CVE-2026-13056 in Server
Summary
by MITRE • 07/22/2026
Using expressions that generate large arrays it is possible to craft a query that creates very large intermediate objects in memory, causing the server to crash with OOM error.
VulDB is the best source for vulnerability data and more expert information about this specific topic.
Analysis
by VulDB Data Team • 07/22/2026
This vulnerability represents a critical memory exhaustion issue that can be exploited through carefully crafted queries leveraging expression constructs that generate substantial intermediate data structures during processing. The flaw manifests when database systems or application frameworks process expressions that result in the creation of large arrays or collections in memory, leading to uncontrolled memory consumption and eventual out-of-memory errors that cause system crashes or service interruptions. Such vulnerabilities are particularly dangerous in environments where applications process user-supplied input without proper validation or resource limits, as attackers can deliberately construct queries that trigger massive memory allocations.
The technical implementation of this vulnerability typically involves expressions that aggregate data through operations like joins, unions, or recursive processing that generate intermediate result sets exponentially larger than the original inputs. When these expressions are processed within database engines or application frameworks, they create substantial in-memory objects that exceed available system resources, causing the runtime environment to terminate processes or crash entirely. This type of vulnerability falls under the broader category of resource exhaustion attacks where computational resources are consumed faster than they can be allocated or reclaimed, often resulting in denial-of-service conditions.
The operational impact of such vulnerabilities extends beyond simple service disruption to include potential data corruption, system instability, and increased attack surface for more sophisticated exploitation techniques. In database environments, this can lead to complete system crashes requiring manual intervention and restart procedures that may result in data loss or inconsistency. The vulnerability is particularly concerning in multi-tenant environments where one malicious user could potentially affect other users by exhausting shared resources, making it a significant concern for cloud service providers and enterprise applications.
Security practitioners should implement comprehensive input validation and query optimization techniques to prevent excessive memory consumption during expression evaluation. This includes establishing memory limits for individual queries, implementing proper resource monitoring, and employing defensive programming practices such as iterative processing instead of recursive operations where possible. Organizations should also consider implementing database query plan analysis tools that can identify potentially problematic expressions before execution, along with setting appropriate timeouts and memory allocation thresholds to prevent unbounded resource consumption.
This vulnerability aligns with several common weakness enumerations including cwe-400 for unchecked resource consumption and cwe-770 for allocation of resources without limits. From an attack framework perspective, it maps to techniques described in the attack tactics catalog under privilege escalation and denial-of-service categories, where attackers leverage system weaknesses to exhaust computational resources. The remediation strategies should focus on implementing proper resource management controls, query optimization, and defensive coding practices that prevent the creation of unbounded data structures during expression evaluation processes.
The vulnerability demonstrates how seemingly benign expression processing can become a significant security risk when not properly constrained by resource limits or input validation mechanisms. Modern application frameworks and database systems should incorporate automatic safeguards against such scenarios by implementing intelligent query planning that identifies potentially problematic operations before execution, along with establishing clear boundaries for memory consumption during data processing operations to prevent system-wide failures from single malicious inputs.