CWE-74: Improper Neutralization of Special Elements in Output Used by a Downstream Component (‘Injection’)
| Abstraction | Structure | Status |
|---|---|---|
| None | Simple | Incomplete |
Description
The product constructs all or part of a command, data structure, or record using externally-influenced input from an upstream component, but it does not neutralize or incorrectly neutralizes special elements that could modify how it is parsed or interpreted when it is sent to a downstream component.
Related Weaknesses
| Nature | ID | View ID | Name |
|---|---|---|---|
| ChildOf | CWE-707 | 1000 | Improper Neutralization |
Modes of Introduction
| Phase | Note |
|---|---|
| Implementation | REALIZATION: This weakness is caused during implementation of an architectural security tactic. |
Applicable Platforms
Languages
Class: Not Language-Specific
Technologies
Likelihood Of Exploit
High
Common Consequences
| Scope | Impact | Note |
|---|---|---|
| Confidentiality | Read Application Data | Many injection attacks involve the disclosure of important information – in terms of both data sensitivity and usefulness in further exploitation. |
| Access Control | Bypass Protection Mechanism | In some cases, injectable code controls authentication; this may lead to a remote vulnerability. |
| Other | Alter Execution Logic | Injection attacks are characterized by the ability to significantly change the flow of a given process, and in some cases, to the execution of arbitrary code. |
| Integrity, Other | Other | Data injection attacks lead to loss of data integrity in nearly all cases as the control-plane data injected is always incidental to data recall or writing. |
| Non-Repudiation | Hide Activities | Often the actions performed by injected control code are unlogged. |
Detection Methods
Automated Static Analysis
Automated static analysis, commonly referred to as Static Application Security Testing (SAST), can find some instances of this weakness by analyzing source code (or binary/compiled code) without having to execute it. Typically, this is done by building a model of data flow and control flow, then searching for potentially-vulnerable patterns that connect “sources” (origins of input) with “sinks” (destinations where the data interacts with external components, a lower layer such as the OS, etc.)
Effectiveness: High
Potential Mitigations
Requirements
Programming languages and supporting technologies might be chosen which are not subject to these issues.
Implementation
Utilize an appropriate mix of allowlist and denylist parsing to filter control-plane syntax from all input.
Observed Examples
- CVE-2024-5184: API service using a large generative AI model allows direct prompt injection to leak hard-coded system prompts or execute other prompts.
- CVE-2022-36069: Python-based dependency management tool avoids OS command injection when generating Git commands but allows injection of optional arguments with input beginning with a dash (CWE-88), potentially allowing for code execution.
- CVE-1999-0067: Canonical example of OS command injection. CGI program does not neutralize “|” metacharacter when invoking a phonebook program.
- CVE-2022-1509: injection of sed script syntax (“sed injection”)
- CVE-2020-9054: Chain: improper input validation (CWE-20) in username parameter, leading to OS command injection (CWE-78), as exploited in the wild per CISA KEV.
- CVE-2021-44228: Product does not neutralize ${xyz} style expressions, allowing remote code execution. (log4shell vulnerability)