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May 14, 2019

[Part 1] 10 Cyber Hygiene Issues Leading to a Security Breach

Spotting cyber hygiene issues caused by a lapse of attention requires AI tools that alert critical changes to network activity. Read part one here!
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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14
May 2019

For as long as people have sought to protect their assets from intrusion, they have safeguarded those assets behind ever more formidable walls, from castle walls made of stone to firewalls comprised of code. Yet no matter how impenetrable such fortifications appear, motivated attackers will inevitably find a way to bypass them. Build a 50-foot fence, and the enemy will bring a 50-foot ladder. Install state-of-the-art endpoint security on every employee’s computer, and cyber-criminals will infiltrate via the smart refrigerator in the office kitchen.

Needless to say, reinforcing the perimeter is still a good idea. Just as a castle in ruins makes a poor home for a king, so too do weak endpoint defenses put intellectual property and sensitive data at risk. The reality, however, is that digital environments are exponentially more difficult to wall off than physical ones, given the sheer number of applications and users that can compromise an entire network with just a single vulnerability or oversight. Improving a company’s cyber hygiene is therefore a continual responsibility, the nature of which perpetually changes as the business evolves.

Because even flawless cyber hygiene isn’t guaranteed to keep external attackers — let alone malicious insiders — from breaching the perimeter, leading companies and governments have turned to cyber AI technologies. Cyber AI works by learning the particular behaviors of a network and its users, allowing it to pick up on the subtly anomalous activity associated with an already infected device. Such technologies have shined a light on ten of the most commonly exploited cyber hygiene issues, five of which are examined below. And whereas there is no silver bullet when it comes to securing the enterprise online, patching these holes in the perimeter is nevertheless a critical first step.

Issue #1: Using SMBv1 — for anything

Server Message Block (SMB) is a very common application layer protocol that provides shared access to files, printers, and serial ports to devices in a network. The latest version, SMBv3, was developed with security in mind, whereas the original version, SMBv1, is more than three decades old and — in Microsoft’s own words — “was designed for a world that no longer exists[;] a world without malicious actors.” As a result, Microsoft has long implored users to stop using it in the strongest possible terms.

However, many of these users still have not disabled the protocol on operating systems older than Windows 8.1 and Windows Server 2012 R2, which do not allow SMB1 to be removed. The 2017 WannaCry ransomware attack abused the famous exploit EternalBlue in SMBv1 to infect Windows machines and move laterally in Windows environments, precipitating billions of dollars in global losses. Furthermore, SMBv1 allows NTLM logins using the anonymous credential by default, while successful anonymous logins can allow attackers to enumerate the target device for more information.

In light of the serious security risks that SMBv1 introduces, Darktrace flags its usage as threatening with the following models:

  • Anomalous Connection / Unusual SMB Version 1 Connectivity
  • Compliance / SMB Version 1 Usage

Issue #2: SMB services exposed to the internet

As mentioned above, SMB allows devices in a network to communicate with one another for a variety of purposes — functionalities that render it a complex protocol with many known vulnerabilities. Users are consequently highly discouraged from allowing connections from the internet to internal devices via any version of SMB — not just SMBv1.

Darktrace detected this poor hygiene practice in early 2019, when it observed the use of SMB from external IP addresses connecting to an internal device. The device happened to be a Domain Controller (DC), a server which manages network security and is responsible for user authentication. Due to the critical network function performed by this server, it is a high value target for cyber-criminals, meaning that any external connections should be limited to only essential administrative activity. In this incident, the external device was seen accessing the DC via SMBv1 and performing anonymous login. Fortunately, Darktrace AI detected the potential compromise with the model Compliance / External Windows Communications.

Issue #3: RDP services exposed to the internet

Microsoft’s proprietary Remote Desktop Protocol (RDP) provides a remote connection to a network-connected computer, affording users significant control over another device and its resources. Such extensive capabilities represent the holy grail for attackers, whether they seek to gain an initial foothold in the network, access restricted content, or directly drop malware on the controlled computer. Exposing devices with RDP services to the internet therefore creates a significant vulnerability in the network perimeter, as passwords and user credentials are liable to be brute-forced by those with malign intent.

Last month, Darktrace’s cyber AI detected a large number of incoming connections over the RDP protocol to a customer’s internet-facing device — possible indicators of a brute-force attack. While this activity might have been benign under different circumstances, the AI’s understanding of ‘self’ versus ‘not self’ for the particular device in question enabled it to flag the connections as anomalous, since they breached its Compliance / Incoming RDP from Rare Endpoints model.

By investigating further with Darktrace’s device tracking capability, we can see that the computer also breached several other AI models, including Compliance / Crypto Currency Mining Activity, Compliance / Outbound RDP, and Compromise / Beaconing Activity to External Rare. These breaches suggest that the attackers might have sought to use the computer to plant crypto-mining modules on other network-connected devices.

Models that the device breached within three days

Issue #4: Data uploads to unapproved cloud services

No innovation has antiquated the perimeter-only approach to cyber security more than cloud computing, since cloud and hybrid infrastructures have nebulous borders at best. Nevertheless, there are a number of bad cyber hygiene habits that make bypassing perimeter defenses much easier, including employees who upload data to close storage providers that are not on an organization’s approved list. Whether done maliciously or inadvertently, this decision prevents organizations from gaining any visibility over that data being transferred across the globe.

Darktrace cyber AI detects such unauthorized data movements with the following models:

  • Anomalous Connection / Data Sent To New External Device
  • Unusual Activity / Unusual External Data Transfer

Issue #5: Weak password usage and storage

Among the most common and most avoidable cyber-attacks are those that exploit systems with weak passwords, which can be breached by brute-force or dictionary attacks. Yet stronger, more complex passwords introduce a separate problem: because they are harder to be remember, users tend to store these passwords in sometimes unsafe locations. Whereas passwords housed in encrypted mediums such as password managers are relatively secure, many users instead save them in cleartext. Several modern strains of malware possess the ability to comb through the network in search of possible files which contains passwords, rendering this a critical vulnerability.

Darktrace has a set of models to spot such attempts at password guessing:

  • Device / SMB Session Bruteforce
  • Unusual Activity / Large Volume of Kerberos Failures
  • User / Kerberos Password Bruteforce
  • SaaS / Login Bruteforce Attempt

Darktrace also has a set of models that flag anomalous password storage or access:

  • Compliance / Sensitive Terms in Unusual SMB Connection
  • Compliance / Possible Unencrypted Password Storage
  • SaaS / Unusual SaaS Sensitive File Access

Read the second part: Part two — The perils of convenience

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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