PYSEC-2026-66
Vulnerability from pysec - Published: 2026-02-27 22:16 - Updated: 2026-05-20 09:19
VLAI
Details
Gradio is an open-source Python package designed for quick prototyping. Prior to version 6.6.0, a Server-Side Request Forgery (SSRF) vulnerability in Gradio allows an attacker to make arbitrary HTTP requests from a victim's server by hosting a malicious Gradio Space. When a victim application uses gr.load() to load an attacker-controlled Space, the malicious proxy_url from the config is trusted and added to the allowlist, enabling the attacker to access internal services, cloud metadata endpoints, and private networks through the victim's infrastructure. Version 6.6.0 fixes the issue.
Severity
8.6 (High)
Impacted products
| Name | purl | gradio | pkg:pypi/gradio |
|---|
Aliases
{
"affected": [
{
"package": {
"ecosystem": "PyPI",
"name": "gradio",
"purl": "pkg:pypi/gradio"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "6.6.0"
}
],
"type": "ECOSYSTEM"
}
],
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]
}
],
"aliases": [
"CVE-2026-28416",
"GHSA-jmh7-g254-2cq9"
],
"details": "Gradio is an open-source Python package designed for quick prototyping. Prior to version 6.6.0, a Server-Side Request Forgery (SSRF) vulnerability in Gradio allows an attacker to make arbitrary HTTP requests from a victim\u0027s server by hosting a malicious Gradio Space. When a victim application uses `gr.load()` to load an attacker-controlled Space, the malicious `proxy_url` from the config is trusted and added to the allowlist, enabling the attacker to access internal services, cloud metadata endpoints, and private networks through the victim\u0027s infrastructure. Version 6.6.0 fixes the issue.",
"id": "PYSEC-2026-66",
"modified": "2026-05-20T09:19:02.232478Z",
"published": "2026-02-27T22:16:24.667Z",
"references": [
{
"type": "ADVISORY",
"url": "https://github.com/gradio-app/gradio/security/advisories/GHSA-jmh7-g254-2cq9"
}
],
"severity": [
{
"score": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:C/C:H/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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