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attack-surface.md

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Attack Surface Analysis for prefecthq/prefect

Attack Surface: Weak API Access Credentials & API Key Exposure

Description: Attackers exploit default, weak, or exposed credentials (passwords, API keys) used to access the Prefect Server/Cloud API.

  • Prefect Contribution: Prefect relies on API keys and user credentials for authentication to control and manage workflows, agents, and infrastructure. Insecure management of these credentials directly opens this attack surface, as unauthorized API access grants control over Prefect deployments.
  • Example: A developer hardcodes a Prefect API key into a script that is committed to a public repository. An attacker finds the key and uses it to access the Prefect Cloud account, gaining control over workflows and potentially sensitive data.
  • Impact: Full compromise of the Prefect control plane, unauthorized access to workflows, data, and infrastructure. Potential for data breaches, service disruption, and malicious workflow execution.
  • Risk Severity: Critical
  • Mitigation Strategies:
    • Enforce strong, unique passwords and multi-factor authentication (MFA) for all user accounts accessing Prefect Server/Cloud.
    • Never hardcode API keys. Use environment variables, secure secret management systems (e.g., HashiCorp Vault, AWS Secrets Manager), or Prefect Secrets to store and access API keys.
    • Grant API keys only the necessary permissions using scoped API keys where possible.
    • Implement a policy to regularly rotate API keys.

Attack Surface: API Injection Vulnerabilities

Description: Attackers inject malicious code or commands into API requests targeting Prefect Server/Cloud, exploiting insufficient input validation on Prefect's API endpoints.

  • Prefect Contribution: Prefect's API endpoints handle various inputs, including flow parameters, task parameters, and configuration settings. If Prefect's API does not properly validate and sanitize these inputs, they can be vulnerable to injection attacks when processed by the Prefect Server.
  • Example: A flow parameter is designed to accept a filename. An attacker crafts a malicious filename like ; rm -rf /; and passes it through the Prefect API. If the Prefect Server processes this filename without proper sanitization, it could lead to command injection on the server host.
  • Impact: Server-side command execution, data manipulation, denial of service, potential for lateral movement within the infrastructure hosting the Prefect Server.
  • Risk Severity: High
  • Mitigation Strategies:
    • Implement strict input validation on all Prefect API endpoints. Validate data types, formats, and ranges. Sanitize user-provided input to remove or escape potentially harmful characters before processing by Prefect Server.
    • Use parameterized queries for database interactions within Prefect Server to prevent SQL injection.
    • Run Prefect Server processes with minimal necessary privileges to limit the impact of successful injection attacks.
    • Consider using a Web Application Firewall (WAF) to filter malicious requests and protect Prefect API endpoints.

Attack Surface: Agent Credential Theft

Description: Attackers steal credentials (API keys) used by Prefect Agents to connect to the Prefect Server/Cloud.

  • Prefect Contribution: Prefect Agents require API keys to authenticate and communicate with the Prefect Server/Cloud. If agents are not securely configured and deployed, the agent API keys can be compromised, allowing attackers to impersonate legitimate agents within the Prefect system.
  • Example: An attacker gains access to the file system of a machine where a Prefect Agent is running and extracts the API key stored in the agent's configuration file or environment variables. The attacker can then use this key to register rogue agents, manipulate workflows, or disrupt operations within the Prefect deployment.
  • Impact: Unauthorized agent registration, ability to execute malicious flows within the Prefect system, potential for data exfiltration by rogue flows, denial of service by disrupting legitimate agents.
  • Risk Severity: High
  • Mitigation Strategies:
    • Avoid storing agent API keys in easily accessible files or environment variables. Use secure secret management solutions or operating system-level credential stores where possible for agent API keys.
    • Implement a mechanism to regularly rotate agent API keys.
    • Run agents in isolated environments with restricted access to sensitive resources. Limit the privileges of the agent process.
    • Monitor agent activity for suspicious behavior, such as unexpected agent registrations or unusual flow executions.

Attack Surface: Dynamic Code Execution in Flows

Description: Attackers exploit vulnerabilities in flows that dynamically generate and execute code based on external or untrusted inputs.

  • Prefect Contribution: Prefect flows are Python code, and developers might use dynamic code execution techniques within flows. If flows are designed to accept and process untrusted or poorly validated external inputs that influence dynamic code execution, it creates a direct vulnerability within the Prefect workflow.
  • Example: A flow takes a user-provided Python module name as input and dynamically imports and executes functions from that module. An attacker provides a malicious module name that contains code to steal data or compromise the execution environment when the flow is run by a Prefect Agent.
  • Impact: Arbitrary code execution within the flow's execution environment, potentially leading to data breaches, privilege escalation, or denial of service originating from within a Prefect workflow execution.
  • Risk Severity: High
  • Mitigation Strategies:
    • Minimize or eliminate the use of dynamic code execution within flows whenever possible. Refactor flows to use safer alternatives to dynamic code generation.
    • If dynamic code execution is necessary, rigorously validate and sanitize all inputs used to construct or influence the code being executed within the flow.
    • Execute flows in sandboxed or isolated environments (e.g., containers, virtual machines) to limit the impact of code execution vulnerabilities within Prefect flow runs.
    • Conduct thorough code reviews of flows to identify and mitigate potential dynamic code execution risks.

Attack Surface: Vulnerable Python Packages in Flow Environments

Description: Attackers exploit known vulnerabilities in Python packages used as dependencies in Prefect flow environments.

  • Prefect Contribution: Prefect flows rely on Python packages defined in their environments. While Prefect doesn't introduce the vulnerabilities in the packages themselves, Prefect facilitates the use of these packages within flow executions. If vulnerable packages are used in flow environments, and Prefect agents execute these flows, it creates an attack surface within the Prefect ecosystem.
  • Example: A flow depends on an outdated version of a popular Python library that has a known remote code execution vulnerability. An attacker crafts a malicious input that triggers the vulnerability during flow execution, allowing them to execute arbitrary code on the agent host when the flow is executed by a Prefect Agent.
  • Impact: Remote code execution, data breaches, denial of service, compromise of the agent host and potentially other systems in the execution environment, stemming from vulnerabilities in flow dependencies managed within the Prefect system.
  • Risk Severity: High
  • Mitigation Strategies:
    • Regularly scan flow environments for vulnerable Python packages using tools like pip-audit, safety, or dependency vulnerability scanners integrated into CI/CD pipelines.
    • Maintain up-to-date versions of all Python packages used in flow environments. Implement a process for regularly updating dependencies and patching vulnerabilities in flow environments managed by Prefect.
    • Pin specific versions of Python packages in requirements.txt or conda.yaml for flow environments to ensure consistent and controlled dependency versions and facilitate vulnerability management.
    • Minimize the number of dependencies in flow environments to reduce the overall attack surface related to third-party Python packages used in Prefect flows.