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November 13, 2023

OracleIV: A dockerized DDoS botnet

OracleIV is a DDoS botnet exploiting misconfigured Docker Engine APIs. It delivers a malicious Python ELF executable within a Docker container ("oracleiv_latest") to perform various DoS attacks. The botnet communicates with a C2 server for commands, demonstrating attackers' continued use of exposed Docker instances.
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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13
Nov 2023

Introduction: OracleIV

Researchers from Cado Security Labs (now part of Darktrace) discovered a novel campaign targeting publicly exposed instances of the Docker Engine API.

Attackers are exploiting this misconfiguration to deliver a malicious Docker container, built from an image named "oracleiv_latest" and containing Python malware compiled as an ELF executable. The malware itself acts as a Distributed Denial of Service (DDoS) bot agent, capable of conducting Denial of Service (DoS) attacks via a number of methods.

It’s not the first time the Docker Engine API has been targeted by attackers. This method of initial access has been increasing in recent years and is often used to deliver cryptojacking malware [1]. Inadvertent exposure of the Docker Engine API occurs frequently enough that several unrelated campaigns have been observed scanning for it. 

This should come as no surprise, given the move to microservice-driven architectures by many software teams. Once a valid endpoint is discovered, it’s trivial to pull a malicious image and launch a container from it to carry out any conceivable objective. Hosting the malicious container in Docker Hub, Docker’s container image library, streamlines this process even further.

Initial access

In keeping with other attacks of this kind, initial access typically begins with a HTTP POST request to the /images/create endpoint of Docker’s API. This effectively runs a docker pull command on the host to retrieve the specified image from Docker Hub. A follow-up container start command is then used to spawn a container from the pulled image. 

An example of the image create command used in the OracleIV command can be seen below:

POST /v1.43/images/create?
tag=latest&fromImage=robbertignacio328832/oracleiv_latest 

Malicious Docker hub image

As can be seen in the Docker API command above, the attacker retrieves an image named oracleiv_latest which was uploaded to Docker Hub. This image was still live at the time of writing and had over 3,000 pulls. Furthermore, the image itself appeared to be undergoing regular iteration, with the most recent changes pushed only 3 days prior to the writing of this blog.

The user also added the description Mysql image for docker to the image’s Docker Hub page, likely to make it seem more innocuous.

Examining the image layers reveals commands used by the attacker to retrieve their malicious payload - named oracle.sh, despite being an ELF executable - and bake it into the resulting image.

Image layer RUN command to retrieve malicious payload
Figure 1: Image layer RUN command to retrieve malicious payload

The image also includes additional wget commands to retrieve a copy of XMRig and an associated miner configuration file.

Image layer RUN command to retrieve xmrig miner
Figure 2: Image layer RUN command to retrieve xmrig miner
Image layer RUN command to retrieve miner configuration file
Figure 3: Image layer RUN command to retrieve miner configuration file

It is worth noting that Cado researchers did not observe any mining performed by this malicious container, but with these files baked into the image it would certainly be possible.

Static analysis

Since the bundled version of XMRig is both unused and a vanilla release of the miner, this section will focus on analysis of the oracle.sh executable embedded in the malicious container.

Static analysis of this executable revealed a 64-bit, statically linked ELF, with debug information intact. Further investigation led to the discovery of a number of functions with CyFunction in the name, confirming that the malware is Python code compiled with Cython.

Embedded Cython functions
Figure 4: Embedded Cython functions

The attacker code is relatively concise, the majority of it is dedicated to the different DoS methods present. The following functions were identified:

  • bot.main
  • bot.init_socket
  • bot.checksum
  • bot.register_ssl
  • bot.register_httpget
  • bot.register_slow
  • bot.register_five
  • bot.register_vse
  • bot.register_udp
  • bot.register_udp_pps
  • bot.register_ovh

Functions with the register_ prefix correspond to DoS attack methods, the details of which will be discussed in the following section.

Dynamic analysis

The bot connects back to a Command-and-Control server (C2) at 46.166.185[.]231 on TCP port 40320. It then performs primitive authentication, where the bot supplies the C2 with basic information about its environment in addition to a hardcoded password.

 : client hello from zombie! : X86 : key: b'bjN0ZzM0cnAwd24zZA==' : os: linux

The key decodes to “n3tg34rp0wn3d”. Supplying an incorrect key causes the C2 to reply with a string of expletive language, followed by the connection being terminated.

Following successful authentication, the C2 will continuously send “routine ping, greetz Oracle IV”. This is likely due to an implementation quirk, where many novice programmers new to socket programming will implement the blocking receive operation in a loop and require constant input to keep the loop going.

Cado Security Labs has performed monitoring of the botnet activity and has observed the botnet being used to DDoS a number of targets, with the operator preferring to use a UDP based flood in addition to an SSL based flood.

Botnet commands

C2 commands used to initiate the different DoS attacks take the following form:

<attack type> <target IP/domain> <attack duration> <rate> <target port>

For example, to conduct an SSL DoS attack on the website example.com for 30 seconds, a rate of 30, and on port 80, the C2 server would send the following command:

ssl example.com 30 30 80

Cado Security Labs were able to trick a botnet agent into connecting to a mimic C2 server instead of the real one and issued commands to observe the capabilities of the botnet. The botnet has the following DDoS capabilities:

UDP:

  • Performs a UDP flood with 40,000-byte packets
  • These far exceed the threshold and consequently get fragmented. This will create an additional computational overhead on both the target and source due to the reassembly of fragments, however it is unclear if this is intentional.

UDP_PPS:

  • Seems non-functional, when the command was issued no activity was observed.

SSL:

  • Opens a TCP connection, sends a large amount of data, and then closes. This process then repeats. The Cado dummy target server rejected all the fake requests with an error 400, so it would appear that the attack aims at flooding the target rather than exploiting some protocol specific function.
Tcpdump output for SSL Dos method
Figure 5: Tcpdump output for SSL DoS method

SYN:

  • It was anticipated that this would be a SYN flood, however the observed behavior is identical to SSL.

HTTPGET:

  • Seems non-functional, when the command was issued no activity was observed.

SLOW:

  • This is a “slowloris” style attack. The agent opens up many connections to the server and continuously sends small amounts of data to keep the connection open.

FIVE:

  • This is a UDP flood with 18-byte packets. Likely the packets are a part of the FiveM server protocol, and designed to cause a denial of service a FiveM server

VSE:

  • This is a UDP flood with 20-byte packets. Similar to FIVE, this seems protocol specific to Valve source engine.

OVH:

  • This is a UDP flood with 8-byte packets, designed to circumvent OVH’s DDoS protection.

Conclusion

OracleIV demonstrates that attackers are still intent on leveraging misconfigured Docker Engine API deployments as a means of initial access for a variety of campaigns. The portability that containerization brings allows malicious payloads to be executed in a deterministic manner across Docker hosts, regardless of the configuration of the host itself. 

Whilst OracleIV is not technically a supply chain attack, users of Docker Hub should be aware that malicious container images do indeed exist in Docker’s image library. Cado researchers reported the malicious user behind OracleIV to Docker.

Despite this, users of Docker Hub are encouraged to perform periodic assessments of the images they are pulling from the registry, to ensure that they have not been polluted with malicious code. 

Consistent with other attacks reliant on a misconfigured internet-facing service (e.g. Jupyter, Redis etc), Cado researchers strongly urge users of these services to periodically review their exposure and implement network defenses accordingly.

Indicators of compromise (IoCs)

File name SHA256

oracle.sh (embedded in container) 5a76c55342173cbce7d1638caf29ff0cfa5a9b2253db9853e881b129fded59fb

xmrig (embedded in container) 20a0864cb7dac55c184bd86e45a6e0acbd4bb19aa29840b824d369de710b6152

config.json (embedded in container) 776c6ef3e9e74719948bdc15067f3ea77a0a1eb52319ca1678d871d280ab395c

IP addresses

46[.]166[.]185[.]231

Docker image

robbertignacio328832/oracleiv_latest:latest

References

  1. https://blog.aquasec.com/threat-alert-anatomy-of-silentbobs-cloud-attack
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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April 1, 2026

AI-powered security for a rapidly growing grocery enterprise

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Protecting a complex, fast-growing retail organization

For this multi-banner grocery holding organization, cybersecurity is considered an essential business enabler, protecting operations, growth, and customer trust. The organization’s lean IT team manages a highly distributed environment spanning corporate offices, 100+ stores, distribution centers and  thousands of endpoints, users, and third-party connections.

Mergers and acquisitions fueled rapid growth, but they also introduced escalating complexity that constrained visibility into users, endpoints, and security risks inherited across acquired environments.

Closing critical visibility gaps with limited resources

Enterprise-wide visibility is a top priority for the organization, says the  Vice President of Information Technology. “We needed insights beyond the perimeter into how users and devices were behaving across the organization.”

A security breach that occurred before the current IT leadership joined the company reinforced the urgency and elevated cybersecurity to an executive-level priority with a focus on protecting customer trust. The goal was to build a multi-layered security model that could deliver autonomous, enterprise-wide protection without adding headcount.

Managing cyber risk in M&A

Mergers and acquisitions are central to the grocery holding company’s growth strategy. But each transaction introduces new cyber risk, including inherited network architectures, inconsistent tooling, excessive privileges, and remnants of prior security incidents that were never fully remediated.

“Our M&A targets range from small chains with a single IT person and limited cyber tools to large chains with more developed IT teams, toolsets and instrumentation,” explains the VP of IT. “We needed a fast, repeatable, and reliable way to assess cyber risk before transactions closed.”

AI-driven security built for scale, speed, and resilience

Rather than layering additional point tools onto an already complex environment, the retailer adopted the Darktrace ActiveAI Security Platform™ in 2020 as part of a broader modernization effort to improve resilience, close visibility gaps, and establish a security foundation that could scale with growth.

“Darktrace’s AI-driven approach provided the ideal solution to these challenges,” shares the VP of IT. “It has empowered our organization to maintain a robust security strategy, ensuring the protection of our network and the smooth operation of our business.”

Enterprise-wide visibility into traffic  

By monitoring both north-south and east-west traffic and applying Self-Learning AI, Darktrace develops a dynamic understanding of how users and devices normally behave across locations, roles, and systems.

“Modeling normal behavior across the environment enables us to quickly spot behavior that doesn’t fit. Even subtle changes that could signal a threat but appear legitimate at first glance,” explains the VP of IT.

Real-time threat containment, 24/7

Adopting autonomous response has created operational breathing room for the security team, says the company’s Cybersecurity  Engineer.

“Early on, we enabled full Darktrace autonomous mode and we continue to do so today,” shares the IT Security Architect. “Allowing the technology to act first gives us the time we need to investigate incidents during business hours without putting the business at risk.”

Unified, actionable view of security ecosystem

The grocery retailer integrated Darktrace with its existing security ecosystem of firewalls, vulnerability management tools, and endpoint detection and response, and the VP of IT described the adoption process as “exceptionally smooth.”

The team can correlate enterprise-wide security data for a unified and actionable picture of all activity and risk. Using this “single pane of glass” approach, the retailer trains Level 1 and Level 2 operations staff to assist with investigations and user follow-ups, effectively extending the reach of the security function without expanding headcount.

From reactive defense to security at scale

With Darktrace delivering continuous visibility, autonomous containment, and integrated security workflows, the organization has strengthened its cybersecurity posture while improving operational efficiency. The result is a security model that not only reduces risk, but also supports growth, resilience, and informed decision-making at the business level.

Faster detection, faster resolution

With autonomous detection and response, the retailer can immediately contain risk while analysts investigate and validate activity. With this approach, the company can maintain continuous protection even outside business hours and reduce the chance of lateral spread across systems or locations.

Enterprise-grade protection with a lean team

From cloud environments to clients to SaaS collaboration tools, Darktrace provides holistic autonomous AI defense, processing petabytes of the organization’s network traffic and investigating millions of individual events that could be indicative of a wider incident.

Today, Darktrace autonomously conducts the majority of all investigations on behalf of the IT team, escalating only a tiny fraction for analyst review. The impact has been profound, freeing analysts from endless alerts and hours of triage so they can focus on more valuable, proactive, and gratifying work.

“From an operational perspective, Darktrace gives us time back,” says the Cybersecurity Engineer. More importantly, says the VP of IT, “it gives us peace of mind that we’re protected even if we’re not actively monitoring every alert.”

A strategic input for M&A decision-making

One of the most strategic outcomes has been the role of cybersecurity on M&A. 90 days prior to closing a transaction, the security team uses Darktrace alongside other tools to perform a cyber risk assessment of the potential acquisition. “Our approach with Darktrace has consistently identified gaps and exposed risks,” says the VP of IT, including:

  • Remnants of previous incidents that were never fully remediated
  • Network configurations with direct internet exposure
  • Excessive administrative privileges in Active Directory or on critical hosts

While security findings may not alter deal timelines, the VP of IT says they can have enormous business implications. “With early visibility into these risks, we can reduce exposure to inherited cyber threats, strengthen our position during negotiations, and establish clear remediation requirements.”

A security strategy built to evolve with the business

As the holding group expands its cloud footprint, it will extend Darktrace protections into Azure, applying the same AI-driven visibility and autonomous response to cloud workloads. The VP of IT says Darktrace's evolving capabilities will be instrumental in addressing the organization’s future cybersecurity needs and ability to adapt to the dynamic nature of cloud security.

“With Darktrace’s AI-driven approach, we have moved beyond reactive defense, establishing a resilient security foundation for confident expansion and modernization.”

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March 31, 2026

Phantom Footprints: Tracking GhostSocks Malware

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Why are attackers using residential proxies?

In today's threat landscape, blending in to normal activity is the key to success for attackers and the growing reliance on residential proxies shows a significant shift in how threat actors are attempting to bypass IP detection tools.

The increasing dependency on residential proxies has exposed how prevalent proxy services are and how reliant a diverse range of threat actors are on them. From cybercriminal groups to state‑sponsored actors, the need to bypass IP detection tools is fundamental to the success of these groups. One malware that has quietly become notorious for its ability to avoid anomaly detection is GhostSocks, a malware that turns compromised devices into residential proxies.

What is GhostSocks?

Originally marketed on the Russian underground forum xss[.]is as a Malware‑as‑a‑Service (MaaS), GhostSocks enables threat actors to turn compromised devices into residential proxies, leveraging the victim's internet bandwidth to route malicious traffic through it.

How does Ghostsocks malware work? 

The malware offers the threat actor a “clean” IP address, making it look like it is coming from a household user. This enables the bypassing of geographic restrictions and IP detection tools, a perfect tool for avoiding anomaly detection. It wasn’t until 2024, when a partnership was announced with the infamous information stealer Lumma Stealer, that GhostSocks surged into widespread adoption and alluded to who may be the author of the proxy malware.

Written in GoLang, GhostSocks utilizes the SOCKS5 proxy protocol, creating a SOCKS5 connection on infected devices. It uses a relay‑based C2 implementation, where an intermediary server sits in between the real command-and-control (C2) server and the infected device.

How does Ghostsocks malware evade detection?

To further increase evasion, the Ghostsocks malware wraps its SOCKS5 tunnels in TLS encryption, allowing its malicious traffic to blend into normal network traffic.

Early variants of GhostSocks do not implement a persistence mechanism; however, later versions achieve persistence via registry run keys, ensuring sustained proxy operational time [1].

While proxying is its primary purpose, GhostSocks also incorporates backdoor functionality, enabling malicious actors to run arbitrary commands and download and deploy additional malicious payloads. This was evident with the well‑known ransomware group Black Basta, which reportedly used GhostSocks as a way of maintaining long‑term access to victims’ networks [1].

Darktrace’s detection of GhostSocks Malware

Darktrace observed a steady increase in GhostSocks activity across its customer base from late 2025, with its Threat Research team identifying multiple incidents involving the malware. In one notable case from December 2025, Darktrace detected GhostSocks operating alongside Lumma Stealer, reinforcing that the partnership between Lumma and GhostSocks remains active despite recent attempts to disrupt Lumma’s infrastructure.

Darktrace’s first detection of GhostSocks‑related activity came when a device on the network of a customer in the education sector began making connections to an endpoint with a suspicious self‑signed certificate that had never been seen on the network before.

The endpoint in question, 159.89.46[.]92 with the hostname retreaw[.]click, has been flagged by multiple open‑source intelligence (OSINT) sources as being associated with Lumma Stealer’s C2 infrastructure [2], indicating its likely role in the delivery of malicious payloads.

Darktrace’s detection of suspicious SSL connections to retreaw[.]click, indicating an attempted link to Lumma C2 infrastructure.
Figure 1: Darktrace’s detection of suspicious SSL connections to retreaw[.]click, indicating an attempted link to Lumma C2 infrastructure.

Less than two minutes later, Darktrace observed the same device downloading the executable (.exe) file “Renewable.exe” from the IP 86.54.24[.]29, which Darktrace recognized as 100% rare for this network.

Darktrace’s detection of a device downloading the unusual executable file “Renewable.exe”.
Figure 2: Darktrace’s detection of a device downloading the unusual executable file “Renewable.exe”.

Both the file MD5 hash and the executable itself have been identified by multiple OSINT vendors as being associated with the GhostSocks malware [3], with the executable likely the backdoor component of the GhostSocks malware, facilitating the distribution of additional malicious payloads [4].

Following this detection, Darktrace’s Autonomous Response capability recommended a blocking action for the device in an early attempt to stop the malicious file download. In this instance, Darktrace was configured in Human Confirmation Mode, meaning the customer’s security team was required to manually apply any mitigative response actions. Had Autonomous Response been fully enabled at the time of the attack, the connections to 86.54.24[.]29 would have been blocked, rendering the malware ineffective at reaching its C2 infrastructure and halting any further malicious communication.

 Darktrace’s Autonomous Response capability suggesting blocking the suspicious connections to the unusual endpoint from which the malicious executable was downloaded.
Figure 3: Darktrace’s Autonomous Response capability suggesting blocking the suspicious connections to the unusual endpoint from which the malicious executable was downloaded.

As the attack was able to progress, two days later the device was detected downloading additional payloads from the endpoint www.lbfs[.]site (23.106.58[.]48), including “Setup.exe”, “,.exe”, and “/vp6c63yoz.exe”.

Darktrace’s detection of a malicious payload being downloaded from the endpoint www.lbfs[.]site.
Figure 4: Darktrace’s detection of a malicious payload being downloaded from the endpoint www.lbfs[.]site.

Once again, Darktrace recognized the anomalous nature of these downloads and suggested that a “group pattern of life” be enforced on the offending device in an attempt to contain the activity. By enforcing a pattern of life on a device, Darktrace restricts its activity to connections and behaviors similar to those performed by peer devices within the same group, while still allowing it to carry out its expected activity, effectively preventing deviations indicative of compromise while minimizing disruption. As mentioned earlier, these mitigative actions required manual implementation, so the activity was able to continue. Darktrace proceeded to suggest further actions to contain subsequent malicious downloads, including an attempt to block all outbound traffic to stop the attack from progressing.

An overview of download activity and the Autonomous Response actions recommended by Darktrace to block the downloads.
Figure 5: An overview of download activity and the Autonomous Response actions recommended by Darktrace to block the downloads.

Around the same time, a third executable download was detected, this time from the hostname hxxp[://]d2ihv8ymzp14lr.cloudfront.net/2021-08-19/udppump[.]exe, along with the file “udppump.exe”.While GhostSocks may have been present only to facilitate the delivery of additional payloads, there is no indication that these CloudFront endpoints or files are functionally linked to GhostSocks. Rather, the evidence points to broader malicious file‑download activity.

Shortly after the multiple executable files had been downloaded, Darktrace observed the device initiating a series of repeated successful connections to several rare external endpoints, behavior consistent with early-stage C2 beaconing activity.

Cyber AI Analyst’s investigation

Darktrace’s detection of additional malicious file downloads from malicious CloudFront endpoints.
Figure 7: Darktrace’s detection of additional malicious file downloads from malicious CloudFront endpoints.

Throughout the course of this attack, Darktrace’s Cyber AI Analyst carried out its own autonomous investigation, piecing together seemingly separate events into one wider incident encompassing the first suspicious downloads beginning on December 4, the unusual connectivity to many suspicious IPs that followed, and the successful beaconing activity observed two days later. By analyzing these events in real-time and viewing them as part of the bigger picture, Cyber AI Analyst was able to construct an in‑depth breakdown of the attack to aid the customer’s investigation and remediation efforts.

Cyber AI Analyst investigation detailing the sequence of events on the compromised device, highlighting its extensive connectivity to rare endpoints, the related malicious file‑download activity, and finally the emergence of C2 beaconing behavior.
Figure 8: Cyber AI Analyst investigation detailing the sequence of events on the compromised device, highlighting its extensive connectivity to rare endpoints, the related malicious file‑download activity, and finally the emergence of C2 beaconing behavior.

Conclusion

The versatility offered by GhostSocks is far from new, but its ability to convert compromised devices into residential proxy nodes, while enabling long‑term, covert network access—illustrates how threat actors continue to maximise the value of their victims’ infrastructure. Its growing popularity, coupled with its ongoing partnership with Lumma, demonstrates that infrastructure takedowns alone are insufficient; as long as threat actors remain committed to maintaining anonymity and can rapidly rebuild their ecosystems, related malware activity is likely to persist in some form.

Credit to Isabel Evans (Cyber Analyst), Gernice Lee (Associate Principal Analyst & Regional Consultancy Lead – APJ)
Edited by Ryan Traill (Content Manager)

Appendices

References

1.    https://bloo.io/research/malware/ghostsocks

2.    https://www.virustotal.com/gui/domain/retreaw.click/community

3.    https://synthient.com/blog/ghostsocks-from-initial-access-to-residential-proxy

4.    https://www.joesandbox.com/analysis/1810568/0/html

5. https://www.virustotal.com/gui/url/fab6525bf6e77249b74736cb74501a9491109dc7950688b3ae898354eb920413

Darktrace Model Detections

Real-time Detection Models

Anomalous Connection / Suspicious Self-Signed SSL

Anomalous Connection / Rare External SSL Self-Signed

Anomalous File / EXE from Rare External Location

Anomalous File / Multiple EXE from Rare External Locations

Compromise / Possible Fast Flux C2 Activity

Compromise / Large Number of Suspicious Successful Connections

Compromise / Large Number of Suspicious Failed Connections

Compromise / Sustained SSL or HTTP Increase

Autonomous Response Models

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

Antigena / Network / External Threat / Antigena Suspicious File Block

Antigena / Network / Significant Anomaly / Antigena Controlled and Model Alert

Antigena / Network / External Threat / Antigena File then New Outbound Block

Antigena / Network / Significant Anomaly / Antigena Alerts Over Time Block

Antigena / Network / External Threat / Antigena Suspicious Activity Block

MITRE ATT&CK Mapping

Tactic – Technique – Sub-Technique

Resource Development – T1588 - Malware

Initial Access - T1189 - Drive-by Compromise

Persistence – T1112 – Modify Registry

Command and Control – T1071 – Application Layer Protocol

Command and Control – T1095 – Non-application Layer Protocol

Command and Control – T1071 – Web Protocols

Command and Control – T1571 – Non-Standard Port

Command and Control – T1102 – One-Way Communication

List of Indicators of Compromise (IoCs)

86.54.24[.]29 - IP - Likely GhostSocks C2

http[://]86.54.24[.]29/Renewable[.]exe - Hostname - GhostSocks Distribution Endpoint

http[://]d2ihv8ymzp14lr.cloudfront[.]net/2021-08-19/udppump[.]exe - CDN - Payload Distribution Endpoint

www.lbfs[.]site - Hostname - Likely C2 Endpoint

retreaw[.]click - Hostname - Lumma C2 Endpoint

alltipi[.]com - Hostname - Possible C2 Endpoint

w2.bruggebogeyed[.]site - Hostname - Possible C2 Endpoint

9b90c62299d4bed2e0752e2e1fc777ac50308534 - SHA1 file hash – Likely GhostSocks payload

3d9d7a7905e46a3e39a45405cb010c1baa735f9e - SHA1 file hash - Likely follow-up payload

10f928e00a1ed0181992a1e4771673566a02f4e3 - SHA1 file hash - Likely follow-up payload

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About the author
Isabel Evans
Cyber Analyst
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