CVE-2025-13708: Tencent NeuralNLP-NeuralClassifier · Deserialization of Untrusted Data
Affects Tencent NeuralNLP-NeuralClassifierSource CVE Database V5
Not known to be exploited · CVSS 7.8 High.
Data as of 26 Oct 2026, 14:05 UTC
- 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:HExploitation evidence
Disclosed: evidence found.
CVE published 23 Dec 2025
Detectable: no evidence on record.
No public nuclei template indexed
Weaponised: no evidence on record.
No public exploit code indexed in Metasploit or Exploit-DB
Exploited in the wildExploited: no evidence on record.
Not in CISA KEV and no source reports exploitation
Observed by OffSeq MirageSeen by 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 26 Oct 2026.
What to do
AI-suggested
- Immediately audit and restrict the sources from which serialized checkpoint data can be loaded, ensuring only trusted and verified files are accepted.
- Implement strict input validation and sandboxing around the deserialization process to prevent execution of malicious payloads.
- Employ application whitelisting and privilege separation to limit the impact of potential code execution.
- Educate users about the risks of opening untrusted files or visiting suspicious websites to reduce the likelihood of user interaction-based exploitation.
- Monitor system logs and network traffic for unusual activity related to checkpoint loading or unexpected process spawning.
- Engage with Tencent for updates and patches, and plan for timely deployment once available.
- Consider isolating AI model processing environments from critical infrastructure to contain potential breaches.
- 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.