The product generates a query intended to access or manipulate data in a data store such as a database, but it does not neutralize or incorrectly neutralizes special elements that can modify the intended logic of the query.

Depending on the capabilities of the query language, an attacker could inject additional logic into the query to:

Modify the intended selection criteria, thus changing which data entities (e.g., records) are returned, modified, or otherwise manipulated
Append additional commands to the query
Return more entities than intended
Return fewer entities than intended
Cause entities to be sorted in an unexpected way

The ability to execute additional commands or change which entities are returned has obvious risks. But when the product logic depends on the order or number of entities, this can also lead to vulnerabilities. For example, if the query expects to return only one entity that specifies an administrative user, but an attacker can change which entities are returned, this could cause the logic to return information for a regular user and incorrectly assume that the user has administrative privileges.
While this weakness is most commonly associated with SQL injection, there are many other query languages that are also subject to injection attacks, including HTSQL, LDAP, DQL, XQuery, Xpath, and "NoSQL" languages.

Primary

NoSQL Injection, NoSQLi
Injection of data query language into NoSQL databases such as Cassandra, ElasticSearch, MongoDB, Redis, and others

Implementation
REALIZATION: This weakness is caused during implementation of an architectural security tactic.

Confidentiality
Integrity
Availability
Access Control
Bypass Protection Mechanism
Read Application Data
Modify Application Data
Varies by Context

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.)
High

The following code dynamically constructs and executes a SQL query that searches for items matching a specified name. The query restricts the items displayed to those where owner matches the user name of the currently-authenticated user.

...string userName = ctx.getAuthenticatedUserName();string query = "SELECT * FROM items WHERE owner = '" + userName + "' AND itemname = '" + ItemName.Text + "'";sda = new SqlDataAdapter(query, conn);DataTable dt = new DataTable();sda.Fill(dt);...

The query that this code intends to execute follows:

SELECT * FROM items WHERE owner = <userName> AND itemname = <itemName>;

However, because the query is constructed dynamically by concatenating a constant base query string and a user input string, the query only behaves correctly if itemName does not contain a single-quote character. If an attacker with the user name wiley enters the string:

name' OR 'a'='a

for itemName, then the query becomes the following:

SELECT * FROM items WHERE owner = 'wiley' AND itemname = 'name' OR 'a'='a';

The addition of the:

OR 'a'='a

condition causes the WHERE clause to always evaluate to true, so the query becomes logically equivalent to the much simpler query:

SELECT * FROM items;

This simplification of the query allows the attacker to bypass the requirement that the query only return items owned by the authenticated user; the query now returns all entries stored in the items table, regardless of their specified owner.

The code below constructs an LDAP query using user input address data:

context = new InitialDirContext(env);String searchFilter = "StreetAddress=" + address;NamingEnumeration answer = context.search(searchBase, searchFilter, searchCtls);

Because the code fails to neutralize the address string used to construct the query, an attacker can supply an address that includes additional LDAP queries.

Consider the following simple XML document that stores authentication information and a snippet of Java code that uses XPath query to retrieve authentication information:

<users><user><login>john</login><password>abracadabra</password><home_dir>/home/john</home_dir></user><user><login>cbc</login><password>1mgr8</password><home_dir>/home/cbc</home_dir></user></users>

The Java code used to retrieve the home directory based on the provided credentials is:

XPath xpath = XPathFactory.newInstance().newXPath();XPathExpression xlogin = xpath.compile("//users/user[login/text()='" + login.getUserName() + "' and password/text() = '" + login.getPassword() + "']/home_dir/text()");Document d = DocumentBuilderFactory.newInstance().newDocumentBuilder().parse(new File("db.xml"));String homedir = xlogin.evaluate(d);

Assume that user "john" wishes to leverage XPath Injection and login without a valid password. By providing a username "john" and password "' or ''='" the XPath expression now becomes

//users/user[login/text()='john' or ''='' and password/text() = '' or ''='']/home_dir/text()

This lets user "john" login without a valid password, thus bypassing authentication.

CVE-2024-50672
NoSQL injection in product for building eLearning courses allows password resets using a query processed by the Mongoose find function
https://www.cve.org/CVERecord?id=CVE-2024-50672

CVE-2021-20736
NoSQL injection in team collaboration product
https://www.cve.org/CVERecord?id=CVE-2021-20736

CVE-2020-35666
NoSQL injection in a PaaS platform using a MongoDB operator
https://www.cve.org/CVERecord?id=CVE-2020-35666

CVE-2014-2503
Injection using Documentum Query Language (DQL)
https://www.cve.org/CVERecord?id=CVE-2014-2503

CVE-2014-2508
Injection using Documentum Query Language (DQL)
https://www.cve.org/CVERecord?id=CVE-2014-2508

Allowed-with-Review
This CWE entry is a Class and might have Base-level children that would be more appropriate
Examine children of this entry to see if there is a better fit

It could be argued that data query languages are effectively a command language - albeit with a limited set of commands - and thus any query-language injection issue could be treated as a child of CWE-74. However, CWE-943 is intended to better organize query-oriented issues to separate them from fully-functioning programming languages, and also to provide a more precise identifier for the many query languages that do not have their own CWE identifier.

CWE Content Team
MITRE
2014-06-19
2.7
2014-06-23

CWE Content Team
MITRE
2015-12-07
updated Relationships

CWE Content Team
MITRE
2017-11-08
updated Modes_of_Introduction, Observed_Examples, Relationships

CWE Content Team
MITRE
2018-03-27
updated Relationships

CWE Content Team
MITRE
2019-06-20
updated Relationships

CWE Content Team
MITRE
2020-02-24
updated Relationships

CWE Content Team
MITRE
2021-03-15
updated Maintenance_Notes

CWE Content Team
MITRE
2022-04-28
updated Related_Attack_Patterns

CWE Content Team
MITRE
2023-01-31
updated Description

CWE Content Team
MITRE
2023-04-27
updated Detection_Factors, Relationships

CWE Content Team
MITRE
2023-06-29
updated Mapping_Notes

CWE Content Team
MITRE
2024-02-29
4.14
2024-02-29
updated Demonstrative_Examples

CWE Content Team
MITRE
2025-04-03
4.17
2025-04-03
updated Alternate_Terms, Observed_Examples, References

CWE Content Team
MITRE
2025-12-11
4.19
2025-12-11
updated Weakness_Ordinalities