CVE-2026-81640 (GCVE-0-2026-81640)
Vulnerability from cvelistv5 – Published: 2026-09-09 15:40 – Updated: 2026-09-09 19:12
VLAI
EPSS
VEX
Title
Softish C6 Ear Camera and EarVision Android Application Use of Hard-coded Credentials
Summary
An attacker could derive the camera's Wi-Fi password and connect to its wireless network. This weakens or eliminates the security value of the access-point password and may expose the live video stream, device services, status interfaces, and firmware-update functionality.
Severity
SSVC
Exploitation: none
Automatable: no
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-09-09 19:11 UTC
CWE
Assigner
References
1 reference
Impacted products
2 products
| Vendor | Product | Version | |
|---|---|---|---|
| Softish | EarVision Android application |
Affected:
1.3.1
(custom)
|
|
| Softish | C6 Ear Camera |
Affected:
1.3.1_Code 132
|
Date Public
2026-09-08 17:00
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Experimental. This forecast is provided for visualization only and may change without notice. Do not use it for operational decisions.
Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.
Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
- Seen: The vulnerability was mentioned, discussed, or observed by the user.
- Confirmed: The vulnerability has been validated from an analyst's perspective.
- Published Proof of Concept: A public proof of concept is available for this vulnerability.
- Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
- Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
- Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
- Not confirmed: The user expressed doubt about the validity of the vulnerability.
- Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.
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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.
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.
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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