Cybersecurity 3 min read

Unveiling the Cloudypots System: Cado Security's Latest Strategy in Cybersecurity

Unveiling the Cloudypots System: Cado Security's Latest Strategy in Cybersecurity

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In the ever-evolving landscape of cybersecurity, innovative approaches are crucial to stay ahead of malicious actors. Cado Security has introduced a groundbreaking methodology called Cloudypots, designed to detect and analyze novel attack techniques targeting popular software and services. This blog post delves into the intricate details of Cloudypots, examining its development, functionality, and significant findings.

The Genesis of Cloudypots

Cado Security Labs has long utilized honeypots - decoy systems that mimic various services like SSH and Redis - to uncover new malware and threat vectors. Previously, they employed a commonly used honeypot system known as T-Pot, which led to the discovery of malware like P2Pinfect. However, recognizing the need for more advanced and interactive honeypots, Cado Security embarked on developing Cloudypots. This system, leveraging OpenStack, enables the safe and efficient operation of honeypot virtual machines (VMs).

Conceptualization and Implementation

The idea for Cloudypots emerged during investigations into increased exploitations of vulnerabilities like GitLab CVE-2021-22205. The team initially considered using Firecracker containers to deploy Open Containers Initiative (OCI) images of vulnerable services but later opted for OpenStack as their hypervisor. This choice allowed them to construct a private cloud environment, ideal for running malware without the risks associated with using public cloud services.

The Cloudypots Design

Cloudypots' design is straightforward yet effective. Vulnerable services are deployed within VMs equipped with extensive logging capabilities. Additionally, each VM boot generates a new set of Thinkst canary tokens, which are strategically placed in files like .aws/credentials. A unique guardrail system on the host identifies suspicious activities and conducts an "autopsy" of the VM, analyzing the memory, disk, and network traffic using the Cado platform and Volatility3. After the analysis, the VM is reset to a baseline image.

Guardrail: The Safety Mechanism

The guardrail is a critical component of Cloudypots, ensuring the honeypot remains secure and is not used for malicious activities. It includes strict security groups within OpenStack, allowing only inbound traffic and limiting outbound traffic. The guardrail also incorporates a detection system utilizing network traffic capture and data from Prometheus, a monitoring database. This system identifies potential compromises through patterns indicative of malware activities, such as port scanning, masscan detection, and blocklisted port detection.

Autopsy and Analysis

When a rule is triggered, an autopsy is initiated, halting the VM's CPU and preserving all volatile artifacts. The honeypot's disk image and memory are then cloned and analyzed for any signs of intrusion or malicious activity. This comprehensive process allows the team to accurately identify and understand the nature of the attacks and the attackers' objectives.

Key Findings and Impact

Since its deployment, Cloudypots has detected and analyzed over 200 compromises, primarily from Docker and Jupyter honeypots. This analysis led to the discovery of significant malware campaigns like Qubitstrike and OracleIV. One notable observation was the Cetus malware, predominantly used by TeamTNT, which continues to spread despite inactive command and control servers. These findings underscore Cloudypots' effectiveness in providing vital threat intelligence and staying ahead of cyber attackers.


Cloudypots represents a significant advancement in cybersecurity, showcasing Cado Security's commitment to innovative and proactive defense strategies. By continuously evolving and expanding this system, Cado Security aims to uncover new attack techniques and respond swiftly to emerging threats, fortifying their position as a leader in cloud forensics and incident response.

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