Blog
/
Network
/
November 20, 2023

Understanding and Mitigating Sectop RAT

Understand the risks posed by the Sectop remote access Trojan and how Darktrace implements strategies to enhance cybersecurity defenses.
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
Justin Torres
Cyber Analyst
Default blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog image
20
Nov 2023

Introduction

As malicious actors across the threat landscape continue to look for new ways to gain unauthorized access to target networks, it is unsurprising to see Remote Access Trojans (RATs) leveraged more and more. These RATs are downloaded discretely without the target’s knowledge, typically through seemingly legitimate software downloads, and are designed to gain highly privileged network credentials, ultimately allowing attackers to have remote control over compromised devices. [1]

SectopRAT is one pertinent example of a RAT known to adopt a number of stealth functions in order to gather and exfiltrate sensitive data from its targets including passwords, cookies, autofill and history data stores in browsers, as well as cryptocurrency wallet details and system hardware information. [2]

In early 2023, Darktrace identified a resurgence of the SectopRAT across customer environments, primarily targeting educational industries located in the United States (US), Europe, the Middle East and Africa (EMEA) and Asia-Pacific (APAC) regions. Darktrace DETECT™ was able to successfully identify suspicious activity related to SectopRAT at the network level, as well as any indicators of post-compromise on customer environments that did not have Darktrace RESPOND™ in place to take autonomous preventative action.

What is SectopRAT?

First discovered in early 2019, the SectopRAT is a .NET RAT that contains information stealing capabilities. It is also known under the alias ‘ArechClient2’, and is commonly distributed through drive-by downloads of illegitimate software and utilizes malvertising, including via Google Ads, to increase the chances of it being downloaded.

The malware’s code was updated at the beginning of 2021, which led to refined and newly implemented features, including command and control (C2) communication encryption with Advanced Encryption Stanard 256 (AES256) and additional commands. SectopRAT also has a function called "BrowserLogging", ultimately sending any actions it conducts on web browsers to its C2 infrastructure. When the RAT is executed, it then connects to a Pastebin associated hostname to retrieve C2 information; the requested file reaches out to get the public IP address of the infected device. To receive commands, it connects to its C2 server primarily on port 15647, although other ports have been highlighted by open source intelligence (OSINT), which include 15678, 15649, 228 and 80. Ultimately, sensitive data data gathered from target networks is then exfiltrated to the attacker’s C2 infrastructure, typically in a JSON file [3].

Darktrace Coverage

During autonomous investigations into affected customer networks, Darktrace DETECT was able to identify SSL connections to the endpoint pastebin[.]com over port 443, followed by failed connections to one of the IPs and ports (i.e., 15647, 15648, 15649) associated with SectopRAT. This resulted in the devices breaching the ‘Compliance/Pastebin and Anomalous Connection/Multiple Failed Connections to Rare Endpoint’ models, respectively.

In some instances, Darktrace observed a higher number of attempted connections that resulted in the additional breach of the model ‘Compromise / Large Number of Suspicious Failed Connections’.

Over a period of three months, Darktrace investigated multiple instances of SectopRAT infections across multiple clients, highlighting indicators of compromise (IoCs) through related endpoints.Looking specififically at one customer’s activity which centred on January 25, 2023, one device was observed initially making suspicious connections to a Pastebin endpoint, 104.20.67[.]143, likely in an attempt to receive C2 information.

Darktrace DETECT recognized this activity as suspicious, causing the 'Compliance / Pastebin' DETECT models to breach. In response to this detection, Darktrace RESPOND took swift action against the Pastebin connections by blocking them and preventing the device from carrying out further connections with Pastebin endpoints. Darktrace RESPOND actions related to blocking Pastebin connections were commonly observed on this device throughout the course of the attack and likely represented threat actors attempting to exfiltrate sensitive data outside the network.

Darktrace UI image
Figure 1: Model breach event log highlighting the Darktrace DETECT model breach ‘Compliance / Pastebin’.

Around the same time, Darktrace observed the device making a large number of failed connections to an unusual exernal location in the Netherlands, 5.75.147[.]135, via port 15647. Darktrace recognized that this endpoint had never previously been observed on the customer’s network and that the frequency of the failed connections could be indicative of beaconing activity. Subsequent investigation into the endpoint using OSINT indicated it had links to malware, though Darktrace’s successful detection did not need to rely on this intelligence.

Darktrace model breach event log
Figure 2: Model breach event log highlighting the multiple failed connectiosn to the suspicious IP address, 5.75.147[.]135 on January 25, 2023, causing the Darktrace DETECT model ‘Anomalous Connection / Multiple Failed Connections to Rare Endpoint’ to breach.

After these initial set of breaches on January 25, the same device was observed engaging in further external connectivity roughly a month later on February 27, including additional failed connections to the IP 167.235.134[.]14 over port 15647. Once more, multiple OSINT sources revealed that this endpoint was indeed a malicious C2 endpoint.

Darktrace model breach event log 2
Figure 3: Model breach event log highlighting the multiple failed connectiosn to the suspicious IP address, 167.235.134[.]14 on February 27, 2023, causing the Darktrace DETECT model ‘Anomalous Connection / Multiple Failed Connections to Rare Endpoint’ to breach.

While the initial Darktrace coverage up to this point has highlighted the attempted C2 communication and how DETECT was able to alert on the suspicious activity, Pastebin activity was commonly observed throughout the course of this attack. As a result, when enabled in autonomous response mode, Darktrace RESPOND was able to take swift mitigative action by blocking all connections to Pastebin associated hostnames and IP addresses. These interventions by RESPOND ultimately prevented malicious actors from stealing sensitive data from Darktrace customers.

Darktrace RESPOND action list
Figure 4: A total of nine Darktrace RESPOND actions were applied against suspicious Pastebin activity during the course of the attack.

In another similar case investigated by the Darktrace, multiple devices were observed engaging in external connectivity to another malicious endpoint,  88.218.170[.]169 (AS207651 Hosting technology LTD) on port 15647.  On April 17, 2023, at 22:35:24 UTC, the breach device started making connections; of the 34 attempts, one connection was successful – this connection lasted 8 minutes and 49 seconds. Darktrace DETECT’s Self-Learning AI understood that these connections represented a deviation from the device’s usual pattern of behavior and alerted on the activity with the ‘Multiple Connections to new External TCP Port’ model.

Darktrace model breach event log
Figure 5: Model breach event log highlighting the affected device successfully connecting to the suspicious endpoint, 88.218.170[.]169.
Darktrace advanced search query
Figure 6: Advanced Search query highlighting the one successful connection to the endpoint 88.218.170[.]169 out of the 34 attempted connections.

A few days later, on April 20, 2023, at 12:33:59 (UTC) the source device connected to a Pastebin endpoint, 172.67.34[.]170 on port 443 using the SSL protocol, that had never previously be seen on the network. According to Advanced Search data, the first SSL connection lasted over two hours. In total, the device made 9 connections to pastebin[.]com and downloaded 85 KB of data from it.

Darktrace UI highlighting connections
Figure 7: Screenshot of the Darktrace UI highlighting the affected device making multiple connections to Pastebin and downloading 85 KB of data.

Within the same minute, Darktrace detected the device beginning to make a large number of failed connections to another suspicious endpoints, 34.107.84[.]7 (AS396982 GOOGLE-CLOUD-PLATFORM) via port 15647. In total the affected device was observed initiating 1,021 connections to this malicious endpoint, all occurring over the same port and resulting the failed attempts.

Darktrace advanced search query 2
Figure 8: Advanced Search query highlighting the affected device making over one thousand connections to the suspicious endpoint 34.107.84[.]7, all of which failed.

Conclusion

Ultimately, thanks to its Self-Learning AI and anomaly-based approach to threat detection, Darktrace was able to preemptively identify any suspicious activity relating to SectopRAT at the network level, as well as post-compromise activity, and bring it to the immediate attention of customer security teams.

In addition to the successful and timely detection of SectopRAT activity, when enabled in autonomous response mode Darktrace RESPOND was able to shut down suspicious connections to endpoints used by threat actors as malicious infrastructure, thus preventing successful C2 communication and potential data exfiltration.

In the face of a Remote Access Trojan, like SectopRAT, designed to steal sensitive corporate and personal information, the Darktrace suite of products is uniquely placed to offer organizations full visibility over any emerging activity on their networks and respond to it without latency, safeguarding their digital estate whilst causing minimal disruption to business operations.

Credit to Justin Torres, Cyber Analyst, Brianna Leddy, Director of Analysis

Appendices

Darktrace Model Detection:

  • Compliance / Pastebin
  • Anomalous Connection / Multiple Failed Connections to Rare Endpoint
  • Compromise / Large Number of Suspicious Failed Connections
  • Anomalous Connection / Multiple Connections to New External TCP Port

List of IoCs

IoC - Type - Description + Confidence

5.75.147[.]135 - IP - SectopRAT C2 Endpoint

5.75.149[.]1 - IP - SectopRAT C2 Endpoint

34.27.150[.]38 - IP - SectopRAT C2 Endpoint

34.89.247[.]212 - IP - SectopRAT C2 Endpoint

34.107.84[.]7 - IP - SectopRAT C2 Endpoint

34.141.16[.]89 - IP - SectopRAT C2 Endpoint

34.159.180[.]55 - IP - SectopRAT C2 Endpoint

35.198.132[.]51 - IP - SectopRAT C2 Endpoint

35.226.102[.]12 - IP - SectopRAT C2 Endpoint

35.234.79[.]173 - IP - SectopRAT C2 Endpoint

35.234.159[.]213 - IP - SectopRAT C2 Endpoint

35.242.150[.]95 - IP - SectopRAT C2 Endpoint

88.218.170[.]169 - IP - SectopRAT C2 Endpoint

162.55.188[.]246 - IP - SectopRAT C2 Endpoint

167.235.134[.]14 - IP - SectopRAT C2 Endpoint

MITRE ATT&CK Mapping

Model: Compliance / Pastebin

ID: T1537

Tactic: EXFILTRATION

Technique Name: Transfer Data to Cloud Account

Model: Anomalous Connection / Multiple Failed Connections to Rare Endpoint

ID: T1090.002

Sub technique of: T1090

Tactic: COMMAND AND CONTROL

Technique Name: External Proxy

ID: T1095

Tactic: COMMAND AND CONTROL

Technique Name: Non-Application Layer Protocol

ID: T1571

Tactic: COMMAND AND CONTROL

Technique Name: Non-Standard Port

Model: Compromise / Large Number of Suspicious Failed Connections

ID: T1571

Tactic: COMMAND AND CONTROL

Technique Name: Non-Standard Port

ID: T1583.006

Sub technique of: T1583

Tactic: RESOURCE DEVELOPMENT

Technique Name: Web Services

Model: Anomalous Connection / Multiple Connections to New External TCP Port

ID: T1095        

Tactic: COMMAND AND CONTROL    

Technique Name: Non-Application Layer Protocol

ID: T1571

Tactic: COMMAND AND CONTROL    

Technique Name: Non-Standard Port

References

1.     https://www.techtarget.com/searchsecurity/definition/RAT-remote-access-Trojan

2.     https://malpedia.caad.fkie.fraunhofer.de/details/win.sectop_rat

3.     https://threatfox.abuse.ch/browse/malware/win.sectop_rat

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
Justin Torres
Cyber Analyst

More in this series

No items found.

Blog

/

/

April 24, 2025

The Importance of NDR in Resilient XDR

picture of hands typing on laptop Default blog imageDefault blog image

As threat actors become more adept at targeting and disabling EDR agents, relying solely on endpoint detection leaves critical blind spots.

Network detection and response (NDR) offers the visibility and resilience needed to catch what EDR can’t especially in environments with unmanaged devices or advanced threats that evade local controls.

This blog explores how threat actors can disable or bypass EDR-based XDR solutions and demonstrates how Darktrace’s approach to NDR closes the resulting security gaps with Self-Learning AI that enables autonomous, real-time detection and response.

Threat actors see local security agents as targets

Recent research by security firms has highlighted ‘EDR killers’: tools that deliberately target EDR agents to disable or damage them. These include the known malicious tool EDRKillShifter, the open source EDRSilencer, EDRSandblast and variants of Terminator, and even the legitimate business application HRSword.

The attack surface of any endpoint agent is inevitably large, whether the software is challenged directly, by contesting its local visibility and access mechanisms, or by targeting the Operating System it relies upon. Additionally, threat actors can readily access and analyze EDR tools, and due to their uniformity across environments an exploit proven in a lab setting will likely succeed elsewhere.

Sophos have performed deep research into the EDRShiftKiller tool, which ESET have separately shown became accessible to multiple threat actor groups. Cisco Talos have reported via TheRegister observing significant success rates when an EDR kill was attempted by ransomware actors.

With the local EDR agent silently disabled or evaded, how will the threat be discovered?

What are the limitations of relying solely on EDR?

Cyber attackers will inevitably break through boundary defences, through innovation or trickery or exploiting zero-days. Preventive measures can reduce but not completely stop this. The attackers will always then want to expand beyond their initial access point to achieve persistence and discover and reach high value targets within the business. This is the primary domain of network activity monitoring and NDR, which includes responsibility for securing the many devices that cannot run endpoint agents.

In the insights from a CISA Red Team assessment of a US CNI organization, the Red Team was able to maintain access over the course of months and achieve their target outcomes. The top lesson learned in the report was:

“The assessed organization had insufficient technical controls to prevent and detect malicious activity. The organization relied too heavily on host-based endpoint detection and response (EDR) solutions and did not implement sufficient network layer protections.”

This proves that partial, isolated viewpoints are not sufficient to track and analyze what is fundamentally a connected problem – and without the added visibility and detection capabilities of NDR, any downstream SIEM or MDR services also still have nothing to work with.

Why is network detection & response (NDR) critical?

An effective NDR finds threats that disable or can’t be seen by local security agents and generally operates out-of-band, acquiring data from infrastructure such as traffic mirroring from physical or virtual switches. This means that the security system is extremely inaccessible to a threat actor at any stage.

An advanced NDR such as Darktrace / NETWORK is fully capable of detecting even high-end novel and unknown threats.

Detecting exploitation of Ivanti CS/PS with Darktrace / NETWORK

On January 9th 2025, two new vulnerabilities were disclosed in Ivanti Connect Secure and Policy Secure appliances that were under malicious exploitation. Perimeter devices, like Ivanti VPNs, are designed to keep threat actors out of a network, so it's quite serious when these devices are vulnerable.

An NDR solution is critical because it provides network-wide visibility for detecting lateral movement and threats that an EDR might miss, such as identifying command and control sessions (C2) and data exfiltration, even when hidden within encrypted traffic and which an EDR alone may not detect.

Darktrace initially detected suspicious activity connected with the exploitation of CVE-2025-0282 on December 29, 2024 – 11 days before the public disclosure of the vulnerability, this early detection highlights the benefits of an anomaly-based network detection method.

Throughout the campaign and based on the network telemetry available to Darktrace, a wide range of malicious activities were identified, including the malicious use of administrative credentials, the download of suspicious files, and network scanning in the cases investigated.

Darktrace / NETWORK’s autonomous response capabilities played a critical role in containment by autonomously blocking suspicious connections and enforcing normal behavior patterns. At the same time, Darktrace Cyber AI Analyst™ automatically investigated and correlated the anomalous activity into cohesive incidents, revealing the full scope of the compromise.

This case highlights the importance of real-time, AI-driven network monitoring to detect and disrupt stealthy post-exploitation techniques targeting unmanaged or unprotected systems.

Unlocking adaptive protection for evolving cyber risks

Darktrace / NETWORK uses unique AI engines that learn what is normal behavior for an organization’s entire network, continuously analyzing, mapping and modeling every connection to create a full picture of your devices, identities, connections, and potential attack paths.

With its ability to uncover previously unknown threats as well as detect known threats using signatures and threat intelligence, Darktrace is an essential layer of the security stack. Darktrace has helped secure customers against attacks including 2024 threat actor campaigns against Fortinet’s FortiManager , Palo Alto firewall devices, and more.  

Stay tuned for part II of this series which dives deeper into the differences between NDR types.

Credit to Nathaniel Jones VP, Security & AI Strategy, FCISO & Ashanka Iddya, Senior Director of Product Marketing for their contribution to this blog.

Continue reading
About the author
Nathaniel Jones
VP, Security & AI Strategy, Field CISO

Blog

/

/

April 22, 2025

Obfuscation Overdrive: Next-Gen Cryptojacking with Layers

man looking at multiple computer screensDefault blog imageDefault blog image

Out of all the services honeypotted by Darktrace, Docker is the most commonly attacked, with new strains of malware emerging daily. This blog will analyze a novel malware campaign with a unique obfuscation technique and a new cryptojacking technique.

What is obfuscation?

Obfuscation is a common technique employed by threat actors to prevent signature-based detection of their code, and to make analysis more difficult. This novel campaign uses an interesting technique of obfuscating its payload.

Docker image analysis

The attack begins with a request to launch a container from Docker Hub, specifically the kazutod/tene:ten image. Using Docker Hub’s layer viewer, an analyst can quickly identify what the container is designed to do. In this case, the container is designed to run the ten.py script which is built into itself.

 Docker Hub Image Layers, referencing the script ten.py.
Figure 1: Docker Hub Image Layers, referencing the script ten.py.

To gain more information on the Python file, Docker’s built in tooling can be used to download the image (docker pull kazutod/tene:ten) and then save it into a format that is easier to work with (docker image save kazutod/tene:ten -o tene.tar). It can then be extracted as a regular tar file for further investigation.

Extraction of the resulting tar file.
Figure 2: Extraction of the resulting tar file.

The Docker image uses the OCI format, which is a little different to a regular file system. Instead of having a static folder of files, the image consists of layers. Indeed, when running the file command over the sha256 directory, each layer is shown as a tar file, along with a JSON metadata file.

Output of the file command over the sha256 directory.
Figure 3: Output of the file command over the sha256 directory.

As the detailed layers are not necessary for analysis, a single command can be used to extract all of them into a single directory, recreating what the container file system would look like:

find blobs/sha256 -type f -exec sh -c 'file "{}" | grep -q "tar archive" && tar -xf "{}" -C root_dir' \;

Result of running the command above.
Figure 4: Result of running the command above.

The find command can then be used to quickly locate where the ten.py script is.

find root_dir -name ten.py

root_dir/app/ten.py

Details of the above ten.py script.
Figure 5: Details of the above ten.py script.

This may look complicated at first glance, however after breaking it down, it is fairly simple. The script defines a lambda function (effectively a variable that contains executable code) and runs zlib decompress on the output of base64 decode, which is run on the reversed input. The script then runs the lambda function with an input of the base64 string, and then passes it to exec, which runs the decoded string as Python code.

To help illustrate this, the code can be cleaned up to this simplified function:

def decode(input):
   reversed = input[::-1]

   decoded = base64.decode(reversed)
   decompressed = zlib.decompress(decoded)
   return decompressed

decoded_string = decode(the_big_text_blob)
exec(decoded_string) # run the decoded string

This can then be set up as a recipe in Cyberchef, an online tool for data manipulation, to decode it.

Use of Cyberchef to decode the ten.py script.
Figure 6: Use of Cyberchef to decode the ten.py script.

The decoded payload calls the decode function again and puts the output into exec. Copy and pasting the new payload into the input shows that it does this another time. Instead of copy-pasting the output into the input all day, a quick script can be used to decode this.

The script below uses the decode function from earlier in order to decode the base64 data and then uses some simple string manipulation to get to the next payload. The script will run this over and over until something interesting happens.

# Decode the initial base64

decoded = decode(initial)
# Remove the first 11 characters and last 3

# so we just have the next base64 string

clamped = decoded[11:-3]

for i in range(1, 100):
   # Decode the new payload

   decoded = decode(clamped)
   # Print it with the current step so we

   # can see what’s going on

   print(f"Step {i}")

   print(decoded)
   # Fetch the next base64 string from the

   # output, so the next loop iteration will

   # decode it

   clamped = decoded[11:-3]

Result of the 63rd iteration of this script.
Figure 7: Result of the 63rd iteration of this script.

After 63 iterations, the script returns actual code, accompanied by an error from the decode function as a stopping condition was never defined. It not clear what the attacker’s motive to perform so many layers of obfuscation was, as one round of obfuscation versus several likely would not make any meaningful difference to bypassing signature analysis. It’s possible this is an attempt to stop analysts or other hackers from reverse engineering the code. However,  it took a matter of minutes to thwart their efforts.

Cryptojacking 2.0?

Cleaned up version of the de-obfuscated code.
Figure 8: Cleaned up version of the de-obfuscated code.

The cleaned up code indicates that the malware attempts to set up a connection to teneo[.]pro, which appears to belong to a Web3 startup company.

Teneo appears to be a legitimate company, with Crunchbase reporting that they have raised USD 3 million as part of their seed round [1]. Their service allows users to join a decentralized network, to “make sure their data benefits you” [2]. Practically, their node functions as a distributed social media scraper. In exchange for doing so, users are rewarded with “Teneo Points”, which are a private crypto token.

The malware script simply connects to the websocket and sends keep-alive pings in order to gain more points from Teneo and does not do any actual scraping. Based on the website, most of the rewards are gated behind the number of heartbeats performed, which is likely why this works [2].

Checking out the attacker’s dockerhub profile, this sort of attack seems to be their modus operandi. The most recent container runs an instance of the nexus network client, which is a project to perform distributed zero-knowledge compute tasks in exchange for cryptocurrency.

Typically, traditional cryptojacking attacks rely on using XMRig to directly mine cryptocurrency, however as XMRig is highly detected, attackers are shifting to alternative methods of generating crypto. Whether this is more profitable remains to be seen. There is not currently an easy way to determine the earnings of the attackers due to the more “closed” nature of the private tokens. Translating a user ID to a wallet address does not appear to be possible, and there is limited public information about the tokens themselves. For example, the Teneo token is listed as “preview only” on CoinGecko, with no price information available.

Conclusion

This blog explores an example of Python obfuscation and how to unravel it. Obfuscation remains a ubiquitous technique employed by the majority of malware to aid in detection/defense evasion and being able to de-obfuscate code is an important skill for analysts to possess.

We have also seen this new avenue of cryptominers being deployed, demonstrating that attackers’ techniques are still evolving - even tried and tested fields. The illegitimate use of legitimate tools to obtain rewards is an increasingly common vector. For example,  as has been previously documented, 9hits has been used maliciously to earn rewards for the attack in a similar fashion.

Docker remains a highly targeted service, and system administrators need to take steps to ensure it is secure. In general, Docker should never be exposed to the wider internet unless absolutely necessary, and if it is necessary both authentication and firewalling should be employed to ensure only authorized users are able to access the service. Attacks happen every minute, and even leaving the service open for a short period of time may result in a serious compromise.

References

1. https://www.crunchbase.com/funding_round/teneo-protocol-seed--a8ff2ad4

2. https://teneo.pro/

Continue reading
About the author
Nate Bill
Threat Researcher
Your data. Our AI.
Elevate your network security with Darktrace AI