Taxonomy

Introducing a Comprehensive Python Vulnerability Taxonomy

Welcome to our community page dedicated to presenting and discussing our latest research on Python vulnerability classification. Our goal is to provide a detailed and systematic approach to understanding and categorizing Python vulnerabilities, fostering collaboration and continuous improvement.

Background and Motivation

In the ever-evolving field of software security, identifying and categorizing vulnerabilities is crucial for developing effective mitigation strategies. Our research builds upon established taxonomies such as Orthogonal Defect Classification (ODC) and Code Defects Classification (CDC), tailored specifically to address the intricacies of Python vulnerabilities.

Taxonomy Overview

Our taxonomy for Python vulnerabilities is designed to provide a comprehensive and detailed classification system, facilitating better understanding, detection, and mitigation of security issues. The taxonomy consists of 10 overarching categories and 41 subcategories, ensuring a nuanced and granular classification of each vulnerability.

Main Categories
  1. Input Validation and Sanitization: Issues related to improper validation or sanitization of user inputs.
  2. Authentication, Authorization, and Session Management: Vulnerabilities affecting authentication mechanisms, user authorization processes, and session management.
  3. Cryptographic: Issues related to cryptographic operations, including encryption, decryption, and key management.
  4. Design Defects: Flaws originating from poor software design decisions.
  5. Configuration Issues: Problems arising from improper software configuration.
  6. Memory Corruption: Vulnerabilities that lead to memory corruption, such as buffer overflows.
  7. Information Leakage: Issues that result in unintended exposure of sensitive information.
  8. Race Condition: Vulnerabilities caused by race conditions in software execution.
  9. Resource Management: Issues related to improper management of system resources.
  10. Numeric Errors: Vulnerabilities arising from improper handling of numeric operations.

Subcategories Example

Each main category is further divided into specific subcategories. For example, within the Cryptographic category, we have subcategories such as:

  • Improper SSL/TLS Certificate Validation
  • Weak Encryption Algorithm

View full list of categories and subcategories
Input Validation and Sanitization
Command Injection

Injection of arbitrary commands into user input.

SQL Injection

Improper sanitization of SQL queries leading to injection attacks.

Insecure Direct Object References (IDOR)

Unauthorized access to objects by manipulating references.

Path Traversal

Improper validation of file paths allowing unauthorized access to directories.

Insecure Parsing or Deserialization

Security issues during deserialization or parsing of data.

Authentication, Authorization, and Session Management
Weak Password Policy

Use of weak or easily guessable passwords.

Insecure Authentication Mechanisms

Flaws in the authentication process.

Session Management Issues

Vulnerabilities related to session handling and management.

Privilege Escalation

Unauthorized elevation of user privileges.

Cryptographic
Unencryped communication

Plain-text communication allows sniffing of sensitive data.

Weak Encryption Algorithm

Weak encryption of sensitive data.

Inadequate random number generation

Generation of inadequate random numbers.

Improper SSL/TLS Certificate Validation

Improper validation of SSL/TLS Certificates.

Cryptographic Implementation Error

Vulnerabilities related to mistakes or flaws in cryptographic algorithms, methods, or libraries.

Design Defects
Inadequate Error Handling

Insufficient handling of unexpected errors or exceptions, potentially exposing sensitive information or causing system instability.

Vulnerable and Outdated Componentes

Outdated and deprecated components that introduce a known vulnerability.

Poorly Designed Access Controls

Flaws in how the system manages user privileges and permissions, leading to unauthorized access.

Security Misconfigurations (PROPOSED TO BE MOVED TO “CONFIGURATION ISSUES”)

Insecure configuration leading to vulnerabilities.

Configuration Issues
Cross-Site Scripting (XSS)

Injecting malicious code into web apps to compromise user data or actions.

Cross-Site Request Forgery (CSRF)

Unauthorized execution of actions through forged requests.

Remote File Inclusion (RFI)

Inclusion of remote files in web applications.

Local File Inclusion (LFI)

Inclusion of local files in web applications.

Open Redirects

Improper handling of redirection URLs.

Server-Side Request Forgery (SSRF)

Tricking the server to make unauthorized requests.

Dynamic Link Library (DLL) Loading Issues

Improper handling of dynamic libraries, potentially allowing malicious DLLs to be loaded and executed.

Memory Corruption
Buffer Overflows

Occurs when a program writes more data to a buffer than it can hold, potentially overwriting adjacent memory.

Out-of-Bound Accesses

Involves accessing memory locations outside the allocated boundaries, often leading to unintended consequences.

Use-After-Free

Refers to using memory after it has been deallocated, potentially causing unpredictable behavior or vulnerabilities.

Information Leakage
Information Disclosure

Accidental exposure of sensitive information related to a system.

Insecure Handling of Sensitive Data

Mishandling and exposure of sensitive information related to a user.

Race Condition
Time-of-Check to Time-of-Use (TOCTOU)

Situations where the state of a resource changes between the time it is checked and the time it is used, leading to unexpected behavior.

Data Race Conditions in Threads

Occur when multiple threads or processes concurrently access and modify shared data, potentially resulting in unpredictable outcomes.

Race Condition in File Operations

Race conditions that specifically affect file operations, which may result in security vulnerabilities when handling files.

Resource Management
File Handle Leaks

Failure to release file handles after use, potentially leading to resource exhaustion or security vulnerabilities.

Socket Handle Leaks

Neglecting to close network socket handles, which can result in resource depletion or potential security issues.

Memory Leaks

Failing to deallocate memory properly, causing the program to consume excessive memory resources.

Resource Exhaustion

Depleting system resources, such as CPU, memory, or network connec- tions, due to poor resource management, potentially leading to system instability or denial of service.

Numeric Errors
Integer Overflow

Occur when integer variables exceed their maximum values, often leading to unexpected or insecure behavior.

Rounding Errors

Result from imprecise rounding of numerical values, potentially causing discrepancies in calculations.

Floating-Point Precision Issues

Stem from the finite precision of floating-point numbers, potentially causing inaccuracies in mathematical operations.

Arithmetic Errors

Involve mistakes in numerical calculations, which can lead to unintended results or vulnerabilities in software.

Methodology

Our methodology for developing this taxonomy involved the following steps:

  1. Compilation of Vulnerabilities: We compiled a list of vulnerabilities from various online resources, including CVE identifiers, descriptions, publication dates, and risk scores.
  2. Systematic Characterization: Using established taxonomies like ODC and CDC, we characterized each vulnerability based on its attributes.
  3. Accessibility Scope Classification: We categorized vulnerabilities by their accessibility scope (local or remote).
  4. AI-in-the-Loop (AIiTL) Approach: We employed AI models to assist in verifying each vulnerability’s categorization and generating vulnerable and patched code samples.
  5. Community Collaboration: Our platform allows the community to review and suggest modifications to the classifications, ensuring continuous improvement and accuracy.
VAITP methodology overview

Fig. 1 – Methodology overview

Community Involvement

We invite the community to contribute to our project by reviewing the classifications and suggesting modifications. Your contributions will help enhance the accuracy of our taxonomy and keep it up to date with the latest security trends.

How to Contribute

  1. Review Classifications: Visit our website to review the current classifications.
  2. Submit Suggestions: Use the provided forms to submit your suggestions for modifications.
  3. Contribute Code Examples: You can also contribute new vulnerability code examples to our GitHub repository. These examples will be automatically loaded and presented on our website.
  4. Support the project by donating ETH:
    0xe8D4856d625C7aDBc8017c05C29d28E60145Bcc9

Final notes

Our comprehensive Python vulnerability taxonomy aims to be a step towards better understanding and mitigating security issues in Python software. By collaborating with the community, we aim to continuously refine and improve this taxonomy, ensuring it remains relevant and accurate.

We encourage you to explore our research, review the taxonomy, and contribute to the ongoing effort to enhance Python security.

Thank you!

_Naghmeh Ivaki
_Frรฉdรฉric Bogaerts
_Josรฉ Fonseca