What is a Botnet?
A botnet is a collection of internet-connected devices, such as computers, servers, or IoT gadgets, that are commandeered by an attacker and controlled remotely. The devices, once infected, typically by malware, are known as bots or zombies. The attacker, referred to as the botmaster, issues commands to these bots using various communication protocols to carry out malicious activities.
Botnets can range in size from a handful of compromised machines to millions globally, all operating under the control of a single entity. Botnets enable attackers to perform coordinated actions at scale, such as launching distributed denial of service (DDoS) attacks, stealing data, spreading malware, or generating spam traffic.
Because botnet nodes may be dispersed across continents and hidden within otherwise legitimate organizations or households, identifying and disbanding them is a significant technical challenge. Their structure can be centralized, using command-and-control (C & C) servers, or decentralized, using peer-to-peer mechanisms for resilience against shutdown attempts.
This is part of a series of articles about Bot protection.
In this article:
Python is commonly used for botnet development due to its simplicity, extensive libraries, and cross-platform compatibility.
Its readable syntax allows even novice programmers to write complex network scripts quickly. Python’s standard library and third-party modules like socket, asyncio, requests, and scapy make it easy to implement networking, HTTP requests, and low-level packet manipulation, all of which are essential for building botnet functionality.
Python also runs on multiple operating systems with minimal changes to the code, making it suitable for creating portable malware that can infect diverse devices. Tools like PyInstaller or Nuitka can compile Python scripts into executables, helping attackers obscure source code and deploy bots more discreetly. Python’s popularity and abundance of learning resources lower the barrier for entry into botnet creation.
Python-based botnets share a common set of operational traits shaped by the language’s networking features and ease of scripting. These capabilities allow attackers to automate control, data movement, and disruption with minimal code:
- Remote command execution for tasks like file manipulation, process control, or shell access
- Automated propagation using simple scanners that probe for open ports, weak credentials, or unpatched services
- DDoS functionality such as TCP floods, HTTP request floods, or slow‑loris style connection exhaustion
- Data exfiltration through scripted collection of credentials, cookies, or system metadata
- Persistence mechanisms using startup entries, scheduled tasks, or small resident scripts
- Encrypted or obfuscated communication channels, often over HTTP, WebSocket, or custom TCP protocols
- Modular loading where the bot pulls new Python scripts or payloads from a server and executes them in memory
- Simple peer-to-peer coordination using lightweight messaging logic to avoid a single point of failure
Uri Dorot
Uri Dorot is a senior product marketing manager at Radware, specializing in application protection solutions, service and trends. With a deep understanding of the cyberthreat landscape, Uri helps bridge the gap between complex cybersecurity concepts and real-world outcomes.
Tips from the expert:
In my experience, here are tips that can help you better defend against Python-based botnets and detect them before they escalate:
Analyze Python execution behavior in EDR telemetry: Python-based malware often runs via python.exe, py.exe, or embedded interpreters. Monitor EDR logs for unusual script executions, especially where Python is used outside development or automation contexts. Alert on Python accessing the network, modifying the registry, or spawning system utilities.
Use static and dynamic analysis on suspicious PyInstaller/Nuitka executables: Attackers frequently compile Python code using PyInstaller or Nuitka to evade detection. These often produce large executables with embedded Python environments. Create workflows to unpack these, extract .pyc or bytecode, and scan for common botnet signatures like C2 patterns or loader logic.
Track Python package manager misuse (e.g., pip abuse): Some botnets self-update or fetch additional modules by installing packages via pip. Monitor for unexpected calls to pip install, especially in production environments. Also, block or isolate systems that attempt to reach known malicious Python package registries or typosquatted domains.
Flag use of networking modules in non-dev systems: Libraries like socket, asyncio, urllib, requests, and scapy are rarely used outside developer or automation environments. If seen in runtime execution on endpoints where Python is not expected, treat this as a potential indicator of Python-based malware or botnet staging.
Detect script-based lateral movement over internal SMB/SSH: Python botnets may scan local networks and propagate using SMB shares or brute-force SSH. Monitor for Python scripts invoking tools like paramiko, pexpect, or OS calls to scp, net use, or winrm, particularly in unusual network segments.
Several botnets written in Python have emerged over the years, often as open-source proof-of-concepts or as parts of more sophisticated attack frameworks. Some well-known examples include:
- Sathurbot: It was observed spreading through malicious torrent downloads and using infected systems to perform web crawling and brute-force attacks against WordPress sites. While its core was not entirely in Python, it incorporated Python scripts for automation and command execution.
- GitHubBot: This open-source botnet project that demonstrated how to use GitHub repositories for command-and-control. By embedding instructions in code repositories, the botmaster could issue updates that bots periodically fetch and execute.
- PBot (PythonBot): This malware has also been used in active campaigns, offering DDoS capabilities and modular features. Its simple architecture and Python-based modules made it popular among novice threat actors.
Some Python botnets have been designed for educational or testing purposes but were repurposed in real-world attacks. For example, Botnet Frameworks like Andromeda or B0ts started as research tools but have been adapted for malicious use.
Here are some of the ways that organizations can better protect themselves against python-based botnets.
1. Implement Network-Level DDoS Monitoring and Protection
Enterprises should implement dedicated solutions for monitoring and mitigating distributed denial-of-service attacks at the network layer. These systems analyze incoming traffic for patterns indicative of botnet activity, such as high-volume requests from disparate geographic locations or anomalous protocol usage. Automated mitigation tools can then respond by blocking or rate-limiting suspicious traffic, preventing network resources from being overwhelmed.
Deploying services like cloud-based DDoS mitigation platforms or purpose-built hardware appliances enables real-time detection and response. Combining layered monitoring at both network edge and internal segments can reveal attacks originating from compromised hosts within the organization. Continuous tuning of detection signatures and anomaly baselines is essential to keep pace with evolving Python-based botnet tactics.
2. Hardening Exposed Services and Frameworks
Botnets frequently exploit weak or misconfigured internet-facing services, including outdated web frameworks, custom APIs, and IoT management consoles. Organizations should ensure all exposed services are minimized, patched, and configured to reduce attack surface. This includes disabling unused ports, restricting remote access, and employing secure coding and deployment practices for all public-facing software.
Regularly auditing exposed services through vulnerability scanning and penetration testing helps identify new flaws before attackers do. Applying the principle of least privilege to services, isolating critical resources, and leveraging network segmentation further mitigates the risk of mass compromise by Python-based botnets.
3. Enforcing Authentication and Key Hygiene
Strong, enforced authentication practices are crucial for preventing unauthorized access and botnet propagation. Organizations must replace default passwords, implement multi-factor authentication (MFA) wherever possible, and mandate the use of complex, unique credentials for all users and services. Privileged accounts should be closely monitored and limited only to essential personnel.
Key management hygiene is also vital: SSH keys, API tokens, and other credentials should be rotated regularly and not hardcoded into applications or scripts. Access control lists must be kept up to date, eliminating outdated or unnecessary accounts promptly. Utilizing centralized identity and access management (IAM) solutions helps ensure granular policy enforcement and visibility into authentication events that might signal botnet infiltration attempts.
4. Detecting Abnormal Network Beaconing
Detecting botnet command-and-control activity often relies on monitoring for unusual beaconing patterns. Python botnets tend to communicate with C & C infrastructure at regular or randomized intervals, which may stand out from standard network traffic. Implementing intrusion detection and network traffic analysis systems can help surface such anomalies for investigation.
Analysts should baseline typical outbound traffic and flag deviations, such as uncharacteristic contact with IPs in suspicious geographies or encrypted HTTP requests to non-business domains. Threat intelligence integration is useful for automatically updating detection rules with the latest indicators of compromise. Early detection of beaconing behavior enables rapid containment and remediation.
5. Implementing Rapid Patching and CVE Response Workflows
Timely patch management significantly reduces exposure to botnet exploitation, especially for Python-based campaigns that often automate their scanning for known vulnerabilities. Organizations must maintain up-to-date inventories of software, frameworks, and dependencies, applying security patches as soon as they are available. Automated patch management can minimize delays and ensure consistency across heterogeneous environments.
A defined process for reviewing, prioritizing, and responding to critical vulnerability disclosures (CVEs) is equally important. Security and operations teams should track patching progress and verify remediation through testing and system audits. Collaborating with software vendors and using vulnerability intelligence services helps organizations stay ahead of botnet exploitation cycles.
Python-based botnets are often built for speed and flexibility, enabling attackers to scan exposed services, exploit known vulnerabilities, establish command-and-control (C2) beacons, and launch DDoS or application-layer abuse at scale. Defending against these threats requires layered controls that detect malicious automation early, block exploitation attempts, and mitigate attacks before availability or data is impacted. Radware helps organizations reduce exposure to Python botnets by combining behavioral detection, automated mitigation, and threat intelligence across network and application layers.
DefensePro
Many Python botnets are used to launch volumetric and protocol-based floods, including SYN floods, UDP floods, and reflection-style attacks. DefensePro provides real-time, behavior-based DDoS mitigation that detects botnet-driven attacks without relying solely on signatures. It profiles normal traffic patterns, identifies anomalies such as sudden bursts, malformed protocol behavior, and high-rate attack traffic, and applies targeted mitigation to keep services available. This network-layer defense is also effective against reconnaissance patterns often used by botnets to map exposed services.
Cloud DDoS Protection Service
When botnet traffic is large enough to overwhelm upstream bandwidth or cloud environments, Radware Cloud DDoS Protection Service mitigates attacks using high-capacity scrubbing and behavioral analytics. It filters malicious traffic before it reaches origin infrastructure, helping organizations maintain uptime during high-volume botnet campaigns. This is especially valuable for Python botnets that can rapidly scale attacks using rented infrastructure and compromised devices.
Bot Manager
Python botnets frequently perform automated probing, credential abuse, scraping, and high-velocity API interactions. Radware Bot Manager detects and blocks automated traffic that mimics legitimate behavior, helping prevent bot-driven reconnaissance and application-layer resource exhaustion. This reduces attack surface exposure and prevents botnets from repeatedly testing endpoints for weaknesses, misconfigurations, or exploitable functions.
Cloud WAF Service
Python botnets commonly exploit unpatched web frameworks, exposed admin interfaces, and vulnerable application components. Cloud WAF helps block exploit attempts, filter malicious payloads, and enforce strict request validation to protect applications from injection attacks and known CVE exploitation patterns. Virtual patching is particularly important during the critical window between vulnerability disclosure and full remediation, reducing botnet infection opportunities.
Threat Intelligence Subscriptions
Botnets rely on known attacker infrastructure—scanning nodes, C2 servers, and distributed bot IP ranges. Radware Threat Intelligence Subscriptions provide continuously updated indicators derived from real-world attack environments, enabling proactive blocking of high-risk sources. By stopping known malicious infrastructure early, organizations can reduce reconnaissance traffic and limit botnet reach before attacks escalate.
Together, these Radware capabilities strengthen defenses against Python botnets across their lifecycle, disrupting reconnaissance, blocking exploitation attempts, and mitigating DDoS and automated abuse at both network and application layers.