CVE-2025-13708
Published: 23 December 2025
Summary
CVE-2025-13708 is a high-severity Deserialization of Untrusted Data (CWE-502) vulnerability in Zerodayinitiative (inferred from references). Its CVSS base score is 7.8 (High).
Operationally, ranked in the top 18.2% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog.
Deeper analysis
Tencent NeuralNLP-NeuralClassifier contains a deserialization of untrusted data vulnerability in the _load_checkpoint function. The flaw stems from insufficient validation of user-supplied input, allowing an attacker to supply a malicious serialized object that results in arbitrary code execution when processed. The affected component is the open-source NeuralNLP-NeuralClassifier project from Tencent, and successful exploitation grants code execution in the root context.
Remote attackers can trigger the issue by convincing a target to visit a malicious page or open a malicious file. No authentication is required, and the attack vector is rated local with user interaction under the supplied CVSS 7.8 scoring. The vulnerability was originally reported as ZDI-CAN-27184 and is tracked under CWE-502.
A patch addressing the issue is available in commit 8dea5ffdb45cf0a33b3d116de38507afaee87594 on the project's GitHub repository, and further details are provided in Zero Day Initiative advisory ZDI-25-1033. The associated EPSS score has remained flat at 0.0155 with no material increase observed after disclosure.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-204972
Vulnerability details
Tencent NeuralNLP-NeuralClassifier _load_checkpoint Deserialization of Untrusted Data Remote Code Execution Vulnerability. This vulnerability allows remote attackers to execute arbitrary code on affected installations of Tencent NeuralNLP-NeuralClassifier. User interaction is required to exploit this vulnerability in that the target must visit…
more
a malicious page or open a malicious file. The specific flaw exists within the _load_checkpoint function. The issue results from the lack of proper validation of user-supplied data, which can result in deserialization of untrusted data. An attacker can leverage this vulnerability to execute code in the context of root. Was ZDI-CAN-27184.
- CWE(s)
Related Threats
No named actor attribution yet. ATT&CK technique mapping in progress for this CVE.
Affected Assets
Mitigating Controls
Likely Mitigating Controls AI
Per-CVE control mapping for this CVE has not run yet; the list below is derived from the weakness types (CWEs) cited in the NVD entry.
Penetration testing supplies malicious serialized objects, detecting unsafe deserialization and supporting corrective actions.
Evaluation of untrusted data handling (deserialization testing) reveals unsafe processing, which the required remediation process addresses.
Untrusted serialized data can be deserialized and observed inside the chamber, blocking gadget-chain exploitation outside the sandbox.
Validates or rejects untrusted serialized data before deserialization occurs.
Identifies and blocks malicious code introduced through deserialization of untrusted data at system boundaries.
Integrity verification of serialized information can detect tampering before deserialization occurs.
Provenance of associated data allows detection of untrusted sources before deserialization or processing occurs.