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Threat Model Analysis for pinterest/ktlint

Threat: Supply Chain Compromise of ktlint

  • Description: A malicious actor compromises ktlint's distribution channels (GitHub, Maven Central, mirrors) and injects malicious code into the ktlint artifact or its dependencies. Attackers might compromise maintainer accounts, exploit vulnerabilities in distribution infrastructure, or perform man-in-the-middle attacks during download.
  • Impact: Introduction of backdoors, vulnerabilities, or data exfiltration capabilities directly into our development environment and potentially into the codebase during linting or formatting processes. This could lead to full system compromise, data breaches, or supply chain attacks affecting our applications and users.
  • Affected ktlint component: ktlint distribution mechanism (GitHub repository, Maven Central, download mirrors), ktlint core artifact, ktlint dependencies.
  • Risk Severity: High
  • Mitigation Strategies:
    • Strict artifact verification: Always verify the integrity of downloaded ktlint artifacts using checksums (SHA-256 or stronger) against official and trusted sources.
    • Dependency scanning and management: Implement automated dependency scanning tools to continuously monitor ktlint and its dependencies for known vulnerabilities.
    • Pin ktlint versions: Explicitly define and pin specific, tested ktlint versions in project build configurations to prevent automatic updates to potentially compromised versions.
    • Monitor official ktlint channels: Regularly monitor official ktlint communication channels (GitHub repository, announcements) for security updates and advisories.
    • Utilize trusted artifact repositories: Prefer using reputable artifact repositories and consider using a private, internally managed repository for ktlint and its dependencies to enhance control and security.

Threat: Malicious ktlint Rulesets

  • Description: Attackers create or compromise ktlint rulesets (custom or third-party) to embed malicious logic that is executed by ktlint during code analysis. This could involve injecting code, exploiting vulnerabilities in ktlint's rule engine through crafted rules, or exfiltrating data from the codebase during rule execution. Malicious rulesets could be distributed through public repositories or social engineering.
  • Impact: Direct code injection into the codebase during ktlint execution, introduction of vulnerabilities through malicious rule logic, or unauthorized exfiltration of sensitive data from the codebase during the linting process. This can lead to backdoors, data breaches, and compromised application security posture.
  • Affected ktlint component: ktlint rule engine, custom ruleset loading and execution, third-party ruleset integration.
  • Risk Severity: High
  • Mitigation Strategies:
    • Rigorous review of custom rulesets: Implement mandatory and thorough security reviews and code audits for all custom ktlint rulesets before deployment.
    • Trusted ruleset sources only: Strictly limit the usage of third-party rulesets to those originating from highly trusted and reputable sources with a proven security track record.
    • Static analysis of rulesets: Apply static analysis tools and techniques to examine ktlint ruleset code for suspicious patterns, potential vulnerabilities, or malicious intent before integration.
    • Principle of least privilege for ruleset execution: Ensure that ktlint rulesets operate with the minimal necessary permissions and restrict their access to system resources and network access to prevent potential abuse.
    • Sandboxing ruleset execution (if feasible): Explore and implement sandboxing or isolation techniques for ktlint ruleset execution to limit the potential impact of malicious rulesets.