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Attack Tree Analysis for lucasg/dependencies

Objective: To execute arbitrary code on the target application server (Remote Code Execution - RCE) or exfiltrate sensitive data from the target application by exploiting vulnerabilities within the lucasg/dependencies project or its transitive dependencies.

Attack Tree Visualization

  • 1. Exploit Vulnerability in Direct Dependency

    • 1.1. Identify Vulnerable Direct Dependency (CRITICAL NODE)
      • 1.1.1. Analyze dependencies.yml (or equivalent):
        • Likelihood: Very High
        • Impact: N/A (Identification step)
        • Effort: Very Low
        • Skill Level: Novice
        • Detection Difficulty: Very Easy
        • Reasoning: This is the most straightforward way to list dependencies, making it easy for an attacker to identify potential targets.
      • 1.1.2. Use SCA tool (e.g., Snyk, Dependabot, OWASP Dependency-Check):
        • Likelihood: High
        • Impact: N/A
        • Effort: Low
        • Skill Level: Novice
        • Detection Difficulty: Easy
        • Reasoning: Automated tools are designed for this purpose and are highly effective.
      • 1.1.3. Monitor CVE databases (NVD, GitHub Advisories):
        • Likelihood: High
        • Impact: N/A
        • Effort: Low
        • Skill Level: Novice
        • Detection Difficulty: Easy
        • Reasoning: Publicly disclosed vulnerabilities are readily available.
    • 1.3. Trigger Vulnerability via Application Input (CRITICAL NODE)
      • Likelihood: Medium
      • Impact: N/A
      • Effort: Medium
      • Skill Level: Intermediate
      • Detection Difficulty: Medium
      • Reasoning: The success of this step depends heavily on how the application uses the vulnerable dependency. If the application passes user-controlled input directly to a vulnerable function, the likelihood is higher. Detection difficulty depends on the application's logging and monitoring capabilities.

  • 2. Exploit Vulnerability in Transitive Dependency

    • 2.1. Identify Vulnerable Transitive Dependency (CRITICAL NODE)
      • Likelihood: Medium
      • Impact: N/A
      • Effort: High
      • Skill Level: Intermediate
      • Detection Difficulty: Hard
      • Reasoning: Transitive dependencies are less obvious and require deeper analysis. Attackers often target these because they are less scrutinized.
      • 2.1.2. Use SCA tool (critical for transitive dependencies):
        • Likelihood: High
        • Impact: N/A
        • Effort: Low
        • Skill Level: Novice
        • Detection Difficulty: Easy
        • Reasoning: SCA tools are essential for identifying vulnerabilities in transitive dependencies.

  • 3. Supply Chain Attack on a Dependency (HIGH-RISK PATH)

    • Likelihood: Low
    • Impact: Very High
    • Effort: Very High
    • Skill Level: Expert
    • Detection Difficulty: Very Hard
    • 3.1. Compromise Dependency Maintainer Account
      • Likelihood: Low
      • Impact: N/A
      • Effort: High
      • Skill Level: Advanced
      • Detection Difficulty: Hard
      • Reasoning: Requires bypassing authentication and security measures of the package repository or the maintainer's personal accounts.
      • 3.1.1. Phishing/social engineering:
        • Likelihood: Medium
        • Impact: N/A
        • Effort: Medium
        • Skill Level: Intermediate
        • Detection Difficulty: Medium
        • Reasoning: Success depends on the maintainer's security awareness.
    • 3.2. Inject Malicious Code into Dependency (CRITICAL NODE)
      • Likelihood: Low
      • Impact: N/A
      • Effort: Medium
      • Skill Level: Advanced
      • Detection Difficulty: Hard
      • Reasoning: This is the core of the supply chain attack. It requires the attacker to successfully compromise a maintainer's account or the repository itself and then subtly modify the code.
      • 3.2.2. Publish a new, malicious version:
        • Likelihood: Low
        • Impact: N/A
        • Effort: Low
        • Skill Level: Advanced
        • Detection Difficulty: Medium
        • Reasoning: Once the account is compromised, publishing is relatively easy, but the malicious version might be detected by vigilant users or automated systems.
    • 3.3. Application Updates to Malicious Version (CRITICAL NODE)
      • Likelihood: Medium
      • Impact: N/A
      • Effort: N/A
      • Skill Level: N/A
      • Detection Difficulty: Medium
      • Reasoning: This depends entirely on the application's update mechanism. Automatic updates without version pinning significantly increase the likelihood.
      • 3.3.1. Automatic updates (without version pinning):
        • Likelihood: High
        • Impact: N/A
        • Effort: N/A
        • Skill Level: N/A
        • Detection Difficulty: Medium
        • Reasoning: This is the worst-case scenario, as the application will automatically pull in the malicious version.

  • 4. Dependency Confusion Attack (HIGH-RISK PATH)

    • Likelihood: Low

    • Impact: High

    • Effort: Medium

    • Skill Level: Intermediate

    • Detection Difficulty: Hard

    • 4.1 Identify internal package names used by the application or lucasg/dependencies.

      • Likelihood: Medium
      • Impact: N/A
      • Effort: Medium
      • Skill Level: Intermediate
      • Detection Difficulty: Medium
      • Reasoning: Requires reconnaissance or insider knowledge. Open-source projects or leaked information can make this easier.

    • 4.2 Publish malicious packages with the same names to a public registry (e.g., npm, PyPI). (CRITICAL NODE)

      • Likelihood: High
      • Impact: N/A
      • Effort: Low
      • Skill Level: Intermediate
      • Detection Difficulty: Very Hard
      • Reasoning: Public registries generally allow anyone to publish packages, making this step relatively easy. Detecting this proactively is very difficult.

    • 4.3 Hope that the application's build process or dependency resolution mechanism prioritizes the public registry over the internal one. (CRITICAL NODE)

      • Likelihood: Medium
      • Impact: N/A
      • Effort: N/A
      • Skill Level: N/A
      • Detection Difficulty: Medium
      • Reasoning: This depends on the specific configuration of the build system and dependency management tools. Misconfigurations are common.

    • 4.4. Application Updates to Malicious Version

      • Likelihood: Medium
      • Impact: N/A
      • Effort: N/A
      • Skill Level: N/A
      • Detection Difficulty: Medium
      • Reasoning: This depends entirely on the application's update mechanism. Automatic updates without version pinning significantly increase the likelihood.
      • 4.4.1 Automatic updates (without version pinning):
        • Likelihood: High
        • Impact: N/A
        • Effort: N/A
        • Skill Level: N/A
        • Detection Difficulty: Medium
        • Reasoning: This is the worst-case scenario, as the application will automatically pull in the malicious version.

Attack Tree Path: High-Risk Path 3: Supply Chain Attack

  1. Reconnaissance: The attacker researches the lucasg/dependencies project and its maintainers, looking for potential weaknesses in their security practices (e.g., weak passwords, lack of 2FA, outdated software).
  2. Compromise: The attacker targets a maintainer's account through phishing, credential stuffing, or exploiting a vulnerability in their systems.
  3. Code Injection: Once the attacker gains access, they subtly modify the dependency's code to include malicious functionality (e.g., a backdoor, data exfiltration code). They aim to make the changes difficult to detect during code review.
  4. Publication: The attacker publishes a new version of the compromised dependency to the public package registry.
  5. Propagation: Applications using lucasg/dependencies (especially those without version pinning) automatically update to the malicious version.
  6. Exploitation: The malicious code is executed when the application uses the compromised dependency, leading to RCE or data exfiltration.

Attack Tree Path: High-Risk Path 4: Dependency Confusion Attack

  1. Reconnaissance: The attacker identifies internal package names used by the target application or lucasg/dependencies. This might involve analyzing the application's source code (if available), examining build logs, or using social engineering.
  2. Package Creation: The attacker creates a malicious package with the same name as the internal package. This package contains malicious code designed to achieve the attacker's goals (RCE or data exfiltration).
  3. Publication: The attacker publishes the malicious package to a public package registry (e.g., npm, PyPI).
  4. Exploitation: The application's build system, due to misconfiguration or default behavior, downloads and installs the malicious package from the public registry instead of the intended internal package. This happens because the public registry often has higher priority.
  5. Execution: When the application runs, the malicious code within the attacker's package is executed, leading to compromise.