PYSEC-2024-219
Vulnerability from pysec - Published: 2024-10-10 23:15 - Updated: 2025-01-19 22:22
VLAI
Details
Gradio is an open-source Python package designed for quick prototyping. This vulnerability involves insecure communication between the FRP (Fast Reverse Proxy) client and server when Gradio's share=True option is used. HTTPS is not enforced on the connection, allowing attackers to intercept and read files uploaded to the Gradio server, as well as modify responses or data sent between the client and server. This impacts users who are sharing Gradio demos publicly over the internet using share=True without proper encryption, exposing sensitive data to potential eavesdroppers. Users are advised to upgrade to gradio>=5 to address this issue. As a workaround, users can avoid using share=True in production environments and instead host their Gradio applications on servers with HTTPS enabled to ensure secure communication.
Severity
9.1 (Critical)
Impacted products
| Name | purl | gradio | pkg:pypi/gradio |
|---|
Aliases
{
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{
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"ecosystem": "PyPI",
"name": "gradio",
"purl": "pkg:pypi/gradio"
},
"ranges": [
{
"events": [
{
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{
"fixed": "5.0.0"
}
],
"type": "ECOSYSTEM"
}
],
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}
],
"aliases": [
"CVE-2024-47871",
"GHSA-279j-x4gx-hfrh"
],
"details": "Gradio is an open-source Python package designed for quick prototyping. This vulnerability involves **insecure communication** between the FRP (Fast Reverse Proxy) client and server when Gradio\u0027s `share=True` option is used. HTTPS is not enforced on the connection, allowing attackers to intercept and read files uploaded to the Gradio server, as well as modify responses or data sent between the client and server. This impacts users who are sharing Gradio demos publicly over the internet using `share=True` without proper encryption, exposing sensitive data to potential eavesdroppers. Users are advised to upgrade to `gradio\u003e=5` to address this issue. As a workaround, users can avoid using `share=True` in production environments and instead host their Gradio applications on servers with HTTPS enabled to ensure secure communication.",
"id": "PYSEC-2024-219",
"modified": "2025-01-19T22:22:23.897787+00:00",
"published": "2024-10-10T23:15:03+00:00",
"references": [
{
"type": "ADVISORY",
"url": "https://github.com/gradio-app/gradio/security/advisories/GHSA-279j-x4gx-hfrh"
}
],
"severity": [
{
"score": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/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.
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.
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