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attack-surface.md

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Attack Surface Analysis for locustio/locust

Attack Surface: Unauthorized Access to Locust Web UI/API

  • Description: Unprotected access to the Locust web interface or its underlying REST API allows attackers to control load tests.
  • How Locust Contributes: Locust provides the web UI and API for managing tests; this is a core Locust component, and its insecurity is a direct Locust-related risk.
  • Example: An attacker accesses http://locust-master:8089 without credentials and starts a massive load test against a production system.
  • Impact:
    • Denial of Service (DoS) against the target application.
    • Unauthorized data access (viewing test results, potentially including sensitive data).
    • Manipulation of test parameters, leading to inaccurate results.
    • Disruption of legitimate testing activities.
  • Risk Severity: Critical
  • Mitigation Strategies:
    • Authentication: Enforce strong authentication (e.g., basic auth, OAuth, LDAP integration) for both the web UI and API. Use strong, unique passwords.
    • Authorization: Implement role-based access control (RBAC) to limit user privileges within the Locust UI/API.
    • Network Segmentation: Restrict network access to the Locust master node (where the UI/API runs) using firewalls and network segmentation. Do not expose it directly to the public internet.
    • HTTPS: Use HTTPS to encrypt all communication with the Locust UI/API, protecting credentials and data in transit.
    • API Rate Limiting: Implement rate limiting on the API to prevent abuse and potential DoS attacks against the Locust master itself.
    • Input Validation: Validate all API inputs to prevent injection attacks.

Attack Surface: Worker Node Compromise

  • Description: Attackers gaining control of Locust worker nodes, which execute the load test scripts.
  • How Locust Contributes: Locust's distributed architecture relies on worker nodes; their compromise is a direct consequence of using Locust's distributed model. While the vulnerability might be in the OS, the attack vector is through Locust's architecture.
  • Example: An attacker exploits a vulnerability in the operating system of a worker node and installs malware.
  • Impact:
    • Lateral movement within the network from the compromised worker.
    • Modification of test scripts to inject malicious code or alter test behavior.
    • Data exfiltration from the worker node or the target system.
    • Use of worker resources for malicious purposes (e.g., cryptocurrency mining).
  • Risk Severity: High
  • Mitigation Strategies:
    • Isolation: Run worker nodes in isolated environments (containers, VMs) with minimal privileges (least privilege principle).
    • Hardening: Harden the operating system and software on worker nodes. Apply security patches promptly.
    • Secure Communication: Use secure communication channels (SSH, TLS) between the master and worker nodes.
    • Authentication: Implement strong authentication for accessing worker nodes (e.g., SSH keys).
    • Monitoring: Monitor worker node activity for suspicious behavior.
    • Resource Limits: Implement resource limits (e.g., cgroups) to prevent a compromised worker from consuming excessive resources.

Attack Surface: Insecure Test Scripts

  • Description: Vulnerabilities within the Python test scripts themselves (e.g., hardcoded credentials, command injection).
  • How Locust Contributes: Locust executes these user-provided Python scripts; the vulnerability exists because Locust runs this code. This is a direct attack surface of Locust's core functionality.
  • Example: A test script contains a hardcoded API key, which is exposed when the script is compromised or leaked. Or, a script uses os.system() with unsanitized user input, leading to command injection.
  • Impact:
    • Exposure of sensitive information (credentials, API keys).
    • Execution of arbitrary code on worker nodes.
    • Data breaches if the script interacts with sensitive data.
  • Risk Severity: High
  • Mitigation Strategies:
    • Secure Coding Practices: Follow secure coding guidelines for Python. Avoid hardcoding credentials; use environment variables or secure configuration management.
    • Input Sanitization: Sanitize all user inputs and external data used in test scripts. Validate data types and formats.
    • Code Reviews: Conduct thorough code reviews of test scripts to identify and fix vulnerabilities.
    • Static Analysis: Use static analysis tools (linters, security scanners) to detect potential security issues in the Python code.
    • Avoid eval()/exec(): Avoid using eval() or exec() with untrusted input.