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January 30, 2023

How Vidar Malware Spreads via Malvertising on Google

Discover how Vidar info stealer malware is distributed through malvertising on Google and the risks it poses to users and organizations.
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
Roberto Martinez
Devalyst, Threat Researcher
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30
Jan 2023

In recent weeks, security researchers and cyber security vendors have noted an increase in malvertising campaigns on Google, aimed at infiltrating info-stealer malware into the systems of unsuspecting victims, as reported in sources [1] [2]. It has been observed that when individuals search for popular tools such as Notepad++, Zoom, AnyDesk, Foxit, Photoshop, and others on Google, they may encounter ads that redirect them to malicious sites. This report aims to provide a high-level analysis of one such campaign, specifically focusing on the delivery of the Vidar Info-stealer malware.

Campaign Details

On the 25th of January 2023, Darktrace researchers observed that the advertisement depicted in Figure 1 was being displayed on Google when searching for the term "Notepad++" from within the United States.

Figure 1: Google Ad shown when searching for Notepad++

As can be seen in Figure 2, the advertisement in question had no visible information regarding its publisher.

Figure 2: Advertisement information

Clicking on the advertisement would direct potential victims to the website notepadplusplus.site, which had been registered on the 4th of January and is hosted on IP address 37.140.192.11. Upon selecting the desired version of the software, a download button is presented to the visitor.

Figure 3: Malicious site with fake Notepad++
Figure 4: Malicious site with fake Notepad++

When clicking on Download, regardless of the version selected, the traffic is then redirected to https://download-notepad-plus-plus.duckdns.org/, and a .zip file with name “npp.Installer.x64.zip” is downloaded.

Figure 5: Traffic redirection

Upon extraction, the file "npp.Installer.x64.exe" has a file size of 684.1 megabytes. The significant size is attributed to the inclusion of an excessive number of null bytes, which serve to prevent the file from being scanned by some Antivirus and uploaded to malware analysis platforms such as VirusTotal, which has a file size limit of 650 megabytes.

Figure 6: npp.Installer.x64.zip

Initially, padding was incorporated at the end of the executable, enabling individuals to remove it while maintaining a fully functional file. However, in the sample analysed in this report, padding was inserted into the binary's central region. This method renders the removal of padding more challenging, as simply deleting the zeroes would compromise the integrity of the file and impede its functionality during dynamic analysis.

Figure 7: Beginning of null bytes padding

Figure 8: End of null bytes padding

After execution, the malware promptly establishes a connection to a Telegram channel to acquire its command and control (C2) address, specifically http://95.217.16.127. If Telegram is not available, the malware will then attempt to connect to a profile on video game platform Steam, in which case the C2 address was http://157.90.148.112/ at the time of initial analysis and http://116.203.6.107 later. It then proceeds to check-in and obtain its configuration file and subsequently downloads get.zip, an archive containing several legitimate DLL libraries, which are utilized to extract information and saved passwords from various applications and browsers. Through traffic analysis, the method by which the malware obtains its Command and Control (C2) location, and analysis of the configuration obtained, it can be assessed with high confidence that the malware in question is the info-stealer known as Vidar. Vidar has been extensively covered by various cybersecurity organizations. Further information regarding this info-stealer and its origins can be found here[3].

Figure 9: Telegram traffic
Figure 10: Telegram channel containing the location of Vidar’s C2 address
Figure 11: Steam profile containing the location of Vidar’s C2 address
Figure 12: Vidar C2 traffic
Figure 13: Vidar configuration obtained from the C2
Figure 14: Libraries downloaded by Vidar

Campaign ID 827

The domain download-notepad-plus-plus.duckdns.org, from which the malware is distributed, resolves to the IP address 185.163.204.10. Using passive DNS, it has been determined that multiple domains also resolve to this IP address. This information suggests that the threat group responsible for this campaign is also utilizing advertising to target individuals searching for specific applications besides Notepad++, including:

  • OBS Studio
  • Davinci Resolve
  • Sqlite
  • Rufus
  • Krita

Furthermore, it has been observed that all the malware samples obtained in this investigation connect to the same Telegram channel, utilize the same two Command and Control IP addresses, and share the same campaign ID of "827".

Conclusion 

The recent proliferation of malvertising campaigns, which are employed by cyber-criminals to distribute malware, has become a significant cause for concern. Unlike more traditional infection vectors, such as email, malvertising is harder to protect against. Furthermore, the use of padding techniques to inflate the size of malware payloads can make detection and analysis more challenging.

To mitigate the risk of falling victim to such attacks, it is recommended to exercise caution when interacting with online advertisements. Specifically, it is advisable to avoid clicking on any advertisements while searching for free software on search engines and to instead download programs directly from official sources. This approach can reduce the likelihood of inadvertently downloading malware from untrusted sources. 

Another effective measure to counteract the threat of malicious ads is the utilization of ad-blocker software. The implementation of an ad-blocker can provide an additional layer of protection against malvertising campaigns and enhance overall cybersecurity.

Appendices

Indicators of Compromise

Filename        npp.Installer.x64.zip

SHA256 Hash  7DFD1D4FE925F802513FEA5556DE53706D9D8172BFA207D0F8AAB3CEF46424E8

Filename         npp.Installer.x64.exe

SHA256 Hash  368008b450397c837f0b9c260093935c5cef56646e16a375ba7c47fea5562bfd

Filename         rufus-3.21.zip

SHA256 Hash  75db4f8187abf49376a6ff3de0163b2d708d72948ea4b3d5645b86a0e41af084

Filename         rufus-3.21.exe

SHA256 Hash  169603a5b5d23dc2f02dc0f88a73dcdd08a5c62d12203fb53a3f43998c04bb41

Filename         DaVinci_Resolve_18.1.2_Windows.zip

SHA256 Hash  73f00e3b3ab01f4d5de42790f9ab12474114abe10cd5104f623aef9029c15b1e

Filename         DaVinci_Resolve_18.1.2_Windows.exe

SHA256 Hash  169603a5b5d23dc2f02dc0f88a73dcdd08a5c62d12203fb53a3f43998c04bb41

Filename         krita-x64-5.1.5-setup.zip

SHA256 Hash  85eb4b0e3922312d88ca046d89909fba078943aea3b469d82655a253e0d3ac67

Filename         krita-x64-5.1.5-setup.exe

SHA256 Hash  169603a5b5d23dc2f02dc0f88a73dcdd08a5c62d12203fb53a3f43998c04bb41

URL      http://95.217.16.127/827

URL      http://95.217.16.127/get.zip

URL      http://95.217.16.127/

URL      http://157.90.148.112/827

URL     http://157.90.148.112/

URL      http://157.90.148.112/get.zip

URL      http://116.203.6.107/

Domain           notepadplusplus.site

Domain           download-notepad-plus-plus.duckdns.org

Domain           download-obsstudio.duckdns.org

Domain           dowbload-notepadd.duckdns.org

Domain           dowbload-notepad1.duckdns.org

Domain           download-davinci-resolve.duckdns.org

Domain           download-davinci.duckdns.org

Domain           download-sqlite.duckdns.org

Domain           download-davinci17.duckdns.org

Domain           download-rufus.duckdns.org

Domain           download-kritapaint.duckdns.org

IP Address      37.140.192.11

IP Address      185.163.204.10

IP Address      95.217.16.127

IP Address       157.90.148.112

IP Address      116.203.6.107

URL      https://t.me/litlebey

URL      https://steamcommunity.com/profiles/76561199472399815

References

[1] https://www.bleepingcomputer.com/news/security/hackers-push-malware-via-google-search-ads-for-vlc-7-zip-ccleaner/

[2] https://www.bleepingcomputer.com/news/security/ransomware-access-brokers-use-google-ads-to-breach-your-network/

[3] https://www.team-cymru.com/post/darth-vidar-the-dark-side-of-evolving-threat-infrastructure

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
Roberto Martinez
Devalyst, Threat Researcher

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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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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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