attack-tree.md

Attack Tree Analysis for robb/cartography

Objective: Exfiltrate Sensitive Data or Manipulate Cloud Resources via Cartography

Attack Tree Visualization

Goal: Exfiltrate Sensitive Data or Manipulate Cloud Resources via Cartography

├── 1. Compromise Cartography Instance [HR] │ ├── 1.1.3 Dependency Vulnerabilities [HR] │ │ └── 1.1.3.1 Exploit known vulnerabilities in Cartography's Python dependencies. [CN] │ ├── 1.1.5 Configuration Errors [HR] │ │ └── 1.1.5.2 Weak or default credentials used. [CN] │ └── 1.3 Social Engineering / Phishing [HR] │ └── 1.3.1 Trick an administrator into revealing credentials. [CN] │ ├── 2. Leverage Cartography's Access [HR] │ ├── 2.1 Directly Query Neo4j Database [HR] │ │ └── 2.1.1 Execute arbitrary Cypher queries. [CN] │ └── 2.3 Use Cartography's Credentials [HR] │ └── 2.3.1 Extract cloud provider credentials. [CN] │ └── 3. Compromise Neo4j Database [HR] ├── 3.1 Exploit Neo4j Vulnerabilities │ └── 3.1.1 Exploit known vulnerabilities in the specific Neo4j version. [CN] ├── 3.2 Weak Neo4j Credentials [HR] │ └── 3.2.1 Use default or easily guessable credentials. [CN] └── 3.3 Neo4j Misconfiguration [HR] └── 3.3.1 Neo4j exposed to the public internet. [CN]

Attack Tree Path: 1. Compromise Cartography Instance [HR]

• 1.1.3 Dependency Vulnerabilities [HR]

  • 1.1.3.1 Exploit known vulnerabilities in Cartography's Python dependencies. [CN]
    • Description: Attackers scan for and exploit publicly known vulnerabilities (CVEs) in Cartography's Python dependencies (e.g., Boto3, Neo4j driver, requests). They might use automated tools or manually craft exploits.
    • Likelihood: Medium to High
    • Impact: Medium to High (depending on the specific vulnerability)
    • Effort: Low (often automated tools are available)
    • Skill Level: Intermediate
    • Detection Difficulty: Easy (with vulnerability scanning) / Medium (without)
    • Mitigation: Rigorous dependency management, frequent updates, vulnerability scanning.

• 1.1.5 Configuration Errors [HR]

  • 1.1.5.2 Weak or default credentials used. [CN]
    • Description: Attackers attempt to log in using default credentials (e.g., "admin/admin") or easily guessable passwords for Cartography or its associated services (Neo4j, cloud accounts).
    • Likelihood: Medium
    • Impact: High
    • Effort: Very Low
    • Skill Level: Novice
    • Detection Difficulty: Easy
    • Mitigation: Strong, unique passwords; never use default credentials; enforce password complexity policies.

• 1.3 Social Engineering / Phishing [HR]

  • 1.3.1 Trick an administrator into revealing credentials. [CN]
    • Description: Attackers use phishing emails, phone calls, or other social engineering techniques to trick an administrator with access to Cartography into revealing their credentials or installing malware.
    • Likelihood: Medium
    • Impact: High
    • Effort: Low to Medium
    • Skill Level: Intermediate
    • Detection Difficulty: Medium (with user awareness training)
    • Mitigation: User awareness training; multi-factor authentication; strong email security.

Attack Tree Path: 2. Leverage Cartography's Access [HR]

• 2.1 Directly Query Neo4j Database [HR]

  • 2.1.1 Execute arbitrary Cypher queries. [CN]
    • Description: After compromising the Cartography instance, the attacker uses their access to execute arbitrary Cypher queries against the Neo4j database, potentially extracting all stored data or modifying it.
    • Likelihood: High (if Cartography instance is compromised)
    • Impact: High
    • Effort: Low
    • Skill Level: Intermediate
    • Detection Difficulty: Medium (with query monitoring)
    • Mitigation: Least privilege access to Neo4j; query monitoring and auditing; input validation (if applicable).

• 2.3 Use Cartography's Credentials [HR]

  • 2.3.1 Extract cloud provider credentials. [CN]
    • Description: The attacker extracts cloud provider credentials (AWS keys, Azure service principals, GCP service accounts) that Cartography uses or stores. This allows them to bypass Cartography and directly access cloud resources.
    • Likelihood: Medium to High (depending on how credentials are stored)
    • Impact: Very High
    • Effort: Low
    • Skill Level: Intermediate
    • Detection Difficulty: Medium (with access logging)
    • Mitigation: Never store credentials in plain text; use a secure credential management system (e.g., AWS Secrets Manager, Azure Key Vault, HashiCorp Vault); rotate credentials regularly.

Attack Tree Path: 3. Compromise Neo4j Database [HR]

• 3.1 Exploit Neo4j Vulnerabilities

  • 3.1.1 Exploit known vulnerabilities in the specific Neo4j version. [CN]
    • Description: Similar to Cartography's dependencies, attackers exploit known vulnerabilities in the deployed Neo4j version.
    • Likelihood: Medium to High (depending on update frequency)
    • Impact: High (full database access)
    • Effort: Low (if using automated exploit tools)
    • Skill Level: Intermediate
    • Detection Difficulty: Easy (with vulnerability scanning) / Medium (without)
    • Mitigation: Keep Neo4j updated; vulnerability scanning; follow Neo4j security best practices.

• 3.2 Weak Neo4j Credentials [HR]

  • 3.2.1 Use default or easily guessable credentials. [CN]
    • Description: Attackers gain access to Neo4j using default or weak credentials.
    • Likelihood: Medium
    • Impact: High
    • Effort: Very Low
    • Skill Level: Novice
    • Detection Difficulty: Easy
    • Mitigation: Strong, unique passwords; never use default credentials; enforce password complexity.

• 3.3 Neo4j Misconfiguration [HR]

  • 3.3.1 Neo4j exposed to the public internet. [CN]
    • Description: The Neo4j database is directly accessible from the public internet without proper authentication or network security controls.
    • Likelihood: Medium
    • Impact: High
    • Effort: Low
    • Skill Level: Novice
    • Detection Difficulty: Easy (with network scanning)
    • Mitigation: Never expose Neo4j directly to the public internet; use a firewall; configure network ACLs; use a VPN or private network.