threat-modeling.md

Threat Model Analysis for mxgmn/wavefunctioncollapse

Threat: Malicious Input Sample - Constraint Violation Injection

• Threat: Malicious Input Sample - Constraint Violation Injection

  • Description: An attacker provides an input sample or configuration that introduces contradictory or impossible constraints to the Wave Function Collapse algorithm. For example, they might specify adjacency rules that cannot be satisfied simultaneously, or define an output size that is incompatible with the input patterns.
  • Impact: The algorithm may fail to converge, leading to a denial-of-service (DoS) condition due to infinite loops or excessive resource consumption. Alternatively, it might produce highly distorted or nonsensical output, rendering the application unusable.
  • Affected Component: adjacency_extraction (or equivalent module responsible for parsing input rules), constraints module (if separate), and the core collapse function.
  • Risk Severity: High
  • Mitigation Strategies:
    • Implement robust constraint validation before starting the generation process. Check for inconsistencies and contradictions in the input rules and parameters using logical checks and potentially a constraint solver.
    • Implement a timeout mechanism to prevent the algorithm from running indefinitely if it fails to converge due to constraint violations.
    • Provide clear error messages to the user or application logs indicating the nature of the constraint violation.

Threat: Library Code Modification - Backdoor Introduction

• Threat: Library Code Modification - Backdoor Introduction

  • Description: An attacker gains access to the application's codebase (or a compromised development environment) and directly modifies the wavefunctioncollapse library's source code to introduce a backdoor. This backdoor could allow the attacker to control the generated output, exfiltrate data related to the generation process, or potentially execute arbitrary code within the context of the application.
  • Impact: Complete compromise of the application's functionality related to output generation. High potential for arbitrary code execution and data breaches, depending on the nature of the backdoor.
  • Affected Component: Any part of the wavefunctioncollapse library or its direct dependencies could be targeted.
  • Risk Severity: Critical
  • Mitigation Strategies:
    • Use a package manager and pin the library to a specific, audited version. Avoid including the library's source code directly in the project unless absolutely necessary (and then, subject it to rigorous security audits).
    • Implement code signing and integrity checks (e.g., using checksums) to detect unauthorized modifications to the library files.
    • Regularly conduct security audits and code reviews of the entire application, including the wavefunctioncollapse library and its dependencies.
    • Employ Software Composition Analysis (SCA) tools to identify known vulnerabilities in the library and its dependencies.

Threat: Dependency Hijacking - Malicious Package

• Threat: Dependency Hijacking - Malicious Package

  • Description: An attacker compromises a direct dependency of the wavefunctioncollapse library and publishes a malicious version to a public package repository (e.g., npm, PyPI). The application, during installation or update, unknowingly installs the malicious dependency.
  • Impact: Similar to direct code modification – potential for arbitrary code execution within the application, data exfiltration, and complete control over the generated output. The attacker gains a foothold within the application through the compromised dependency.
  • Affected Component: Any part of the wavefunctioncollapse library that relies on the compromised dependency. The vulnerability is introduced through the dependency, but the impact is on the wavefunctioncollapse integration.
  • Risk Severity: Critical
  • Mitigation Strategies:
    • Use a package manager and pin all dependencies (including transitive dependencies) to specific, known-good versions. Utilize a lockfile (e.g., package-lock.json, poetry.lock, Pipfile.lock) to ensure consistent and reproducible builds.
    • Regularly audit dependencies for known vulnerabilities using SCA tools.
    • Consider using a private package repository to host trusted and vetted versions of dependencies, reducing reliance on public repositories.
    • Verify the integrity of downloaded packages using checksums or digital signatures, if available from the package repository or the dependency's maintainers.

Threat: Resource Exhaustion - Memory Overload

• Threat: Resource Exhaustion - Memory Overload

  • Description: An attacker provides input (e.g., a very large output size request, a highly complex set of constraints, or a specially crafted set of input samples) that causes the wavefunctioncollapse algorithm to allocate an excessive amount of memory. This leads to a denial-of-service (DoS) condition as the application crashes or becomes unresponsive due to memory exhaustion.
  • Impact: Application crash or unresponsiveness, preventing legitimate users from accessing the application's functionality.
  • Affected Component: The core collapse function and any data structures used to store the wave function, output grid, and intermediate states during the generation process.
  • Risk Severity: High
  • Mitigation Strategies:
    • Set strict and reasonable limits on the maximum output size that the application will allow.
    • Implement memory monitoring during the execution of the wavefunctioncollapse algorithm. Terminate the process if memory usage exceeds predefined, safe limits.
    • Use a memory-efficient implementation of the algorithm. Consider techniques like sparse matrices if the output is expected to be mostly empty or has a regular structure.
    • Profile the memory usage of the library with various inputs to identify potential memory leaks or areas of excessive allocation.

Threat: Resource Exhaustion - CPU Overload (Non-Convergence)

• Threat: Resource Exhaustion - CPU Overload (Non-Convergence)

  • Description: An attacker provides input with contradictory constraints, a poorly defined set of adjacency rules, or a configuration that prevents the wavefunctioncollapse algorithm from converging to a solution. The algorithm continues to run indefinitely (or for a very long time), consuming CPU resources and blocking other operations.
  • Impact: Denial of service (DoS) due to excessive CPU usage. The application becomes unresponsive, and legitimate requests cannot be processed.
  • Affected Component: The core collapse function, specifically the iterative process of selecting tiles, propagating constraints, and backtracking.
  • Risk Severity: High
  • Mitigation Strategies:
    • Implement a strict timeout mechanism. Terminate the collapse function after a predefined maximum execution time, regardless of whether a solution has been found.
    • Implement a maximum iteration count. Stop the algorithm if it doesn't converge within a reasonable and configurable number of iterations.
    • Validate input constraints for contradictions and inconsistencies before starting the generation process. Use logical checks and potentially a constraint solver to ensure the input is well-formed.
    • Provide informative error messages or logging when the algorithm fails to converge, indicating the likely cause (timeout, maximum iterations reached, constraint violation). This aids in debugging and identifying malicious input.