Summary
NVIDIA NeMo Agent Toolkit contains a SQL injection vulnerability that may allow an attacker to interfere with database queries through insufficiently protected input.
Successful exploitation could enable data tampering, information disclosure, or denial of service when vulnerable database-backed functionality is reachable.
NVIDIA NeMo Agent Toolkit contains a SQL injection vulnerability that may allow an attacker to interfere with database queries through insufficiently protected input.
Successful exploitation could enable data tampering, information disclosure, or denial of service when vulnerable database-backed functionality is reachable.
Affected context
The vulnerability may occur in NeMo Agent Toolkit functionality that incorporates attacker-influenced data into SQL statements without adequate parameterization or validation. Risk depends on whether the affected database interface is exposed, whether untrusted input reaches the query construction path, and the permissions available to the application database account.
The vulnerability may occur in NeMo Agent Toolkit functionality that incorporates attacker-influenced data into SQL statements without adequate parameterization or validation. Risk depends on whether the affected database interface is exposed, whether untrusted input reaches the query construction path, and the permissions available to the application database account.
Exploit preconditions
- Attack surface: A database-backed NeMo Agent Toolkit component that processes externally influenced input
- Required access: Ability to submit or control input accepted by the affected functionality; authentication requirements depend on deployment configuration
- Trigger point: Processing the input in a database query path
- Why exploitation is possible: Untrusted data may be combined with SQL commands rather than being handled strictly as a query parameter
- Attack surface: A database-backed NeMo Agent Toolkit component that processes externally influenced input
- Required access: Ability to submit or control input accepted by the affected functionality; authentication requirements depend on deployment configuration
- Trigger point: Processing the input in a database query path
- Why exploitation is possible: Untrusted data may be combined with SQL commands rather than being handled strictly as a query parameter
Why this happens
The underlying bug is improper separation between data and SQL instructions. When application-controlled and attacker-controlled values are concatenated or otherwise inserted into SQL statements without safe parameter binding, crafted input can alter the intended query structure.
This behavior maps to CWE-89: Improper Neutralization of Special Elements used in an SQL Command ('SQL Injection').
The underlying bug is improper separation between data and SQL instructions. When application-controlled and attacker-controlled values are concatenated or otherwise inserted into SQL statements without safe parameter binding, crafted input can alter the intended query structure.
This behavior maps to CWE-89: Improper Neutralization of Special Elements used in an SQL Command ('SQL Injection').
Potential impact
- An attacker may disclose information from accessible database tables.
- An attacker could modify or delete stored data.
- Database-backed functionality may become unavailable, causing denial of service.
- Exposed database credentials or application secrets may be revealed if they are stored in accessible records.
- Broader compromise could occur if the database account has excessive privileges or supports dangerous secondary functionality.
- An attacker may disclose information from accessible database tables.
- An attacker could modify or delete stored data.
- Database-backed functionality may become unavailable, causing denial of service.
- Exposed database credentials or application secrets may be revealed if they are stored in accessible records.
- Broader compromise could occur if the database account has excessive privileges or supports dangerous secondary functionality.
General mitigation guidance
Mitigation generally focuses on preserving a strict separation between query structure and input data.
Common defensive patterns include:
- Use parameterized queries, prepared statements, or safe query-builder interfaces by default.
- Avoid constructing SQL through string concatenation or interpolation.
- Apply allowlist validation to identifiers such as table names, sort fields, and filter operators when dynamic SQL is unavoidable.
- Reduce database-account privileges to only the operations and records required by the application.
- Minimize trust in externally supplied values and validate inputs at appropriate service boundaries.
- Use secure-by-default database access abstractions and centralize query construction.
- Prevent sensitive data from being returned unnecessarily and apply appropriate authorization checks.
Mitigation generally focuses on preserving a strict separation between query structure and input data.
Common defensive patterns include:
- Use parameterized queries, prepared statements, or safe query-builder interfaces by default.
- Avoid constructing SQL through string concatenation or interpolation.
- Apply allowlist validation to identifiers such as table names, sort fields, and filter operators when dynamic SQL is unavoidable.
- Reduce database-account privileges to only the operations and records required by the application.
- Minimize trust in externally supplied values and validate inputs at appropriate service boundaries.
- Use secure-by-default database access abstractions and centralize query construction.
- Prevent sensitive data from being returned unnecessarily and apply appropriate authorization checks.
How teams detect issues like this
Detection approaches may include:
- Static analysis rules that flag string concatenation, interpolation, or formatting used to construct SQL statements.
- Code review checks for missing parameter binding and dynamically selected identifiers.
- Runtime testing with harmless unexpected input to verify that query structure cannot be altered.
- Database monitoring for anomalous query patterns, unexpected errors, unusual data access, and high-volume requests.
- Policy enforcement requiring approved database-access libraries and parameterized-query APIs.
- Automated code review or software composition analysis tools may help identify related query-construction risks.
Detection approaches may include:
- Static analysis rules that flag string concatenation, interpolation, or formatting used to construct SQL statements.
- Code review checks for missing parameter binding and dynamically selected identifiers.
- Runtime testing with harmless unexpected input to verify that query structure cannot be altered.
- Database monitoring for anomalous query patterns, unexpected errors, unusual data access, and high-volume requests.
- Policy enforcement requiring approved database-access libraries and parameterized-query APIs.
- Automated code review or software composition analysis tools may help identify related query-construction risks.
Sources
| Source | Link |
|---|---|
| github. | github.com/ |
| nvd. | nvd.nist.gov/ |
| cve. | cve.org/ |
