attack-surface.md

Attack Surface Analysis for bradlarson/gpuimage

Attack Surface: Malformed Image/Video File Processing

• Description: Vulnerabilities arising from processing maliciously crafted or corrupted image and video files specifically within GPUImage's decoding and processing logic.
• GPUImage Contribution: GPUImage's core function is to decode and process image and video formats. Vulnerabilities in its internal routines for handling these formats are directly exploitable.
• Example: An attacker uploads a specially crafted PNG image to an application using GPUImage. This image exploits a buffer overflow vulnerability within GPUImage's PNG decoding code, leading to arbitrary code execution when processed.
• Impact:
  • Application crash (Denial of Service)
  • Memory corruption
  • Remote Code Execution
• Risk Severity: Critical
• Mitigation Strategies:
  • Regularly Update GPUImage: Crucially important to get fixes for vulnerabilities in file processing.
  • Input Validation (Basic): Perform basic file header checks and size limits before passing to GPUImage as a first line of defense, but rely primarily on GPUImage's robustness.
  • Sandboxing: For processing truly untrusted files, sandboxing GPUImage processing is a strong mitigation to limit the impact of potential exploits within GPUImage's file handling.

Attack Surface: Shader Injection (Indirect)

• Description: Although GPUImage primarily uses pre-defined filters, vulnerabilities can arise if the application allows any form of user-controlled shader customization or extension, even indirectly, leading to the execution of malicious shaders within GPUImage's rendering pipeline.
• GPUImage Contribution: If the application design allows users to influence filter behavior in ways that translate to shader modifications processed by GPUImage, it creates a shader injection attack surface that directly leverages GPUImage's shader execution capabilities.
• Example: An application uses GPUImage and allows users to load "filter packs" from external files. These filter packs are processed in a way that allows an attacker to inject malicious shader code disguised as filter parameters. This malicious shader, when executed by GPUImage's rendering engine, gains unauthorized access to GPU memory.
• Impact:
  • GPU crashes or instability
  • Access to GPU memory (information disclosure)
  • Potentially system-level compromise if shader vulnerabilities are severe.
• Risk Severity: High to Critical (if shader injection is possible through application design leveraging GPUImage)
• Mitigation Strategies:
  • Avoid User-Controlled Shader Customization: The most effective mitigation is to strictly avoid allowing users to influence shader code in any way when using GPUImage.
  • Extremely Strict Input Validation for Filter Configurations: If filter configurations are absolutely necessary, implement extremely rigorous validation to prevent any form of code injection. Treat external filter configurations as highly untrusted.
  • Code Review of Filter Extension Mechanisms: Thoroughly review any application code that handles filter extensions or configurations to eliminate injection vulnerabilities.

Attack Surface: Code Quality and Bugs within GPUImage

• Description: General software bugs and vulnerabilities that may exist directly within the GPUImage library's codebase, leading to exploitable conditions during its operation.
• GPUImage Contribution: As a software library, GPUImage is susceptible to coding errors. Bugs within its filter implementations, memory management, or core processing logic are direct vulnerabilities introduced by using GPUImage.
• Example: A buffer overflow vulnerability exists in a specific filter implementation within GPUImage's code. An attacker triggers this filter with specific input parameters, causing the buffer overflow during GPUImage's processing and potentially achieving code execution.
• Impact:
  • Application crash
  • Memory corruption
  • Remote Code Execution
  • Information Disclosure
• Risk Severity: High to Critical (depending on the nature and exploitability of bugs within GPUImage)
• Mitigation Strategies:
  • Regularly Update GPUImage: Essential to receive bug fixes and security patches from the library maintainers.
  • Code Review (If Possible & Focused): If resources allow, focus code review efforts on critical sections of GPUImage's code, particularly filter implementations and memory handling, to proactively identify potential bugs.
  • Fuzzing (If Possible & Targeted): If feasible, target fuzzing efforts specifically at GPUImage's filter processing and file handling routines to uncover unexpected behavior or crashes that could indicate vulnerabilities.