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April 15, 2021

AI Neutralizes Hafnium Cyber Attack in December 2020

Protect your business from cyber attacks with AI technology. Learn how Darktrace neutralized the Hafnium attack against Exchange servers in December 2020.
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
Max Heinemeyer
Global Field CISO
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15
Apr 2021

In early December 2020, Darktrace AI autonomously detected and investigated a sophisticated cyber-attack that targeted a customer’s Exchange server. On March 2, 2021, Microsoft disclosed an ongoing campaign by the Hafnium threat actor group leveraging Exchange server zero-days.

Based on similarities in techniques, tools and procedures (TTPs) observed, Darktrace has now assessed with high confidence that the attack in December was the work of the Hafnium group. Although it is not possible to determine whether this attack leveraged the same Exchange zero-days as reported by Microsoft, the finding suggests that Hafnium’s campaign was active several months earlier than assumed.

As a result, organizations may want to go back as far as early December 2020 to check security logs and tools for signs of initial intrusion into their Internet-facing Exchange servers.

As Darktrace does not rely on rules or signatures, it doesn’t require a constant cloud connection. Most customers therefore operate our technology themselves, and we don’t centrally monitor their detections.

At the time of detection in December, this was one of many uncategorized, sophisticated intrusions that affected only a single customer, and was not indicative of a broader campaign.

This means that while we protect our customers from individual intrusions, we are not in a position to do global campaign tracking like other companies which focus primarily on threat intelligence and threat actor tracking.

In this blog, we will analyze the attack to aid organizations in their ongoing investigations, and to raise awareness that the Hafnium campaign may have been active for longer than previously disclosed.

Overview of the Exchange attack

The intrusion was detected at an organization in the critical national infrastructure sector in South Asia. One hypothesis is that the Hafnium group was testing out and refining its TTPs, potentially including the Exchange server exploit, before running a broad-scale campaign against Western organizations in early 2021.

The threat actor used many of the same techniques that were observed in the later Hafnium attacks, including the deployment of the low-activity China Chopper web shell, quickly followed by post-exploitation activity – attempting to move laterally and spread to critical devices in the network.

The following analysis demonstrates how Darktrace’s Enterprise Immune System detected the malicious activity, how Cyber AI Analyst automatically investigated on the incident and surfaced the alert as a top priority, and how Darktrace RESPOND (formerly known as 'Antigena') would have responded autonomously to shut down the attack, had it been in active mode.

All the activity took place in early December 2020, almost three months before Microsoft released information about the Hafnium campaign.

Figure 1: Timeline of the attack from early December 2020

Initial compromise

Unfortunately, the victim organization did not keep any logs or forensic artefacts from their Exchange server in December 2020, which would have allowed Darktrace to ascertain the exploit of the zero-day. However, there is circumstantial evidence suggesting that these Exchange server vulnerabilities were abused.

Darktrace observed no signs of compromise or change in behavior from the Internet-facing Exchange server – no prior internal admin connections, no broad-scale brute-force attempts, no account takeovers, no malware copied to the server via internal channels – until all of a sudden, it began to scan the internal network.

While this is not conclusive evidence that no other avenue of initial intrusion was present, the change in behavior on an administrative level points to a complete takeover of the Exchange server, rather than the compromise of a single Outlook Web Application account.

To conduct a network scan from an Exchange server, a highly privileged, operating SYSTEM-level account is required. The patch level of the Exchange server at the time of compromise appears to have been up-to-date, at least not offering a threat actor the ability to target a known vulnerability to instantly get SYSTEM-level privileges.

For this reason, Darktrace has inferred that the Exchange server zero-days that became public in early March 2021 were possibly being used in this attack observed in early December 2020.

Internal reconnaissance

As soon as the attackers gained access via the web shell, they used the Exchange server to scan all IPs in a single subnet on ports 80, 135, 445, 8080.

This particular Exchange server had never made such a large number of new failed internal connections to that specific subnet on those key ports. As a result, Darktrace instantly alerted on the anomalous behavior, which was indicative of a network scan.

Autonomous Response

Darktrace RESPOND was in passive mode in the environment, so was not able to take action. In active mode, it would have responded by enforcing the previously learned, normal ‘pattern of life’ of the Exchange server – allowing the server to continue normal business operations (sending and receiving emails) but preventing the network scan and any subsequent activity. These actions would have been carried out via various integrations with the customer’s existing security stack, including Firewalls and Network Access Controls.

Specifically, when the network scanning started, the ‘Antigena Network Scan Block’ was triggered. This means that for several hours, Darktrace RESPOND (Antigena) would have blocked any new outgoing connections from the Exchange server to the scanned subnet on port 80, 135, 445, or 8080, preventing the infected Exchange server from conducting network scanning.

As a result, the attackers would not have been able to conclude anything from their reconnaissance — all their scanning would have returned closed ports. At this point, they would need to stop their attack or resort to other means, likely triggering further detections and further Autonomous Response.

The network scan was the first step touching the internal network. This is therefore a clear case of how Darktrace RESPOND can intercept an attack in seconds, acting at the earliest possible evidence of the intrusion.

Lateral movement

Less than an hour after the internal network scan, the compromised Exchange server was observed writing further web shells to other Exchange servers via internal SMB. Darktrace alerted on this as the initially compromised Exchange server had never accessed the other Exchange servers in this fashion over SMB, let alone writing .aspx files to Program Files remotely.

A single click allowed the security team to pivot from the alert into Darktrace’s Advanced Search, revealing further details about the written files. The full file path for the newly deployed web shells was:

Program Files\Microsoft\Exchange Server\V15\FrontEnd\HttpProxy\owa\auth\Current\themes\errorFS.aspx

The attackers thus used internal SMB to compromise further Exchange servers and deploy more web shells, rather than using the Exchange zero-day exploit again to achieve the same goal. The reason for this is clear: exploits can often be unstable, and an adversary would not want to show their hand unnecessarily if it could be avoided.

While the China Chopper web shell has been deployed with many different names in the past, the file path and file name of the actual .aspx web shell bear very close resemblance to the Hafnium campaign details published by Microsoft and others in March 2021.

As threat actors often reuse naming conventions / TTPs in coherent campaigns, it again indicates that this particular attack was in some way part of the broader campaign observed in early 2021.

Further lateral movement

Minutes later, the attacker conducted further lateral movement by making more SMB drive writes to Domain Controllers. This time the attackers did not upload web shells, but malware, in the form of executables and Windows .bat files.

Darktrace alerted the security team as it was extremely unusual for the Exchange server and its peer group to make SMB drive writes to hidden shares to a Domain Controller, particularly using executables and batch files. The activity was presented to the team in the form of a high-confidence alert such as the anonymized example below.

Figure 2: Example graphic of Darktrace detecting unusual connectivity

The batch file was called ‘a.bat’. At this point, the security team could have created a packet capture for the a.bat file in Darktrace with the click of a button, inspecting the content and details of that script at the time of the intrusion.

Darktrace also listed the credentials involved in the activity, providing context into the compromised accounts. This allows an analyst to pivot rapidly around the data and further understand the scope of the intrusion.

Bird’s-eye perspective

In addition to detecting the malicious activity outlined above, Darktrace’s Cyber AI Analyst autonomously summarized the incident and reported on it, outlining the internal reconnaissance and lateral movement activity in a single, cohesive incident.

The organization has several thousand devices covered by Darktrace’s Enterprise Immune System. Nevertheless, over the period of one week, the Hafnium intrusion was in the top five incidents highlighted in Cyber AI Analyst. Even a small or resource-stretched security team, with only a few minutes available per week to review the highest-severity incidents, could have seen and inspected this threat.

Below is a graphic showing a similar Cyber AI Analyst incident created by Darktrace.

Figure 3: A Cyber AI Analyst report showing unusual SMB activity

How to stop a zero-day

Large scale campaigns which target Internet-facing infrastructure and leverage zero-day exploits will continue to occur regularly, and such attacks will always succeed in evading signature-based detection. However, organizations are not helpless against the next high-profile zero-day or supply chain attack.

Detecting the movements of attackers inside a system and responding to contain in-progress threats is possible before IoCs have been provided. The methods of detection outlined above protected the company against this attack in December, and the same techniques will continue to protect the company against unknown threats in the future.

Learn more about how Darktrace AI has stopped Hafnium cyber-attacks and similar threat actors

Darktrace model detections:

  • Device / New or Uncommon WMI Activity
  • Executable Uploaded to DC
  • Compliance / High Priority Compliance Model Breach
  • Compliance / SMB Drive Write
  • Antigena / Network / Insider Threat / Antigena Network Scan Block
  • Device / Network Scan - Low Anomaly Score
  • Unusual Activity / Unusual Internal Connections

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
Max Heinemeyer
Global Field CISO

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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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