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CVE-2025-13708VulnerabilityPublished Updated

CVE-2025-13708: Tencent NeuralNLP-NeuralClassifier · Deserialization of Untrusted Data

Affects Tencent NeuralNLP-NeuralClassifierSource CVE Database V5

0

Not known to be exploited · CVSS 7.8 High.

Data as of 26 Oct 2026, 06:26 UTC

CVSS 3.0 base score
7.8High
How severe the flaw is on the 0–10 CVSS scale.
EPSS
0.5% chance of exploitation in 30 days – higher than 43% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.
CVSS:3.0/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H

Exploitation evidence

  1. Disclosed: evidence found.

    CVE published 23 Dec 2025

  2. Detectable: no evidence on record.

    No public nuclei template indexed

  3. Weaponised: no evidence on record.

    No public exploit code indexed in Metasploit or Exploit-DB

  4. Exploited in the wild: no evidence on record.

    Not in CISA KEV and no source reports exploitation

  5. Observed by OffSeq Mirage: no evidence on record.

    No attack traffic recorded by OffSeq Mirage sensors

A missing step is not evidence of safety. Sources: cve.org (opens in a new tab), the Metasploit, Exploit-DB and nuclei-templates index as of 2026-10-26.

What to do

AI-suggested

1. Immediately audit and restrict the sources from which serialized checkpoint data can be loaded, ensuring only trusted and verified files are accepted. 2. Implement strict input validation and sandboxing around the deserialization process to prevent execution of malicious payloads. 3. Employ application whitelisting and privilege separation to limit the impact of potential code execution. 4. Educate users about the risks of opening untrusted files or visiting suspicious websites to reduce the likelihood of user interaction-based exploitation. 5. Monitor system logs and network traffic for unusual activity related to checkpoint loading or unexpected process spawning. 6. Engage with Tencent for updates and patches, and plan for timely deployment once available. 7. Consider isolating AI model processing environments from critical infrastructure to contain potential breaches. 8. Use endpoint detection and response (EDR) tools to detect and respond to exploitation attempts quickly.

Description

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 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.

References

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