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Attack Tree Analysis for pandas-dev/pandas

Objective: Compromise Application Using Pandas

Attack Tree Visualization

[CRITICAL NODE] Compromise Application Using Pandas [CRITICAL NODE] ├───(OR) [HIGH-RISK PATH] Exploit Data Input Vulnerabilities │ ├───(OR) [HIGH-RISK PATH] Malicious File Input │ │ ├───(AND) [CRITICAL NODE] Pickle Deserialization Vulnerability [CRITICAL NODE] │ │ │ ├─── Attacker provides malicious pickled data │ │ │ └─── Application uses pd.read_pickle() on attacker-controlled data │ │ ├───(AND) [CRITICAL NODE] Exploiting Vulnerabilities in Underlying Parsing Libraries [CRITICAL NODE] │ │ │ ├─── Pandas relies on libraries like openpyxl, lxml, fastparquet, etc. │ │ │ ├─── Vulnerabilities exist in these underlying libraries │ │ │ └─── Application uses pandas to parse data formats handled by vulnerable underlying libraries with attacker-controlled input │ │ └───(AND) Path Traversal via File Paths in Data Input │ │ ├─── Attacker provides file paths containing path traversal sequences │ │ └─── Application uses pandas file functions without proper validation │ ├───(OR) [HIGH-RISK PATH] Malicious Data Source Manipulation │ │ ├───(AND) [HIGH-RISK PATH] Server-Side Request Forgery (SSRF) via URL input │ │ │ ├─── Application uses pandas URL functions │ │ │ ├─── Attacker controls or influences the URL │ │ │ └─── Attacker crafts a malicious URL leading to SSRF │ │ └───(AND) [HIGH-RISK PATH - if applicable] SQL Injection via Pandas SQL Querying │ │ ├─── Application uses pandas SQL functions │ │ ├─── Attacker can inject malicious SQL code │ │ └─── Pandas executes the crafted SQL query

Attack Tree Path: 1. [CRITICAL NODE] Compromise Application Using Pandas [CRITICAL NODE]

  • Description: The ultimate goal of the attacker is to compromise the application using pandas. Success here means the attacker has achieved unauthorized access, control, or caused harm.
  • Likelihood: Varies depending on application security posture, but inherent risks exist due to pandas vulnerabilities.
  • Impact: Critical - Full compromise of the application and potentially underlying systems.
  • Effort: Varies greatly depending on the chosen attack path and application security.
  • Skill Level: Varies greatly depending on the chosen attack path and application security.
  • Detection Difficulty: Varies greatly depending on the chosen attack path and application security.
  • Actionable Insight: Implement comprehensive security measures across all attack vectors identified in this analysis. Focus on secure coding practices, input validation, dependency management, and robust testing.

Attack Tree Path: 2. [HIGH-RISK PATH] Exploit Data Input Vulnerabilities

  • Description: Attackers target vulnerabilities arising from how the application handles external data input processed by pandas.
  • Likelihood: High - Input vulnerabilities are a common attack vector in web applications.
  • Impact: Medium to Critical - Ranges from data manipulation to Remote Code Execution.
  • Effort: Low to Medium - Exploiting input vulnerabilities can be relatively easy.
  • Skill Level: Low to Medium - Basic understanding of input validation bypass techniques.
  • Detection Difficulty: Medium - Can be detected with proper input validation and security testing.
  • Actionable Insight: Implement strict input validation and sanitization for all data processed by pandas, especially data originating from untrusted sources. Follow the principle of least privilege and minimize the application's reliance on external data without proper security checks.

Attack Tree Path: 3. [HIGH-RISK PATH] Malicious File Input

  • Description: Attackers provide malicious files (CSV, Excel, Pickle, etc.) to the application, exploiting vulnerabilities in pandas file reading functions.
  • Likelihood: Medium to High - File uploads and processing are common application features.
  • Impact: Medium to Critical - Ranges from Formula Injection, Path Traversal to Remote Code Execution (Pickle).
  • Effort: Low to Medium - Crafting malicious files is generally not difficult.
  • Skill Level: Low to Medium - Basic understanding of file formats and common vulnerabilities.
  • Detection Difficulty: Medium - Requires file content inspection and robust input validation.
  • Actionable Insight: Treat all file inputs as untrusted. Implement file type validation, content sanitization, and secure file processing practices. Avoid using pd.read_pickle() on untrusted data.

Attack Tree Path: 4. [CRITICAL NODE] Pickle Deserialization Vulnerability [CRITICAL NODE]

  • Description: Exploiting Python's pickle deserialization vulnerability through pd.read_pickle().
    • Attack Step 1: Attacker provides malicious pickled data.
      • Likelihood: Medium
      • Impact: Critical
      • Effort: Medium
      • Skill Level: Medium
      • Detection Difficulty: Hard
    • Attack Step 2: Application uses pd.read_pickle() on attacker-controlled data.
      • Likelihood: High
      • Impact: Critical (if pickle is exploited)
      • Effort: Low
      • Skill Level: Low
      • Detection Difficulty: Easy (code review)
  • Actionable Insight: Avoid using pd.read_pickle() on untrusted data. Use safer serialization formats like CSV or JSON when dealing with external input. If pickle is absolutely necessary, implement strong input validation and consider using safer deserialization alternatives if available.

Attack Tree Path: 5. [CRITICAL NODE] Exploiting Vulnerabilities in Underlying Parsing Libraries [CRITICAL NODE]

  • Description: Exploiting known or zero-day vulnerabilities in libraries pandas depends on (e.g., openpyxl, lxml, fastparquet).
    • Attack Step 1: Vulnerabilities exist in underlying libraries.
      • Likelihood: Low to Medium (depends on library and vulnerability)
      • Impact: Medium to Critical (DoS, RCE, Information Disclosure)
      • Effort: Medium to High
      • Skill Level: Medium to High
      • Detection Difficulty: Hard to Very Hard
    • Attack Step 2: Application uses pandas to parse data formats handled by vulnerable libraries with attacker-controlled input.
      • Likelihood: Medium
      • Impact: Medium to Critical (depends on vulnerability)
      • Effort: Low
      • Skill Level: Low
      • Detection Difficulty: Easy (vulnerability scanners)
  • Actionable Insight: Keep pandas and all its dependencies updated to the latest versions. Regularly monitor security advisories for pandas and its dependencies. Utilize security scanning tools to identify vulnerable dependencies and prioritize patching.

Attack Tree Path: 6. Path Traversal via File Paths in Data Input

  • Description: Attackers inject path traversal sequences in file paths provided as input to pandas file reading functions.
    • Attack Step 1: Attacker provides file paths containing path traversal sequences.
      • Likelihood: Medium
      • Impact: Medium to High
      • Effort: Low
      • Skill Level: Low
      • Detection Difficulty: Medium
    • Attack Step 2: Application uses pandas file functions without proper validation.
      • Likelihood: Medium
      • Impact: Medium to High (Path Traversal)
      • Effort: Low
      • Skill Level: Low
      • Detection Difficulty: Medium
  • Actionable Insight: Validate and sanitize all file paths provided as input to pandas functions. Ensure paths are within expected directories and prevent traversal to sensitive areas. Use secure path handling functions provided by the operating system or libraries.

Attack Tree Path: 7. [HIGH-RISK PATH] Malicious Data Source Manipulation

  • Description: Attackers manipulate external data sources accessed by pandas, leading to application compromise.
  • Likelihood: Medium - Applications often rely on external data sources.
  • Impact: Medium to High - Ranges from SSRF to Database Compromise (SQL Injection).
  • Effort: Low to Medium - Exploiting data source manipulation can be relatively easy.
  • Skill Level: Low to Medium - Basic understanding of web and database vulnerabilities.
  • Detection Difficulty: Medium - Requires careful validation of external data sources and network monitoring.
  • Actionable Insight: Treat external data sources as potentially untrusted. Implement strict validation and sanitization of data retrieved from external sources. Apply the principle of least privilege to network access and database credentials.

Attack Tree Path: 8. [HIGH-RISK PATH] Server-Side Request Forgery (SSRF) via URL input

  • Description: Exploiting SSRF vulnerabilities by manipulating URLs used in pandas URL reading functions.
    • Attack Step 1: Application uses pandas URL functions.
      • Likelihood: Medium
      • Impact: Medium to High
      • Effort: Low
      • Skill Level: Low
      • Detection Difficulty: Medium
    • Attack Step 2: Attacker controls or influences the URL.
    • Attack Step 3: Attacker crafts a malicious URL leading to SSRF.
      • Likelihood: Medium
      • Impact: Medium to High (SSRF)
      • Effort: Low
      • Skill Level: Low
      • Detection Difficulty: Medium
  • Actionable Insight: Validate and sanitize all URLs provided as input to pandas functions. Implement allow-lists for acceptable domains and protocols. Restrict network access from the application server to only necessary external resources.

Attack Tree Path: 9. [HIGH-RISK PATH - if applicable] SQL Injection via Pandas SQL Querying

  • Description: Exploiting SQL Injection vulnerabilities if the application uses pandas for database interaction.
    • Attack Step 1: Application uses pandas SQL functions.
      • Likelihood: Low to Medium
      • Impact: High
      • Effort: Medium
      • Skill Level: Medium
      • Detection Difficulty: Medium
    • Attack Step 2: Attacker can inject malicious SQL code.
    • Attack Step 3: Pandas executes the crafted SQL query.
      • Likelihood: Medium
      • Impact: High (SQL Injection)
      • Effort: Low
      • Skill Level: Low
      • Detection Difficulty: Medium
  • Actionable Insight: Use parameterized queries or ORM instead of string concatenation for building SQL queries in pandas. Sanitize and validate user inputs used in SQL queries. Apply the principle of least privilege to database user credentials used by the application.