attack-tree.md

Attack Tree Analysis for alexreisner/geocoder

Objective: Degrade performance, leak sensitive data, or manipulate application logic via geocoder

Attack Tree Visualization

Goal: Degrade performance, leak sensitive data, or manipulate application logic via geocoder ├── 1. Denial of Service (DoS) / Resource Exhaustion [HIGH RISK] │ └── 1.1. Overwhelm External API (Rate Limiting) │ └── 1.1.2. Craft many requests using the same geocoding provider [CRITICAL] ├── 2. Information Disclosure (Sensitive Location Data) [HIGH RISK] │ └── 2.3. API Key Leakage │ └── 2.3.1. Exploit vulnerabilities in how the application handles/stores API keys (passed to geocoder). [CRITICAL] └── 3. Logic Manipulation [HIGH RISK] └── 3.1. Injection Attacks (if input is used unsafely) ├── 3.1.1. SQL Injection (if geocoder results are used in database queries without proper sanitization). [CRITICAL] └── 3.1.2. Command Injection (if geocoder results are used in shell commands without proper sanitization). [CRITICAL]

Attack Tree Path: 1. Denial of Service (DoS) / Resource Exhaustion [HIGH RISK]

• 1.1. Overwhelm External API (Rate Limiting)
  • 1.1.2. Craft many requests using the same geocoding provider [CRITICAL]
    • Description: The attacker sends a large number of requests to the geocoding service through the application, exceeding the allowed rate limits or quota. This can lead to the application being temporarily blocked or having its API access suspended.
    • Likelihood: High
    • Impact: Medium (Application slowdown/unavailability, potential API account suspension)
    • Effort: Low
    • Skill Level: Low
    • Detection Difficulty: Medium (API usage spikes can be detected through monitoring)
    • Mitigation:
      • Implement robust rate limiting within the application before calling the geocoder library.
      • Monitor API usage and set alerts for unusual activity.
      • Consider using multiple geocoding providers as a fallback, with proper rate limiting for each.
      • Implement a queuing system to handle bursts of requests gracefully.
      • Use exponential backoff when retrying failed requests due to rate limiting.

Attack Tree Path: 2. Information Disclosure (Sensitive Location Data) [HIGH RISK]

• 2.3. API Key Leakage
  • 2.3.1. Exploit vulnerabilities in how the application handles/stores API keys (passed to geocoder). [CRITICAL]
    • Description: The attacker gains access to the API key used by the application to communicate with the geocoding service. This could be due to vulnerabilities like hardcoded keys in the source code, insecure storage in configuration files, or exposure through error messages or logs.
    • Likelihood: Low (Depends on application's security practices)
    • Impact: High (Unauthorized API usage, potential financial loss, account compromise)
    • Effort: Medium (Requires finding vulnerabilities in the application)
    • Skill Level: Medium
    • Detection Difficulty: Medium (Code review, penetration testing, and log analysis can help detect this)
    • Mitigation:
      • Never hardcode API keys in the application code.
      • Store API keys securely using environment variables, a secrets manager (e.g., AWS Secrets Manager, HashiCorp Vault), or encrypted configuration files.
      • Restrict access to API keys on a need-to-know basis.
      • Regularly rotate API keys.
      • Review error handling and logging to ensure API keys are never exposed.
      • Implement monitoring and alerting for unauthorized API key usage.

Attack Tree Path: 3. Logic Manipulation [HIGH RISK]

• 3.1. Injection Attacks (if input is used unsafely)

  • 3.1.1. SQL Injection (if geocoder results are used in database queries without proper sanitization). [CRITICAL]

    • Description: The attacker crafts malicious input that, when combined with the geocoding results and used in a database query, alters the query's logic. This can allow the attacker to read, modify, or delete data in the database.
    • Likelihood: Low (Depends on application's database interaction)
    • Impact: Very High (Data breach, data modification, complete database compromise)
    • Effort: Medium (Requires finding vulnerable SQL queries)
    • Skill Level: Medium
    • Detection Difficulty: Medium (SQL injection detection tools, code review, and penetration testing can help)
    • Mitigation:
      • Use parameterized queries (prepared statements) exclusively for all database interactions. Never construct SQL queries by directly concatenating strings with user-supplied data or data from external sources (like the geocoder).
      • Implement input validation and sanitization to ensure that data received from the geocoder conforms to expected formats and constraints.
      • Use a database user with the least privileges necessary.
      • Regularly update database software to patch known vulnerabilities.

  • 3.1.2. Command Injection (if geocoder results are used in shell commands without proper sanitization). [CRITICAL]

    • Description: The attacker crafts malicious input that, when combined with the geocoding results and used in a shell command, executes arbitrary commands on the server.
    • Likelihood: Low (Depends on application's use of shell commands)
    • Impact: Very High (Arbitrary code execution, system compromise)
    • Effort: Medium (Requires finding vulnerable command executions)
    • Skill Level: Medium
    • Detection Difficulty: Medium (Code review and penetration testing can help)
    • Mitigation:
      • Avoid using shell commands whenever possible. If you must use them, use a safe API that allows you to pass arguments separately from the command itself (e.g., exec.Command in Go with separate arguments, rather than building a command string).
      • Never construct shell commands by directly concatenating strings with user-supplied data or data from external sources.
      • Implement strict input validation and sanitization.
      • Run the application with the least privileges necessary.