FKIE_CVE-2026-54447
Vulnerability from fkie_nvd - Published: 2026-09-14 20:16 - Updated: 2026-09-14 20:16
Severity
Summary
garminconnect is a Python 3 API wrapper for Garmin Connect that retrieves statistics and manages activities. Prior to 0.3.5, garminconnect/client.py Client.dump creates the OAuth token directory and garmin_tokens.json without explicit owner-only modes, so a permissive umask such as 022 can leave the directory mode at 0755 and the token file mode at 0644. garmin_tokens.json contains di_refresh_token, and another unprivileged user on a shared Linux or macOS host can read the token and obtain persistent access to the victim's Garmin Connect account, including health, fitness, activity, and device data. The Garmin.login tokenstore path is affected, and a pre-existing loosely permissioned token file remains exposed until rewritten or manually restricted. This issue is fixed in version 0.3.5.
References
Impacted products
| Vendor | Product | Version |
|---|
{
"affected": [
{
"affectedData": [
{
"product": "python-garminconnect",
"vendor": "cyberjunky",
"versions": [
{
"status": "affected",
"version": "\u003c 0.3.5"
}
]
}
],
"source": "security-advisories@github.com"
}
],
"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "garminconnect is a Python 3 API wrapper for Garmin Connect that retrieves statistics and manages activities. Prior to 0.3.5, garminconnect/client.py Client.dump creates the OAuth token directory and garmin_tokens.json without explicit owner-only modes, so a permissive umask such as 022 can leave the directory mode at 0755 and the token file mode at 0644. garmin_tokens.json contains di_refresh_token, and another unprivileged user on a shared Linux or macOS host can read the token and obtain persistent access to the victim\u0027s Garmin Connect account, including health, fitness, activity, and device data. The Garmin.login tokenstore path is affected, and a pre-existing loosely permissioned token file remains exposed until rewritten or manually restricted. This issue is fixed in version 0.3.5."
}
],
"id": "CVE-2026-54447",
"lastModified": "2026-09-14T20:16:46.290",
"metrics": {
"cvssMetricV31": [
{
"cvssData": {
"attackComplexity": "LOW",
"attackVector": "LOCAL",
"availabilityImpact": "NONE",
"baseScore": 8.4,
"baseSeverity": "HIGH",
"confidentialityImpact": "HIGH",
"integrityImpact": "HIGH",
"privilegesRequired": "LOW",
"scope": "CHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:C/C:H/I:H/A:N",
"version": "3.1"
},
"exploitabilityScore": 2.0,
"impactScore": 5.8,
"source": "security-advisories@github.com",
"type": "Secondary"
}
],
"ssvcV203": [
{
"source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
"ssvcData": {
"id": "CVE-2026-54447",
"options": [
{
"exploitation": "none"
},
{
"automatable": "no"
},
{
"technicalImpact": "total"
}
],
"role": "CISA Coordinator",
"timestamp": "2026-09-14T20:09:53.554592Z",
"version": "2.0.3"
}
}
]
},
"published": "2026-09-14T20:16:46.290",
"references": [
{
"source": "security-advisories@github.com",
"url": "https://github.com/cyberjunky/python-garminconnect/commit/8256b577190b446e279c81b845d8e27d0ea1fbf5"
},
{
"source": "security-advisories@github.com",
"url": "https://github.com/cyberjunky/python-garminconnect/releases/tag/0.3.5"
},
{
"source": "security-advisories@github.com",
"url": "https://github.com/cyberjunky/python-garminconnect/security/advisories/GHSA-wjhr-76vg-2hvc"
}
],
"sourceIdentifier": "security-advisories@github.com",
"vulnStatus": "Received",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-732"
}
],
"source": "security-advisories@github.com",
"type": "Primary"
}
]
}
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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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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.
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