cumulative-distribution-function is an open source npm library used which calculates statistical cumulative distribution function from data array of x values. In versions prior to 2.0.0 apps using this library on improper data may crash or go into an infinite-loop. In the case of a nodejs server-app using this library to act on invalid non-numeric data, the nodejs server may crash. This may affect other users of this server and/or require the server to be rebooted for proper operation. In the case of a browser app using this library to act on invalid non-numeric data, that browser may crash or lock up. A flaw enabling an infinite-loop was discovered in the code for evaluating the cumulative-distribution-function of input data. Although the documentation explains that numeric data is required, some users may confuse an array of strings like [1,2,3,4,5] for numeric data [1,2,3,4,5] when it is in fact string data. An infinite loop is possible when the cumulative-distribution-function is evaluated for a given point when the input data is string data rather than type number. This vulnerability enables an infinite-cpu-loop denial-of-service-attack on any app using npm:cumulative-distribution-function v1.0.3 or earlier if the attacker can supply malformed data to the library. The vulnerability could also manifest if a data source to be analyzed changes data type from Arrays of number (proper) to Arrays of string (invalid, but undetected by earlier version of the library). Users should upgrade to at least v2.0.0, or the latest version. Tests for several types of invalid data have been created, and version 2.0.0 has been tested to reject this invalid data by throwing a TypeError() instead of processing it. Developers using this library may wish to adjust their apps code slightly to better tolerate or handle this TypeError. Apps performing proper numeric data validation before sending data to this library should be mostly unaffected by this patch. The vulnerability can be mitigated in older versions by ensuring that only finite numeric data of type Array[number] or number is passed to cumulative-distribution-function and its f(x) function, respectively.
The product receives input or data, but it does not validate or incorrectly validates that the input has the properties that are required to process the data safely and correctly.
| Name | Vendor | Start Version | End Version |
|---|---|---|---|
| Cumulative-distribution-function | Cumulative-distribution-function_project | * | 2.0.0 (excluding) |
Input validation is a frequently-used technique for checking potentially dangerous inputs in order to ensure that the inputs are safe for processing within the code, or when communicating with other components. When software does not validate input properly, an attacker is able to craft the input in a form that is not expected by the rest of the application. This will lead to parts of the system receiving unintended input, which may result in altered control flow, arbitrary control of a resource, or arbitrary code execution. Input validation is not the only technique for processing input, however. Other techniques attempt to transform potentially-dangerous input into something safe, such as filtering (CWE-790) - which attempts to remove dangerous inputs - or encoding/escaping (CWE-116), which attempts to ensure that the input is not misinterpreted when it is included in output to another component. Other techniques exist as well (see CWE-138 for more examples.) Input validation can be applied to:
Data can be simple or structured. Structured data can be composed of many nested layers, composed of combinations of metadata and raw data, with other simple or structured data. Many properties of raw data or metadata may need to be validated upon entry into the code, such as:
Implied or derived properties of data must often be calculated or inferred by the code itself. Errors in deriving properties may be considered a contributing factor to improper input validation.
Note that “input validation” has very different meanings to different people, or within different classification schemes. Caution must be used when referencing this CWE entry or mapping to it. For example, some weaknesses might involve inadvertently giving control to an attacker over an input when they should not be able to provide an input at all, but sometimes this is referred to as input validation. Finally, it is important to emphasize that the distinctions between input validation and output escaping are often blurred, and developers must be careful to understand the difference, including how input validation is not always sufficient to prevent vulnerabilities, especially when less stringent data types must be supported, such as free-form text. Consider a SQL injection scenario in which a person’s last name is inserted into a query. The name “O’Reilly” would likely pass the validation step since it is a common last name in the English language. However, this valid name cannot be directly inserted into the database because it contains the “’” apostrophe character, which would need to be escaped or otherwise transformed. In this case, removing the apostrophe might reduce the risk of SQL injection, but it would produce incorrect behavior because the wrong name would be recorded.