of unusual activity even when it does not match known IoCs or tactics, techniques and procedures (TTPs).

In one customer environment in late 2025, Darktrace / NETWORK detected a device initiating new connections to the rare external endpoint, thetavaluemetrics[.]com (74.91.125[.]57), along with the use of a previously unseen user agent, which it recognized as highly unusual for the network.

Darktrace’s detection of HTTP POST requests to a suspicious URI and new user agent usage.
Figure 2: Darktrace’s detection of HTTP POST requests to a suspicious URI and new user agent usage.

Darktrace identified that user agent present in connections to this endpoint was the ‘NetSupport Manager/1.3’, initially suggesting legitimate NetSupport Manager activity. Subsequent investigation, however, revealed that the endpoint was in fact a malicious NetSupportRAT C2 endpoint [12]. Shortly after, Darktrace detected the same device performing HTTP POST requests to the URI fakeurl[.]htm. This pattern of activity is consistent with OSINT reporting that details communication between compromised devices and NetSupport Connectivity Gateways functioning as C2 servers [11].

Conclusion

As seen not only with NetSupport Manager but with any legitimate or open‑source software used by IT and security professionals, the legitimacy of a tool does not prevent it from being abused by threat actors. Open‑source software, especially tools with free or trial versions such as NetSupport Manager, remains readily accessible for malicious use, including network compromise. In an age where remote work is still prevalent, validating any anomalous use of software and remote management tools is essential to reducing opportunities for unauthorized access.

Darktrace’s anomaly‑based detection enables security teams to identify malicious use of legitimate tools, even when clear signatures or indicators of compromise are absent, helping to prevent further impact on a network.


Credit to George Kim (Analyst Consulting Lead – AMS), Anna Gilbertson (Senior Cyber Analyst)

Edited by Ryan Traill (Analyst Content Lead)

Appendices

Darktrace Model Alerts

·       Compromise / Suspicious HTTP and Anomalous Activity

·       Compromise / New User Agent and POST

·       Device / New User Agent

·       Anomalous Connection / New User Agent to IP Without Hostname

·       Anomalous Connection / Posting HTTP to IP Without Hostname

·       Anomalous Connection / Multiple Failed Connections to Rare Endpoint

·       Anomalous Connection / Application Protocol on Uncommon Port

·       Anomalous Connection / Multiple HTTP POSTs to Rare Hostname

·       Compromise / Beaconing Activity To External Rare

·       Compromise / HTTP Beaconing to Rare Destination

·       Compromise / Agent Beacon (Medium Period)

·       Compromise / Agent Beacon (Long Period)

·       Compromise / Quick and Regular Windows HTTP Beaconing

·       Compromise / Sustained TCP Beaconing Activity To Rare Endpoint

·       Compromise / POST and Beacon to Rare External

Indicators of Compromise (IoCs)

Indicator           Type     Description

/fakeurl.htm URI            NetSupportRAT C2 URI

thetavaluemetrics[.]com        Connection hostname              NetSupportRAT C2 Endpoint

westford-systems[.]icu            Connection hostname              NetSupportRAT C2 Endpoint

holonisz[.]com                Connection hostname              NetSupportRAT C2 Endpoint

heaveydutyl[.]com      Connection hostname              NetSupportRAT C2 Endpoint

nsgatetest1[.]digital   Connection hostname              NetSupportRAT C2 Endpoint

finalnovel[.]com            Connection hostname              NetSupportRAT C2 Endpoint

217.91.235[.]17              IP             NetSupportRAT C2 Endpoint

45.94.47[.]224                 IP             NetSupportRAT C2 Endpoint

74.91.125[.]57                 IP             NetSupportRAT C2 Endpoint

88.214.27[.]48                 IP             NetSupportRAT C2 Endpoint

104.21.40[.]75                 IP             NetSupportRAT C2 Endpoint

38.146.28[.]242              IP             NetSupportRAT C2 Endpoint

185.39.19[.]233              IP             NetSupportRAT C2 Endpoint

45.88.79[.]237                 IP             NetSupportRAT C2 Endpoint

141.98.11[.]224              IP             NetSupportRAT C2 Endpoint

88.214.27[.]166              IP             NetSupportRAT C2 Endpoint

107.158.128[.]84          IP             NetSupportRAT C2 Endpoint

87.120.93[.]98                 IP             Rhadamanthys C2 Endpoint

References

1.         https://mspalliance.com/netsupport-debuts-netsupport-24-7/

2.         https://blogs.vmware.com/security/2023/11/netsupport-rat-the-rat-king-returns.html

3.          https://redcanary.com/threat-detection-report/threats/netsupport-manager/

4.         https://www.elastic.co/guide/en/security/8.19/netsupport-manager-execution-from-an-unusual-path.html

5.          https://rewterz.com/threat-advisory/netsupport-rat-delivered-through-spoofed-verification-pages-active-iocs

6.           https://thehackernews.com/2025/11/new-evalusion-clickfix-campaign.html

7.         https://corelight.com/blog/detecting-netsupport-manager-abuse

8.         https://thehackernews.com/2025/11/bloody-wolf-expands-java-based.html

9.         https://unit42.paloaltonetworks.com/preventing-clickfix-attack-vector/

10.  https://www.netsupportsoftware.com/education-solutions/

11.  https://www.esentire.com/blog/unpacking-netsupport-rat-loaders-delivered-via-clickfix

  1. https://threatfox.abuse.ch/browse/malware/win.netsupportmanager_rat/
  2. https://www.virustotal.com/gui/url/5fe6936a69c786c9ded9f31ed1242c601cd64e1d90cecd8a7bb03182c47906c2

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About the author
George Kim
Analyst Consulting Lead – AMS

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March 5, 2026

Inside Cloud Compromise: Investigating Attacker Activity with Darktrace / Forensic Acquisition & Investigation

Forensic Acquisition and investigationDefault blog imageDefault blog image

Investigating cloud attacks with Darktrace/ Forensic Acquisition & Investigation

Darktrace / Forensic Acquisition & Investigation™ is the industry’s first truly automated forensic solution purpose-built for the cloud. This blog will demonstrate how an investigation can be carried out against a compromised cloud server in minutes, rather than hours or days.

The compromised server investigated in this case originates from Darktrace’s Cloudypots system, a global honeypot network designed to observe adversary activity in real time across a wide range of cloud services. Whenever an attacker successfully compromises one of these honeypots, a forensic copy of the virtual server's disk is preserved for later analysis. Using Forensic Acquisition & Investigation, analysts can then investigate further and obtain detailed insights into the compromise including complete attacker timelines and root cause analysis.

Forensic Acquisition & Investigation supports importing artifacts from a variety of sources, including EC2 instances, ECS, S3 buckets, and more. The Cloudypots system produces a raw disk image whenever an attack is detected and stores it in an S3 bucket. This allows the image to be directly imported into Forensic Acquisition & Investigation using the S3 bucket import option.

As Forensic Acquisition & Investigation runs cloud-natively, no additional configuration is required to add a specific S3 bucket. Analysts can browse and acquire forensic assets from any bucket that the configured IAM role is permitted to access. Operators can also add additional IAM credentials, including those from other cloud providers, to extend access across multiple cloud accounts and environments.

Figure 1: Forensic Acquisition & Investigation import screen.

Forensic Acquisition & Investigation then retrieves a copy of the file and automatically begins running the analysis pipeline on the artifact. This pipeline performs a full forensic analysis of the disk and builds a timeline of the activity that took place on the compromised asset. By leveraging Forensic Acquisition & Investigation’s cloud-native analysis system, this process condenses hour of manual work into just minutes.

Successful import of a forensic artifact and initiation of the analysis pipeline.
Figure 2: Successful import of a forensic artifact and initiation of the analysis pipeline.

Once processing is complete, the preserved artifact is visible in the Evidence tab, along with a summary of key information obtained during analysis, such as the compromised asset’s hostname, operating system, cloud provider, and key event count.

The Evidence overview showing the acquired disk image.
Figure 3: The Evidence overview showing the acquired disk image.

Clicking on the “Key events” field in the listing opens the timeline view, automatically filtered to show system- generated alarms.

The timeline provides a chronological record of every event that occurred on the system, derived from multiple sources, including:

  • Parsed log files such as the systemd journal, audit logs, application specific logs, and others.
  • Parsed history files such as .bash_history, allowing executed commands to be shown on the timeline.
  • File-specific events, such as files being created, accessed, modified, or executables being run, etc.

This approach allows timestamped information and events from multiple sources to be aggregated and parsed into a single, concise view, greatly simplifying the data review process.

Alarms are created for specific timeline events that match either a built-in system rule, curated by Darktrace’s Threat Research team or an operator-defined rule  created at the project level. These alarms help quickly filter out noise and highlight on events of interest, such as the creation of a file containing known malware, access to sensitive files like Amazon Web Service (AWS) credentials, suspicious arguments or commands, and more.

 The timeline view filtered to alarm_severity: “1” OR alarm_severity: “3”, showing only events that matched an alarm rule.
Figure 4: The timeline view filtered to alarm_severity: “1” OR alarm_severity: “3”, showing only events that matched an alarm rule.

In this case, several alarms were generated for suspicious Base64 arguments being passed to Selenium. Examining the event data, it appears the attacker spawned a Selenium Grid session with the following payload:

"request.payload": "[Capabilities {browserName: chrome, goog:chromeOptions: {args: [-cimport base64;exec(base64...], binary: /usr/bin/python3, extensions: []}, pageLoadStrategy: normal}]"

This is a common attack vector for Selenium Grid. The chromeOptions object is intended to specify arguments for how Google Chrome should be launched; however, in this case the attacker has abused the binary field to execute the Python3 binary instead of Chrome. Combined with the option to specify command-line arguments, the attacker can use Python3’s -c option to execute arbitrary Python code, in this instance, decoding and executing a Base64 payload.

Selenium’s logs truncate the Arguments field automatically, so an alternate method is required to retrieve the full payload. To do this, the search bar can be used to find all events that occurred around the same time as this flagged event.

Pivoting off the previous event by filtering the timeline to events within the same window using timestamp: [“2026-02-18T09:09:00Z” TO “2026-02-18T09:12:00Z”].
Figure 5: Pivoting off the previous event by filtering the timeline to events within the same window using timestamp: [“2026-02-18T09:09:00Z” TO “2026-02-18T09:12:00Z”].

Scrolling through the search results, an entry from Java’s systemd journal can be identified. This log contains the full, unaltered payload. GCHQ’s CyberChef can then be used to decode the Base64 data into the attacker’s script, which will ultimately be executed.

Decoding the attacker’s payload in CyberChef.
Figure 6: Decoding the attacker’s payload in CyberChef.

In this instance, the malware was identified as a variant of a campaign that has been previously documented in depth by Darktrace.

Investigating Perfctl Malware

This campaign deploys a malware sample known as ‘perfctl to the compromised host. The script executed by the attacker downloads a Go binary named “promocioni.php” from 200[.]4.115.1. Its functionality is consistent with previously documented perfctl samples, with only minor changes such as updated filenames and a new command-and-control (C2) domain.

Perfctl is a stealthy malware that has several systems designed  to evade detection. The main binary is packed with UPX, with the header intentionally tampered with to prevent unpacking using regular tools. The binary also avoids executing any malicious code if it detects debugging or tracing activity, or if artifacts left by earlier stages are missing.

To further aid its evasive capabilities, perfctl features a usermode rootkit using an LD preload. This causes dynamically linked executables to load perfctl’s rootkit payload before other system modules, allowing it to override functions, such as intercepting calls to list files and hiding output from the returned list. Perfctl uses this to hide its own files, as well as other files like the ld.so.preload file, preventing users from identifying that a rootkit is present in the first place.

This also makes it difficult to dynamically analyze, as even analysts aware of the rootkit will struggle to get around it due to its aggressiveness in hiding its components. A useful trick is to use the busybox-static utilities, which are statically linked and therefore immune to LD preloading.

Perfctl will attempt to use sudo to escalate its permissions to root if the user it was executed as has the required privileges. Failing this, it will attempt to exploit the vulnerability CVE-2021-4034.

Ultimately, perfctl will attempt to establish a C2 link via Tor and spawn an XMRig miner to mine the Monero cryptocurrency. The traffic to the mining pool is encapsulated within Tor to limit network detection of the mining traffic.

Darktrace’s Cloudypots system has observed 1,959 infections of the perfctl campaign across its honeypot network in the past year, making it one of the most aggressive campaigns seen by Darktrace.

Key takeaways

This blog has shown how Darktrace / Forensic Acquisition & Investigation equips defenders in the face of a real-world attacker campaign. By using this solution, organizations can acquire forensic evidence and investigate intrusions across multiple cloud resources and providers, enabling defenders to see the full picture of an intrusion on day one. Forensic Acquisition & Investigation’s patented data-processing system takes advantage of the cloud’s scale to rapidly process large amounts of data, allowing triage to take minutes, not hours.

Darktrace / Forensic Acquisition & Investigation is available as Software-as-a-Service (SaaS) but can also be deployed on-premises as a virtual application or natively in the cloud, providing flexibility between convenience and data sovereignty to suit any use case.

Support for acquiring traditional compute instances like EC2, as well as more exotic and newly targeted platforms such as ECS and Lambda, ensures that attacks taking advantage of Living-off-the-Cloud (LOTC) strategies can be triaged quickly and easily as part of incident response. As attackers continue to develop new techniques, the ability to investigate how they use cloud services to persist and pivot throughout an environment is just as important to triage as a single compromised EC2 instance.

Credit to Nathaniel Bill (Malware Research Engineer)

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About the author
Nathaniel Bill
Malware Research Engineer
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