General Software Weaknesses that Appear in Products that Use or Support AI/ML Technology

Opis

This category lists general software weaknesses in software that insecurely uses AI/ML components, but frequently appear in many kinds of software products that do not use AI/ML.

Wpisy w tej grupie

• CWE-22 Improper Limitation of a Pathname to a Restricted Directory ('Path Traversal')
• CWE-77 Improper Neutralization of Special Elements used in a Command ('Command Injection')
• CWE-78 Improper Neutralization of Special Elements used in an OS Command ('OS Command Injection')
• CWE-79 Improper Neutralization of Input During Web Page Generation ('Cross-site Scripting')
• CWE-94 Improper Control of Generation of Code ('Code Injection')
• CWE-95 Improper Neutralization of Directives in Dynamically Evaluated Code ('Eval Injection')
• CWE-116 Improper Encoding or Escaping of Output
• CWE-250 Execution with Unnecessary Privileges
• CWE-434 Unrestricted Upload of File with Dangerous Type
• CWE-502 Deserialization of Untrusted Data
• CWE-862 Missing Authorization
• CWE-918 Server-Side Request Forgery (SSRF)
• CWE-1336 Improper Neutralization of Special Elements Used in a Template Engine
• CWE-1426 Improper Validation of Generative AI Output
• CWE-1427 Improper Neutralization of Input Used for LLM Prompting
• CWE-1434 Insecure Setting of Generative AI/ML Model Inference Parameters

Uwagi

This category is likely to be updated frequently in future versions. See Research Gaps.

As of CWE 4.20, it is still difficult to distinguish common AI/ML related attacks from the underlying weaknesses. The CWE AI Working Group has had many discussions about this general topic. Much of the latest research has focused on the attacks, and/or characterizing the underlying design and implementation of AI/ML related systems. From a CWE perspective, the distinction between "control" and "data" is not necessarily as deep as currently considered within the AI/ML community, since most weaknesses are characterized in terms of potentially insecure "behavior" - whether that behavior occurred due to design, insecure code, insecure configuration, or data-driven behaviors such as AI/ML. Since AI/ML is frequently derived from repositories of software that consume AI/ML components - many public reports of AI/ML vulnerabilities ultimately result from commonly-occurring weaknesses that appear in most kinds of software. There are several weakness-focused research efforts within the industry, but these efforts are still in the early stages.

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