Analysis Summary

PumaBot is a newly discovered, sophisticated Linux-based botnet developed in the Go programming language, specifically targeting vulnerable Internet of Things (IoT) devices, with a focus on surveillance and traffic camera systems.

Identified by Analysts, the malware has been observed actively compromising embedded Linux devices through a highly targeted strategy. Unlike traditional botnets that rely on indiscriminate scanning, PumaBot distinguishes itself by retrieving curated IP address lists from its command-and-control (C2) servers. This stealthy tactic allows it to avoid detection mechanisms that typically identify large-scale scanning behavior, thereby increasing its success rate and reducing its exposure.

The malware’s primary infection vector is SSH credential brute-forcing, but its sophistication lies in its ability to tailor attacks based on specific targets. Once access is gained, PumaBot immediately executes fingerprinting logic to confirm if the compromised system matches known surveillance equipment. Upon successful verification, the malware proceeds to establish persistence through clever deception. It embeds itself in system directories such as /lib/redis, disguising its presence by mimicking legitimate services like Redis or MySQL. Notably, it uses naming tricks like capitalizing letters (e.g., mysqI.service) to evade detection while ensuring it starts automatically on system boot through systemd service configuration.

PumaBot’s main monetization goal is cryptocurrency mining. On infected systems, it executes mining-related commands such as xmrig and networkxm, leveraging the device’s computing resources to generate illegal profits. This activity can significantly degrade the performance of affected surveillance infrastructure, posing operational risks to organizations that rely on these devices. The botnet also collects comprehensive system data using commands like uname -a to gather information on the operating system, kernel version, and device architecture, enabling more tailored exploitation or management of the compromised network.

Communication with the botnet’s operators is also highly engineered for stealth. Collected data, including device specifications and compromised credentials, is transmitted back to the C2 infrastructure using custom HTTP headers in JSON format, allowing attackers to maintain detailed inventories without triggering standard network security alerts. Overall, PumaBot’s emergence signals a growing threat to the IoT ecosystem, where poor security hygiene, such as using default or weak credentials, leaves critical infrastructure exposed. It serves as a stark reminder that organizations must implement robust authentication, continuous monitoring, and strict configuration management to safeguard against such advanced threats.

Impact

• Sensitive Data Theft
• Gain Access
• Crypto Theft

Indicators of Compromise

SHA1

• c39c96dc5c1e640d081da30cf8f0638689700483
• 5a1448bb86d5658f396c463f08774fdf171245e6
• 158f869a1ae3aa2a3586920e788a9110b7495b9d

Remediation

• Block all threat indicators at your respective controls.
• Search for indicators of compromise (IOCs) in your environment utilizing your respective security controls.
• Disable default usernames/passwords.
• Require complex, unique passwords or key-based SSH authentication.
• Restrict SSH access using firewalls, VPNs, or IP whitelisting.
• Disable SSH on devices that don’t require remote access.
• Patch all IoT devices and Linux systems with the latest security updates from vendors.
• Monitor vendor advisories for known vulnerabilities.
• Isolate IoT devices from critical systems using VLANs or dedicated networks.
• Limit lateral movement opportunities in case of compromise.
• Use intrusion detection systems (IDS) to monitor for brute-force SSH attempts or abnormal CPU usage.
• Monitor for suspicious traffic, such as outbound connections with custom HTTP headers.
• Regularly inspect system directories (e.g., /lib, /etc/systemd/system/) for unauthorized binaries or suspicious service files.
• Remove or quarantine unknown services mimicking legitimate ones (e.g., mysqI.service).
• Turn off unnecessary services to reduce the attack surface.
• Use port scanning internally to detect unexpected services.
• Conceal device-specific metadata where possible to reduce fingerprinting success.
• Use access controls and monitoring on exposed web interfaces.
• Maintain secure and regular backups of device configurations.
• Develop and test an incident response plan specifically for IoT-related attacks.
• Train administrators on identifying and removing disguised malware.
• Promote best practices for IoT and Linux device security.