CVE-2026-14685 in HdrHistogram
Summary
by MITRE • 07/05/2026
A vulnerability has been found in HdrHistogram up to 2.2.2. This vulnerability affects the function recordValueWithCount of the file src/main/java/org/HdrHistogram/AbstractHistogram.java of the component AbstractHistogram. Such manipulation of the argument Count leads to state issue. The attack can only be performed from a local environment. The exploit has been disclosed to the public and may be used. The project was informed of the problem early through an issue report but has not responded yet.
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Analysis
by VulDB Data Team • 07/05/2026
The vulnerability in HdrHistogram version 2.2.2 represents a critical state management flaw within the AbstractHistogram component that could potentially compromise system integrity. This issue specifically targets the recordValueWithCount function located in src/main/java/org/HdrHistogram/AbstractHistogram.java, where improper handling of the Count argument parameter creates exploitable conditions that affect the internal state of the histogram data structure. The vulnerability stems from inadequate input validation and state consistency checks that allow malicious manipulation of the count parameter to disrupt normal operation.
The technical implementation flaw manifests when the recordValueWithCount function processes the Count argument without proper boundary checking or state verification mechanisms. This creates opportunities for attackers to manipulate the internal counters and potentially corrupt the histogram's statistical data representation. The vulnerability operates at the core level of the histogram's data management system, where the Count parameter directly influences how values are recorded and aggregated within the underlying data structures. Such manipulation can lead to incorrect statistical calculations and potentially cause downstream applications that depend on accurate histogram data to behave unpredictably.
From an operational perspective, this vulnerability presents a significant risk particularly in environments where HdrHistogram is used for performance monitoring, latency tracking, or statistical analysis of system behavior. The local environment requirement limits the attack surface but does not eliminate the threat, as local privilege escalation scenarios or compromised systems can still exploit this weakness. Applications using HdrHistogram for critical metrics collection may experience data corruption that affects monitoring systems, alerting mechanisms, and decision-making processes based on histogram statistics. The public disclosure of this exploit increases the likelihood of real-world exploitation, making it imperative for organizations to assess their exposure.
The vulnerability aligns with CWE-691, which addresses insufficient control of a resource through a public interface, and may also relate to CWE-707, concerning improper handling of externally-controlled input. From an attack framework perspective, this issue could be categorized under ATT&CK technique T1059 for command and scripting interpreter usage when exploited locally, or potentially T1499 for endpoint denial of service if the state corruption leads to system instability. The lack of response from the project maintainers after early reporting creates additional risk as organizations cannot rely on official patches or updates to address this weakness, forcing them to implement custom workarounds or alternative solutions.
Organizations utilizing HdrHistogram should immediately implement mitigation strategies including updating to patched versions when available, implementing local access controls to limit potential exploitation vectors, and monitoring for unusual behavior in systems using this library. Additional defensive measures include runtime input validation, state integrity checks, and comprehensive logging of histogram operations to detect potential exploitation attempts. The vulnerability serves as a reminder of the importance of proper state management in concurrent data structures and highlights the need for robust input validation mechanisms throughout software components that handle statistical data aggregation.