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June 2, 2019

How Cyberseer Detected Advanced Red Team Activity

This guest-authored blog post examines how Cyberseer detected highly advanced red team activities with Darktrace’s Enterprise Immune System.
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
Michael Green
Lead Security Analyst at Cyberseer (Guest Contributor)
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02
Jun 2019

The following guest-authored blog post examines how Cyberseer detected highly advanced red team activities with Darktrace’s Enterprise Immune System.

At Cyberseer, a managed security provider, our analysts know that thwarting sophisticated cyber-criminals requires being prepared for any eventuality. A red team attack today could easily be replicated by far less benign actors tomorrow, which is why we treat these exercises with the same gravity we would a genuine threat, employing the world’s most advanced AI cyber defenses like Darktrace to leave the bad guys without anywhere to hide.

Recently, one of our customers was involved in a red team assessment, partly as a means to see how their security team would react and contain the attack, and partly to determine the visibility of the different attack techniques across their security stack. During the engagement, the red team leveraged a number of stealthy “Living off the Land” (LotL) techniques. LotL refers to the malicious use of legitimate tools present on a system — such as PowerShell scripting, WMI, or PsExec — in order to execute attacks. It should be noted that these techniques are not just limited to red teamers: threat-actors are making use of such tools on compromised systems, a notable example being the 2017 Petya/NotPetya attack.

Here’s an example of how Cyberseer’s analysts used Darktrace to detect the red team, without prior knowledge of their techniques, in real time:

Invoke — Bloodhound

Created by professional penetration tester Andy Robbins, Bloodhound is an open source tool that uses graph theory to understand the relationships in an Active Directory (AD) environment. It can be harnessed to quickly gain deep insights into AD by enumerating all the computers for which a given user has admin rights, in addition to ascertaining group membership information. In the right hands, security teams can use Bloodhound to identify and then limit attack vectors. In the wrong hands, attackers can easily exploit these same pathways if left unaddressed.

To collect data, Bloodhound is complemented by a data ingestor called Sharphound, which comes either as a PowerShell script or an executable. Sharphound makes use of native Windows APIs to query and retrieve information from target hosts. For example, to enumerate Local Admin users, it calls ‘NetLocalGroupGetMember’ API to interact with the Security Account Manager (SAM) database file on the remote host.

These tools typically produce a number of artifacts that we would expect to see from the host device within network traffic:

  • Increase in connections to LDAP (389) and SMB (445) ports
  • Increase in connections to IPC$ shares
  • Increase in Distributed Computing Environment / Remote Procedure Calls (DCE_RPC) Connections to the following named pipes:
  • \PIPE\wkssvc - Query logged-in users
  • \PIPE\srvsvc - Query system information
  • \PIPE\svcctl - Query services with stored credentials
  • \PIPE\atsvc - Query scheduled tasks
  • \PIPE\samr - Enumerate domain and user information
  • \PIPE\lsass - Extract credential information

Associating this back to the red team engagement, upon execution of the Bloodhound tool the attacking device began reaching out to a large number of internal devices, causing a spike in internal connections:

Figure 1: Darktrace visualizing the increase in internal connections, with each dot representing a unique model breach triggered by Bloodhound activity.

In fact, the large volume of anomalous connections triggered a number of Darktrace’s behavioral models, including:

  • Anomalous Connection / SMB Enumeration
  • Anomalous Connection / New Service Control
  • Device / Network Scan
  • Device / Expanded Network Scan
  • Unusual Activity / Unusual Activity from Multiple Metrics
  • Unusual Activity / Sustained Suspicious Activity
  • Unusual Activity / Sustained Unusual Activity

Drilling deeper into these connections, it was possible to identify the named \PIPE\ connections that were detailed above:

Figure 2: Reviewing the raw connection logs within Darktrace’s Advanced Search.

Looking from top to bottom, we see scanning of devices on ports 139 and 445, access to remote IPC$ shares, SMB read / writes of the srvsvc, and samr pipes and lsass binds. Although these protocols have legitimate applications within a typical network, a device initiating so many of them within a short time frame warrants further investigation.

Darktrace AI not only shined a light on these activities, it automatically determined that they were potentially threatening despite being benign under most circumstances. Rooted in an ever-evolving understanding of our customer’s normal ‘pattern of life’, Darktrace correlated numerous weak indicators of anomalous behavior to flag the activity as a significant risk within seconds.

Invoke — PasswordSpray

“Password spraying” is an attack that targets a large number of accounts with a few commonly used passwords. In this case, for instance, the red team attempted to brute-force access to a file share. Although this tactic may seem rudimentary, a recent study by the NCSC found that 75% of organizations had accounts with passwords that featured in the top 1,000 passwords, while 87% had accounts with passwords that featured in the top 10,000.

Similar to the previous Bloodhound attack, the password spraying attack began with an increase in SMB connections on port 445. Darktrace alerted to even this relatively small number of connections, since it was anomalous for our customer’s unique network:

Figure 3: Volume of SMB session failures made to file shares from the attacker’s device.

Each of these connections was making use of a user credential and random password. From the logs below it is possible to see all of the SMB session failures:

Figure 4: A device event log showing repeated SMB session failures for each of the unsuccessful authentication attempts.

Even with only 50 total attempts seen, Darktrace quickly alerted upon both SMB enumeration and brute-force behaviors.

Both of these scenarios highlight the benefits of an AI-powered approach. Rather than focusing on hash or string matches for such tools, Darktrace is able to quickly identify anomalous patterns of behavior linked with their usage. This nuance is particularly critical in this case, given that all of these activities are not malicious in many situations. By differentiating between subtle threats and harmless traffic, Darktrace helps us defeat red teams and real criminals alike.

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
Michael Green
Lead Security Analyst at Cyberseer (Guest Contributor)

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April 24, 2025

The Importance of NDR in Resilient XDR

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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.

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

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April 22, 2025

Obfuscation Overdrive: Next-Gen Cryptojacking with Layers

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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/

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About the author
Nate Bill
Threat Researcher
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