CVE-2025-11645 (GCVE-0-2025-11645)
Vulnerability from cvelistv5 – Published: 2025-10-12 20:32 – Updated: 2025-10-18 21:27
VLAI
EPSS
VEX
Title
Tomofun Furbo Mobile App Authentication Token sensitive information
Summary
A security vulnerability has been detected in Tomofun Furbo Mobile App up to 7.57.0a on Android. This affects an unknown part of the component Authentication Token Handler. The manipulation leads to insecure storage of sensitive information. It is possible to launch the attack on the physical device. The exploit has been disclosed publicly and may be used. The vendor was contacted early about this disclosure but did not respond in any way.
Severity
SSVC
Exploitation: poc
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2025-10-14 14:02 UTC
Assigner
References
4 references
| URL | Tags |
|---|---|
| https://vuldb.com/?id.328056 | vdb-entrytechnical-description |
| https://vuldb.com/?ctiid.328056 | signaturepermissions-required |
| https://vuldb.com/?submit.661899 | third-party-advisory |
| https://github.com/dead1nfluence/Furbo-Advisories… | exploit |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| Tomofun | Furbo Mobile App |
Affected:
7.57.0a
|
guessed |
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Nomenclature
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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the
vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base
model, served locally by ML-Gateway.
They have not been verified by an analyst and are provided for guidance only.
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Browse all ATT&CK techniques and the vulnerabilities related to each.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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Related by attack behaviour
Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.