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attack-surface.md

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Attack Surface Analysis for robb/cartography

Attack Surface: Data Source Credential Compromise

  • Description: Exposure or theft of credentials used by Cartography to access cloud providers and other data sources.
  • Cartography Contribution: Cartography requires storing and managing sensitive credentials for multiple cloud environments to function. This centralizes credential management, making it a high-value target directly due to Cartography's design.
  • Example: An attacker gains access to the server where Cartography is running and retrieves AWS access keys stored in a plaintext configuration file used by Cartography.
  • Impact: Full compromise of the targeted cloud environment (AWS in this example), including data breaches, resource manipulation, denial of service, and potential lateral movement to other connected systems.
  • Risk Severity: Critical
  • Mitigation Strategies:
    • Developers/Users:
      • Never store credentials in plaintext configuration files or version control.
      • Utilize secure secrets management solutions (e.g., HashiCorp Vault, AWS Secrets Manager, Azure Key Vault, GCP Secret Manager) to store and retrieve credentials.
      • Implement least privilege access for Cartography service accounts, granting only necessary permissions to each data source.
      • Regularly rotate credentials used by Cartography.
      • Encrypt configuration files at rest if secrets management solutions are not fully implemented.

Attack Surface: Dependency Vulnerabilities

  • Description: Exploitation of known security vulnerabilities in third-party libraries and software dependencies used by Cartography.
  • Cartography Contribution: Cartography relies on a significant number of Python libraries and the Neo4j database. This extensive dependency chain directly increases the attack surface by introducing potential vulnerabilities not directly within Cartography's code but necessary for its operation.
  • Example: A vulnerability is discovered in the requests Python library, which Cartography uses for API calls. An attacker crafts a malicious API response that exploits this vulnerability when processed by Cartography, leading to remote code execution on the Cartography server.
  • Impact: Compromise of the Cartography server, potentially leading to data exfiltration, denial of service, or further attacks on the infrastructure Cartography monitors.
  • Risk Severity: High
  • Mitigation Strategies:
    • Developers/Users:
      • Implement a robust dependency management process.
      • Regularly scan dependencies for known vulnerabilities using tools like pip-audit, safety, or dependency scanning features in CI/CD pipelines.
      • Keep dependencies up-to-date by promptly applying security patches and updates.
      • Utilize virtual environments to isolate Cartography's dependencies and prevent conflicts with other system libraries.

Attack Surface: Data Ingestion and Processing Flaws

  • Description: Vulnerabilities in how Cartography parses, validates, and processes data received from external APIs and data sources.
  • Cartography Contribution: Cartography's core function is to ingest and process data from various cloud provider APIs. Vulnerabilities in this core functionality are directly related to Cartography's design and implementation.
  • Example: Cartography's code is vulnerable to a Server-Side Request Forgery (SSRF) vulnerability when processing API responses. An attacker manipulates a cloud API response to cause Cartography to make requests to internal resources, potentially exposing sensitive information or allowing further exploitation.
  • Impact: Information disclosure, denial of service, or potentially remote code execution if vulnerabilities in data processing are severe enough.
  • Risk Severity: High
  • Mitigation Strategies:
    • Developers:
      • Implement robust input validation for all data received from external sources.
      • Follow secure coding practices to prevent common vulnerabilities like SSRF, injection flaws, and buffer overflows in data processing modules.
      • Conduct thorough code reviews and security testing of data ingestion and processing logic.
    • Users:
      • Keep Cartography updated to benefit from security patches and improvements in data processing.
      • Monitor Cartography logs for unexpected errors or suspicious activity during data ingestion.

Attack Surface: Insecure Neo4j Database

  • Description: Vulnerabilities arising from misconfiguration or inherent weaknesses in the Neo4j database used by Cartography.
  • Cartography Contribution: Cartography requires a Neo4j database to function. The security of this essential component is directly tied to Cartography's overall security posture.
  • Example: The Neo4j database instance used by Cartography is exposed to the public internet with default credentials. An attacker gains unauthorized access to the Neo4j database and can read, modify, or delete all infrastructure data collected by Cartography.
  • Impact: Complete compromise of infrastructure data collected by Cartography, potential data breaches, and the ability for attackers to gain a deep understanding of the target environment for further attacks.
  • Risk Severity: High to Critical
  • Mitigation Strategies:
    • Developers/Users:
      • Follow Neo4j security hardening guidelines.
      • Implement strong authentication and authorization for Neo4j access.
      • Change default Neo4j passwords immediately.
      • Restrict network access to Neo4j to only authorized systems (e.g., the Cartography server).
      • Keep Neo4j updated with the latest security patches.
      • Regularly back up the Neo4j database to ensure data recovery in case of compromise or failure.

Attack Surface: Information Disclosure through Data Exposure

  • Description: Accidental or intentional exposure of sensitive infrastructure data collected and stored by Cartography.
  • Cartography Contribution: Cartography's primary purpose is to collect and aggregate sensitive infrastructure information. This inherent function directly creates the risk of information disclosure if access controls are insufficient.
  • Example: Cartography's Neo4j database is not properly secured, and an internal employee with overly broad access credentials queries the database and exports sensitive configuration details of production systems, which are then inadvertently shared outside the organization.
  • Impact: Exposure of sensitive infrastructure details, potentially leading to security breaches, competitive disadvantage, and compliance violations.
  • Risk Severity: High
  • Mitigation Strategies:
    • Developers/Users:
      • Implement strict access control to Cartography's application, configuration, logs, and especially the Neo4j database.
      • Apply the principle of least privilege when granting access to Cartography and its data.
      • Encrypt the Neo4j database at rest and in transit.
      • Regularly audit access logs to Cartography and Neo4j to detect and investigate suspicious activity.
      • Implement data loss prevention (DLP) measures to prevent accidental or malicious data exfiltration.