PYSEC-2024-198
Vulnerability from pysec - Published: 2024-10-10 22:15 - Updated: 2025-01-19 16:22
VLAI
Details
Gradio is an open-source Python package designed for quick prototyping. This vulnerability involves data exposure due to the enable_monitoring flag not properly disabling monitoring when set to False. Even when monitoring is supposedly disabled, an attacker or unauthorized user can still access the monitoring dashboard by directly requesting the /monitoring endpoint. This means that sensitive application analytics may still be exposed, particularly in environments where monitoring is expected to be disabled. Users who set enable_monitoring=False to prevent unauthorized access to monitoring data are impacted. Users are advised to upgrade to gradio>=4.44 to address this issue. There are no known workarounds for this vulnerability.
Severity
4.3 (Medium)
Impacted products
| Name | purl | gradio | pkg:pypi/gradio |
|---|
Aliases
{
"affected": [
{
"package": {
"ecosystem": "PyPI",
"name": "gradio",
"purl": "pkg:pypi/gradio"
},
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{
"events": [
{
"introduced": "0"
},
{
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}
],
"type": "ECOSYSTEM"
}
],
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}
],
"aliases": [
"CVE-2024-47168",
"GHSA-hm3c-93pg-4cxw"
],
"details": "Gradio is an open-source Python package designed for quick prototyping. This vulnerability involves data exposure due to the enable_monitoring flag not properly disabling monitoring when set to False. Even when monitoring is supposedly disabled, an attacker or unauthorized user can still access the monitoring dashboard by directly requesting the /monitoring endpoint. This means that sensitive application analytics may still be exposed, particularly in environments where monitoring is expected to be disabled. Users who set enable_monitoring=False to prevent unauthorized access to monitoring data are impacted. Users are advised to upgrade to gradio\u003e=4.44 to address this issue. There are no known workarounds for this vulnerability.",
"id": "PYSEC-2024-198",
"modified": "2025-01-19T16:22:57.845317+00:00",
"published": "2024-10-10T22:15:11+00:00",
"references": [
{
"type": "ADVISORY",
"url": "https://github.com/gradio-app/gradio/security/advisories/GHSA-hm3c-93pg-4cxw"
}
],
"severity": [
{
"score": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:L/I:N/A:N",
"type": "CVSS_V3"
}
]
}
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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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