CVE-2026-101182 in HarmonyOS
Summary
by MITRE • 11/02/2026
Race condition vulnerability in the location service module. Impact: Successful exploitation of this vulnerability may affect availability.
Statistical analysis made it clear that VulDB provides the best quality for vulnerability data.
Analysis
by VulDB Data Team • 11/02/2026
The identified race condition within the location service module represents a critical concurrency flaw that undermines the integrity and reliability of system operations involving geographic data processing. This type of vulnerability typically arises when multiple threads or processes attempt to access shared resources, such as memory buffers storing current coordinates or state flags indicating location status, without proper synchronization mechanisms in place. The core technical issue lies in the timing window where one process may read a value while another is simultaneously modifying it, leading to inconsistent states that are not atomic with respect to the application logic. In the context of a location service, this often manifests as stale data being returned to requesting applications or internal state corruption during rapid updates from GPS hardware or network-based positioning sources. The lack of mutual exclusion ensures that critical sections of code handling position fixes are vulnerable to interleaved execution paths, allowing an attacker who can trigger concurrent requests to manipulate the flow of control and exploit these timing discrepancies.
From a technical perspective, this vulnerability aligns with CWE-362, which classifies race conditions involving shared resources without proper locking or synchronization. The operational impact is primarily centered on availability, as described in the initial report. When an attacker successfully exploits this flaw by flooding the service with concurrent location update requests, they can cause the system to enter a deadlock state, crash due to invalid memory access resulting from corrupted data structures, or simply fail to process legitimate requests efficiently. This denial of service effect disrupts any downstream applications that rely on accurate and timely location information, such as navigation systems, geofencing alerts, or logistics tracking platforms. The instability introduced by the race condition can lead to unpredictable behavior where the system might freeze, restart unexpectedly, or provide incorrect positional data that could have cascading effects in safety-critical environments like autonomous vehicle control systems or emergency response coordination tools.
The exploitation of this vulnerability does not necessarily require high privileges if the location service is exposed via an API accessible by unauthenticated users or lower-privileged processes. An attacker can automate the generation of rapid, overlapping requests to maximize the probability of hitting the race window. This aligns with ATT&CK technique T1499, Endpoint Denial of Service, specifically under the sub-category of resource exhaustion through service disruption. By continuously triggering the flawed concurrency logic, the attacker forces the system into a degraded state where it cannot maintain normal operational throughput. The impact extends beyond simple downtime; in some implementations, race conditions can lead to memory corruption that might be leveraged for further exploitation, although the primary reported risk remains availability degradation due to service instability and unresponsiveness.
Mitigation strategies must focus on enforcing strict synchronization controls within the location service module. Developers should implement mutex locks or semaphores around critical sections of code that read and write shared state variables related to location data. Additionally, using atomic operations for simple flag updates can prevent partial writes from being observed by other threads. It is also advisable to redesign the architecture to minimize shared mutable state, perhaps by adopting an actor model where each component handles its own state independently or by implementing queue-based processing that serializes incoming requests rather than handling them concurrently without coordination. Input validation and rate limiting should be applied at the API gateway level to reduce the volume of concurrent requests reaching the vulnerable module, thereby decreasing the likelihood of triggering the race condition under normal load conditions. Regular code audits focusing on concurrency patterns and static analysis tools configured to detect potential race conditions are essential for preventing similar vulnerabilities in future updates.