Raw vector
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:HSummary
CVE-2025-26866 is a high-severity Deserialization of Untrusted Data (CWE-502) vulnerability in Apache Hugegraph. Its CVSS base score is 8.8 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked in the top 43% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog.
The strongest mitigations our analysis identified map to SA-11 (Developer Testing and Evaluation) and SI-10 (Information Input Validation) — see the control section below for these in your framework.
Deeper analysis AI-assisted summary
Synthesised by an AI model from the NVD description and linked references — a reading aid, not an authoritative source.
CVE-2025-26866 is a remote code execution vulnerability stemming from insecure Hessian deserialization in the PD store component, affecting Apache HugeGraph. A malicious Raft node can exploit this flaw to inject malicious objects during deserialization, leading to arbitrary code execution. The issue is classified under CWE-502 (Deserialization of Untrusted Data) with a CVSS v3.1 base score of 8.8 (AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H), indicating high severity due to network accessibility, low attack complexity, and requirements for only low privileges.
An attacker with low privileges, such as the ability to introduce a malicious Raft node into the cluster, can exploit this over the network with no user interaction. Successful exploitation grants high-impact remote code execution on the targeted PD store, potentially compromising confidentiality, integrity, and availability of the affected system.
Advisories recommend upgrading to Apache HugeGraph version 1.7.0, which addresses the vulnerability by enforcing IP-based authentication to restrict cluster membership and implementing a strict class whitelist to prevent object injection in the Hessian serialization process. Details are available in the GitHub pull request at https://github.com/apache/incubator-hugegraph/pull/2735, Apache mailing list announcement at https://lists.apache.org/thread/ko8jkwbjbb99m45pg4sgo5xsm8gx9nsq, and OSS-Security mailing list at http://www.openwall.com/lists/oss-security/2025/12/09/1.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-203068
Vulnerability Data
A remote code execution vulnerability exists where a malicious Raft node can exploit insecure Hessian deserialization within the PD store. The fix enforces IP-based authentication to restrict cluster membership and implements a strict class whitelist to harden the Hessian serialization…
more
process against object injection attacks. Users are recommended to upgrade to version 1.7.0, which fixes the issue.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Mitigating Controls (NIST 800-53 r5) AI
Developer testing and evaluation can uncover deserialization flaws before deployment.
Input validation directly stops deserialization of untrusted data by ensuring inputs are valid before processing.
Engineering principles such as safe deserialization and input sanitization structurally prevent the weakness from being introduced.
Integrity verification tools can detect malformed or tampered serialized data after the fact.
Mitigating Controls (NIST CSF 2.0) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→CSF cross-walk (authority under review) — links open the control.
PR.PS-02 addresses only post-deployment updates/patching and cannot prevent introduction of unsafe deserialization code, yet it can remediate some instances when the flaw exists in outdated libraries or components.
Mitigating Controls (ISO/IEC 27001:2022 Annex A) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→ISO cross-walk (authority under review) — links open the control.
Security testing includes validation of deserialization routines and the use of untrusted data, reducing the likelihood that unsafe object reconstruction will be deployed.
Requiring vetted libraries, regular updates and SAST before release reduces the likelihood that deserialization logic will accept and act on attacker-controlled serialized objects.
Regular scanning of third-party libraries and timely patching reduce the likelihood that unsafe deserialization vulnerabilities remain active.
Mandatory malware scanning of data received over networks or storage media intercepts malicious serialized payloads before they are deserialized by the target application.