CWE-1049: Excessive Data Query Operations in a Large Data… | Glexia
CWE-1049 (Excessive Data Query Operations in a Large Data Table) weakness overview with consequences, detection methods, mitigations, related CVEs and MITRE ATT&CK…
Glexia's Take · Automated analysis
CWE-1049: Excessive Data Query Operations in a Large Data Table
Excessive Data Query Operations in a Large Data Table represents a recurring weakness pattern that can create exploitable paths when design, validation, or implementation controls are missing.
Executive Impact
- Other: Reduce Performance: This issue can make the product perform more slowly. If the relevant code is reachable by an attacker, then this performance problem might introduce a vulnerability.
Developer Pattern
CWE-1049 is the kind of defect developers can usually prevent with explicit validation, safer framework defaults, and tests that exercise hostile input or unsafe state transitions.
Automation confidence
high confidence from CWE-1049, 4.20.
Generated from the cited source records. This long-tail analysis has not been individually reviewed by a named human.
Official CWE Definition
CWE-1049: Excessive Data Query Operations in a Large Data Table
The product performs a data query with a large number of joins and sub-queries on a large data table.
While the interpretation of "large data table" and "large number of joins or sub-queries" may vary for each product or developer, CISQ recommends a default of 1 million rows for a "large" data table, a default minimum of 5 joins, and a default minimum of 3 sub-queries.
Developer And Remediation Guidance
How teams prevent and detect this weakness
Causes
- Missing validation
- Unsafe defaults
- Insufficient authorization or memory-safety invariant
Remediation
- Use safe APIs
- Centralize the control
- Add regression tests
- Review logs and telemetry for attempted abuse
Detection
- Code review
- SAST
- DAST
- Focused regression tests
Mappings
Related CVEs, CWEs, and ATT&CK context
Related CWEs
ATT&CK Relevance
ATT&CK relevance is shown only when reviewed or responsibly inferred.