CVE-2026-28416

GHSA-jmh7-g254-2cq9 HIGH
Published February 27, 2026
CISO Take

Any Gradio deployment using `gr.load()` to load external or community Spaces is exposed to SSRF attacks that can reach cloud metadata endpoints (AWS IMDS, GCP metadata) and internal network services — a direct path to IAM credential theft and cloud account takeover. Patch to Gradio 6.6.0 immediately; if delay is unavoidable, restrict `gr.load()` to internal/trusted sources only and block egress to 169.254.169.254 at the network layer. Cloud-hosted ML environments are highest priority — this is not a theoretical risk.

Affected Systems

Package Ecosystem Vulnerable Range Patched
gradio pip < 6.6.0 6.6.0
gradio pip No patch
gradio pip No patch

Severity & Risk

CVSS 3.1
8.6 / 10
EPSS
0.0%
chance of exploitation in 30 days
KEV Status
Not in KEV
Sophistication
Trivial

Recommended Action

  1. 1) Patch: upgrade to Gradio 6.6.0 immediately — this is the only full fix. 2) If patching is delayed: audit all gr.load() calls and whitelist only internal, verified Spaces; remove or gate any untrusted external Space loading. 3) Network controls: block outbound HTTP from Gradio servers to RFC1918 ranges and cloud metadata endpoints (169.254.169.254, metadata.google.internal, 169.254.169.254). 4) Least privilege: review and restrict IAM roles attached to instances hosting Gradio — ensure no overly permissive roles exist that SSRF-harvested credentials could abuse. 5) Detection: alert on outbound HTTP requests from Gradio processes to metadata ranges and internal subnets; review Gradio access logs for unexpected proxy_url patterns. 6) Incident response: if exposure is suspected, rotate all IAM credentials associated with affected Gradio hosts.

Classification

Compliance Impact

This CVE is relevant to:

EU AI Act
Article 15 - Accuracy, robustness and cybersecurity Article 9 - Risk management system
ISO 42001
A.6.1.4 - Information security in AI system lifecycle A.6.2.6 - AI system access control Clause 8.4 - AI System Operational Risk Management
NIST AI RMF
GOVERN 6.2 - Policies and procedures are in place for AI supply chain risk management MANAGE 2.4 - Residual risks are managed MANAGE-2.2 - Risk Treatment for AI System Vulnerabilities
OWASP LLM Top 10
LLM05 - Supply Chain Vulnerabilities LLM07 - Insecure Plugin Design

Technical Details

NVD Description

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.

Exploitation Scenario

An attacker publishes a malicious Gradio Space on Hugging Face with a config embedding `proxy_url: http://169.254.169.254/latest/meta-data/iam/security-credentials/prod-ml-role`. A security engineer at a target org runs `gr.load('attacker/demo-model')` to evaluate the Space during routine model vetting. Gradio trusts the returned proxy_url and adds it to the allowlist. The attacker then proxies requests through the victim's server to the metadata endpoint, harvesting temporary AWS IAM credentials for the `prod-ml-role` attached to the Gradio host. With those credentials, the attacker pivots to S3 buckets containing proprietary training data, model artifacts, and customer datasets — achieving data exfiltration with no direct access to victim infrastructure.

CVSS Vector

CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:N/A:N

Timeline

Published
February 27, 2026
Last Modified
March 5, 2026
First Seen
February 27, 2026