Scope | Impact | Likelihood |
---|---|---|
Confidentiality Integrity Availability | Execute Unauthorized Code or Commands Note: Adversaries could execute system commands, typically by changing the SQL statement to redirect output to a file that can then be executed. | |
Confidentiality | Read Application Data Note: Since SQL databases generally hold sensitive data, loss of confidentiality is a frequent problem with SQL injection vulnerabilities. | |
Authentication | Gain Privileges or Assume Identity, Bypass Protection Mechanism Note: If poor SQL commands are used to check user names and passwords or perform other kinds of authentication, it may be possible to connect to the product as another user with no previous knowledge of the password. | |
Access Control | Bypass Protection Mechanism Note: If authorization information is held in a SQL database, it may be possible to change this information through the successful exploitation of a SQL injection vulnerability. | |
Integrity | Modify Application Data Note: Just as it may be possible to read sensitive information, it is also possible to modify or even delete this information with a SQL injection attack. |
Reference | Description |
---|---|
SQL injection in security product dashboard using crafted certificate fields | |
SQL injection in time and billing software, as exploited in the wild per CISA KEV. | |
SQL injection in file-transfer system via a crafted Host header, as exploited in the wild per CISA KEV. | |
SQL injection in firewall product's admin interface or user portal, as exploited in the wild per CISA KEV. | |
An automation system written in Go contains an API that is vulnerable to SQL injection allowing the attacker to read privileged data. | |
chain: SQL injection in library intended for database authentication allows SQL injection and authentication bypass. | |
SQL injection through an ID that was supposed to be numeric. | |
SQL injection through an ID that was supposed to be numeric. | |
SQL injection via user name. | |
SQL injection via user name or password fields. | |
SQL injection in security product, using a crafted group name. | |
SQL injection in authentication library. | |
SQL injection in vulnerability management and reporting tool, using a crafted password. |
Use a vetted library or framework that does not allow this weakness to occur or provides constructs that make this weakness easier to avoid.
For example, consider using persistence layers such as Hibernate or Enterprise Java Beans, which can provide significant protection against SQL injection if used properly.
If available, use structured mechanisms that automatically enforce the separation between data and code. These mechanisms may be able to provide the relevant quoting, encoding, and validation automatically, instead of relying on the developer to provide this capability at every point where output is generated.
Process SQL queries using prepared statements, parameterized queries, or stored procedures. These features should accept parameters or variables and support strong typing. Do not dynamically construct and execute query strings within these features using "exec" or similar functionality, since this may re-introduce the possibility of SQL injection. [REF-867]
Run your code using the lowest privileges that are required to accomplish the necessary tasks [REF-76]. If possible, create isolated accounts with limited privileges that are only used for a single task. That way, a successful attack will not immediately give the attacker access to the rest of the software or its environment. For example, database applications rarely need to run as the database administrator, especially in day-to-day operations.
Specifically, follow the principle of least privilege when creating user accounts to a SQL database. The database users should only have the minimum privileges necessary to use their account. If the requirements of the system indicate that a user can read and modify their own data, then limit their privileges so they cannot read/write others' data. Use the strictest permissions possible on all database objects, such as execute-only for stored procedures.
While it is risky to use dynamically-generated query strings, code, or commands that mix control and data together, sometimes it may be unavoidable. Properly quote arguments and escape any special characters within those arguments. The most conservative approach is to escape or filter all characters that do not pass an extremely strict allowlist (such as everything that is not alphanumeric or white space). If some special characters are still needed, such as white space, wrap each argument in quotes after the escaping/filtering step. Be careful of argument injection (CWE-88).
Instead of building a new implementation, such features may be available in the database or programming language. For example, the Oracle DBMS_ASSERT package can check or enforce that parameters have certain properties that make them less vulnerable to SQL injection. For MySQL, the mysql_real_escape_string() API function is available in both C and PHP.
Assume all input is malicious. Use an "accept known good" input validation strategy, i.e., use a list of acceptable inputs that strictly conform to specifications. Reject any input that does not strictly conform to specifications, or transform it into something that does.
When performing input validation, consider all potentially relevant properties, including length, type of input, the full range of acceptable values, missing or extra inputs, syntax, consistency across related fields, and conformance to business rules. As an example of business rule logic, "boat" may be syntactically valid because it only contains alphanumeric characters, but it is not valid if the input is only expected to contain colors such as "red" or "blue."
Do not rely exclusively on looking for malicious or malformed inputs. This is likely to miss at least one undesirable input, especially if the code's environment changes. This can give attackers enough room to bypass the intended validation. However, denylists can be useful for detecting potential attacks or determining which inputs are so malformed that they should be rejected outright.
When constructing SQL query strings, use stringent allowlists that limit the character set based on the expected value of the parameter in the request. This will indirectly limit the scope of an attack, but this technique is less important than proper output encoding and escaping.
Note that proper output encoding, escaping, and quoting is the most effective solution for preventing SQL injection, although input validation may provide some defense-in-depth. This is because it effectively limits what will appear in output. Input validation will not always prevent SQL injection, especially if you are required to support free-form text fields that could contain arbitrary characters. For example, the name "O'Reilly" would likely pass the validation step, since it is a common last name in the English language. However, it cannot be directly inserted into the database because it contains the "'" apostrophe character, which would need to be escaped or otherwise handled. In this case, stripping the apostrophe might reduce the risk of SQL injection, but it would produce incorrect behavior because the wrong name would be recorded.
When feasible, it may be safest to disallow meta-characters entirely, instead of escaping them. This will provide some defense in depth. After the data is entered into the database, later processes may neglect to escape meta-characters before use, and you may not have control over those processes.
Ensure that error messages only contain minimal details that are useful to the intended audience and no one else. The messages need to strike the balance between being too cryptic (which can confuse users) or being too detailed (which may reveal more than intended). The messages should not reveal the methods that were used to determine the error. Attackers can use detailed information to refine or optimize their original attack, thereby increasing their chances of success.
If errors must be captured in some detail, record them in log messages, but consider what could occur if the log messages can be viewed by attackers. Highly sensitive information such as passwords should never be saved to log files.
Avoid inconsistent messaging that might accidentally tip off an attacker about internal state, such as whether a user account exists or not.
In the context of SQL Injection, error messages revealing the structure of a SQL query can help attackers tailor successful attack strings.
This weakness can often be detected using automated static analysis tools. Many modern tools use data flow analysis or constraint-based techniques to minimize the number of false positives.
Automated static analysis might not be able to recognize when proper input validation is being performed, leading to false positives - i.e., warnings that do not have any security consequences or do not require any code changes.
Automated static analysis might not be able to detect the usage of custom API functions or third-party libraries that indirectly invoke SQL commands, leading to false negatives - especially if the API/library code is not available for analysis.
According to SOAR, the following detection techniques may be useful:
According to SOAR, the following detection techniques may be useful:
According to SOAR, the following detection techniques may be useful:
According to SOAR, the following detection techniques may be useful:
According to SOAR, the following detection techniques may be useful:
According to SOAR, the following detection techniques may be useful:
CAPEC-ID | Attack Pattern Name |
---|---|
CAPEC-108 | Command Line Execution through SQL Injection An attacker uses standard SQL injection methods to inject data into the command line for execution. This could be done directly through misuse of directives such as MSSQL_xp_cmdshell or indirectly through injection of data into the database that would be interpreted as shell commands. Sometime later, an unscrupulous backend application (or could be part of the functionality of the same application) fetches the injected data stored in the database and uses this data as command line arguments without performing proper validation. The malicious data escapes that data plane by spawning new commands to be executed on the host. |
CAPEC-109 | Object Relational Mapping Injection An attacker leverages a weakness present in the database access layer code generated with an Object Relational Mapping (ORM) tool or a weakness in the way that a developer used a persistence framework to inject their own SQL commands to be executed against the underlying database. The attack here is similar to plain SQL injection, except that the application does not use JDBC to directly talk to the database, but instead it uses a data access layer generated by an ORM tool or framework (e.g. Hibernate). While most of the time code generated by an ORM tool contains safe access methods that are immune to SQL injection, sometimes either due to some weakness in the generated code or due to the fact that the developer failed to use the generated access methods properly, SQL injection is still possible. |
CAPEC-110 | SQL Injection through SOAP Parameter Tampering An attacker modifies the parameters of the SOAP message that is sent from the service consumer to the service provider to initiate a SQL injection attack. On the service provider side, the SOAP message is parsed and parameters are not properly validated before being used to access a database in a way that does not use parameter binding, thus enabling the attacker to control the structure of the executed SQL query. This pattern describes a SQL injection attack with the delivery mechanism being a SOAP message. |
CAPEC-470 | Expanding Control over the Operating System from the Database An attacker is able to leverage access gained to the database to read / write data to the file system, compromise the operating system, create a tunnel for accessing the host machine, and use this access to potentially attack other machines on the same network as the database machine. Traditionally SQL injections attacks are viewed as a way to gain unauthorized read access to the data stored in the database, modify the data in the database, delete the data, etc. However, almost every data base management system (DBMS) system includes facilities that if compromised allow an attacker complete access to the file system, operating system, and full access to the host running the database. The attacker can then use this privileged access to launch subsequent attacks. These facilities include dropping into a command shell, creating user defined functions that can call system level libraries present on the host machine, stored procedures, etc. |
CAPEC-66 | SQL Injection This attack exploits target software that constructs SQL statements based on user input. An attacker crafts input strings so that when the target software constructs SQL statements based on the input, the resulting SQL statement performs actions other than those the application intended. SQL Injection results from failure of the application to appropriately validate input. |
CAPEC-7 | Blind SQL Injection Blind SQL Injection results from an insufficient mitigation for SQL Injection. Although suppressing database error messages are considered best practice, the suppression alone is not sufficient to prevent SQL Injection. Blind SQL Injection is a form of SQL Injection that overcomes the lack of error messages. Without the error messages that facilitate SQL Injection, the adversary constructs input strings that probe the target through simple Boolean SQL expressions. The adversary can determine if the syntax and structure of the injection was successful based on whether the query was executed or not. Applied iteratively, the adversary determines how and where the target is vulnerable to SQL Injection. |
Name | Organization | Date | Date Release | Version |
---|---|---|---|---|
PLOVER | Draft 3 |
Name | Organization | Date | Comment |
---|---|---|---|
Eric Dalci | Cigital | updated Time_of_Introduction | |
KDM Analytics | added/updated white box definitions | ||
Veracode | Suggested OWASP Top Ten 2004 mapping | ||
CWE Content Team | MITRE | updated Applicable_Platforms, Common_Consequences, Modes_of_Introduction, Name, Relationships, Other_Notes, Relationship_Notes, Taxonomy_Mappings | |
CWE Content Team | MITRE | updated Description | |
CWE Content Team | MITRE | updated Observed_Examples | |
CWE Content Team | MITRE | updated Demonstrative_Examples, Description, Enabling_Factors_for_Exploitation, Modes_of_Introduction, Name, Observed_Examples, Other_Notes, Potential_Mitigations, References, Relationships | |
CWE Content Team | MITRE | updated Potential_Mitigations | |
CWE Content Team | MITRE | updated Demonstrative_Examples, Name, Related_Attack_Patterns | |
KDM Analytics | Improved the White_Box_Definition | ||
CWE Content Team | MITRE | updated Description, Name, White_Box_Definitions | |
CWE Content Team | MITRE | updated Potential_Mitigations | |
CWE Content Team | MITRE | updated Demonstrative_Examples, Detection_Factors, Potential_Mitigations, References, Relationships, Taxonomy_Mappings | |
CWE Content Team | MITRE | updated Demonstrative_Examples, Potential_Mitigations | |
CWE Content Team | MITRE | updated Common_Consequences, Demonstrative_Examples, Description, Detection_Factors, Name, Potential_Mitigations, References, Relationships | |
CWE Content Team | MITRE | updated Potential_Mitigations | |
CWE Content Team | MITRE | updated Demonstrative_Examples | |
CWE Content Team | MITRE | updated Common_Consequences | |
CWE Content Team | MITRE | updated Relationships | |
CWE Content Team | MITRE | updated Potential_Mitigations, References | |
CWE Content Team | MITRE | updated Potential_Mitigations, References, Related_Attack_Patterns, Relationships | |
CWE Content Team | MITRE | updated Potential_Mitigations | |
CWE Content Team | MITRE | updated Relationships | |
CWE Content Team | MITRE | updated Relationships | |
CWE Content Team | MITRE | updated Detection_Factors, Relationships, Taxonomy_Mappings | |
CWE Content Team | MITRE | updated Relationships | |
CWE Content Team | MITRE | updated Relationships | |
CWE Content Team | MITRE | updated Applicable_Platforms, Demonstrative_Examples, Enabling_Factors_for_Exploitation, Likelihood_of_Exploit, Modes_of_Introduction, Observed_Examples, References, Relationships, White_Box_Definitions | |
CWE Content Team | MITRE | updated References, Relationships | |
CWE Content Team | MITRE | updated References, Relationships, Taxonomy_Mappings | |
CWE Content Team | MITRE | updated Relationships | |
CWE Content Team | MITRE | updated Relationships | |
CWE Content Team | MITRE | updated Potential_Mitigations, Relationships, Time_of_Introduction | |
CWE Content Team | MITRE | updated Demonstrative_Examples, Potential_Mitigations, Relationship_Notes | |
CWE Content Team | MITRE | updated Relationships | |
CWE Content Team | MITRE | updated Potential_Mitigations, Relationships | |
CWE Content Team | MITRE | updated Relationships | |
CWE Content Team | MITRE | updated Relationships | |
CWE Content Team | MITRE | updated Observed_Examples, Relationships | |
CWE Content Team | MITRE | updated Observed_Examples, References | |
CWE Content Team | MITRE | updated Demonstrative_Examples, Description | |
CWE Content Team | MITRE | updated References, Relationships, Time_of_Introduction | |
CWE Content Team | MITRE | updated Mapping_Notes, Relationships | |
CWE Content Team | MITRE | updated Demonstrative_Examples, Observed_Examples | |
CWE Content Team | MITRE | updated Alternate_Terms, Common_Consequences, Description, Diagram, References | |
CWE Content Team | MITRE | updated Relationships |