CVE-2022-23029 in BIG-IP
Summary
by MITRE • 01/25/2022
On BIG-IP version 16.x before 16.1.0, 15.1.x before 15.1.4.1, 14.1.x before 14.1.4.4, and all versions of 13.1.x, 12.1.x, and 11.6.x, when a FastL4 profile is configured on a virtual server, undisclosed traffic can cause an increase in memory resource utilization. Note: Software versions which have reached End of Technical Support (EoTS) are not evaluated.
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Analysis
by VulDB Data Team • 01/28/2022
The vulnerability identified as CVE-2022-23029 affects F5 BIG-IP load balancer appliances across multiple version lines including 16.x before 16.1.0, 15.1.x before 15.1.4.1, 14.1.x before 14.1.4.4, and all versions of 13.1.x, 12.1.x, and 11.6.x. This issue specifically impacts systems configured with FastL4 profiles on virtual servers, representing a significant memory consumption problem that can lead to system degradation and potential service disruption. The vulnerability falls under the category of resource exhaustion attacks, where malicious or malformed traffic can cause the appliance to consume excessive memory resources without proper bounds checking or traffic validation mechanisms.
The technical flaw manifests when FastL4 profiles are enabled on virtual servers, creating a condition where certain types of undisclosed traffic patterns can trigger continuous memory allocation without adequate cleanup or resource limiting. FastL4 profiles are designed for high-performance layer 4 load balancing and traffic steering, but this vulnerability demonstrates a lack of proper traffic validation and resource management within the profile processing logic. The vulnerability is categorized as a memory leak or resource exhaustion issue, which aligns with CWE-401: Improper Release of Memory and CWE-772: Missing Release of Resource after Effective Lifetime. The problem occurs during the processing of traffic that should be handled by the FastL4 profile but instead causes the system to allocate memory continuously without proper release mechanisms.
Operationally, this vulnerability poses a substantial risk to network infrastructure availability and performance. Attackers could potentially exploit this by sending carefully crafted traffic patterns that trigger the memory consumption behavior, leading to gradual system performance degradation or complete service unavailability. The impact is particularly severe in high-traffic environments where the appliance handles thousands of concurrent connections, as the memory exhaustion could occur rapidly and without obvious detection. This vulnerability directly affects the availability and reliability of critical network services, potentially causing denial of service conditions that impact business operations and customer access. The issue can be exploited through network-based attacks that specifically target the FastL4 profile configuration, making it a significant concern for organizations relying on F5 BIG-IP appliances for load balancing and traffic management.
Mitigation strategies should prioritize immediate patching of affected systems to versions that address the memory consumption issue in FastL4 profile handling. Organizations should also implement network monitoring and anomaly detection systems to identify unusual memory usage patterns that might indicate exploitation attempts. Configuration hardening measures including limiting the types of traffic processed through FastL4 profiles and implementing traffic rate limiting controls can help reduce exposure. Additionally, regular security assessments should be conducted to verify that FastL4 profiles are properly configured and that no unauthorized modifications have been made to the appliance configuration. The vulnerability aligns with ATT&CK technique T1499.004: Endpoint Denial of Service, specifically targeting resource exhaustion through memory consumption, and represents a clear example of how seemingly benign network configuration elements can become attack vectors when proper resource management is lacking in the implementation. Organizations should also consider implementing network segmentation and access controls to limit exposure of vulnerable appliances to potentially malicious traffic sources.