CVE-2024-47870
Race Condition in Gradio Project Gradio ≤ 5.0.0
Raw vector
CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:H/VI:N/VA:N/SC:N/SI:N/SA:N/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:XSummary
CVE-2024-47870 is a high-severity Race Condition (CWE-362) vulnerability in Gradio Project Gradio. Its CVSS base score is 7.1 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploitation for Privilege Escalation (T1068); ranked at the 30th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
This vulnerability is AI-related — categorised as Other AI Platforms; in the Privacy and Disclosure risk domain.
The strongest mitigations our analysis identified map to SC-39 (Process Isolation) and SC-4 (Information in Shared System Resources) — see the control section below for these in your framework.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2024-0073
Vulnerability Data
Gradio is an open-source Python package designed for quick prototyping. This vulnerability involves a **race condition** in the `update_root_in_config` function, allowing an attacker to modify the `root` URL used by the Gradio frontend to communicate with the backend. By exploiting…
more
this flaw, an attacker can redirect user traffic to a malicious server. This could lead to the interception of sensitive data such as authentication credentials or uploaded files. This impacts all users who connect to a Gradio server, especially those exposed to the internet, where malicious actors could exploit this race condition. Users are advised to upgrade to `gradio>=5` to address this issue. There are no known workarounds for this issue.
- CWE(s)
AI Security AnalysisAI
- AI Category
- Other AI Platforms
- Risk Domain
- Privacy and Disclosure
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Gradio is an open-source platform for creating web-based UIs and demos for machine learning models, fitting under 'Other Platforms' as it enables rapid prototyping and deployment of AI/ML applications.
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
—
—
—
V10.4.2V10.4.5V15.1.3V15.4.1
Mitigating Controls (NIST 800-53 r5) AI
Maintaining separate execution domains for each process structurally eliminates unintended concurrent access to the same shared resources.
Preventing unintended information transfer through shared system resources directly addresses the improper concurrent modification that defines a race condition.
Mitigating Controls (NIST CSF 2.0) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→CSF cross-walk (authority under review) — links open the control.
Secure SDLC practices directly require proper synchronization primitives and concurrency testing that prevent race conditions.
Mitigating Controls (ISO/IEC 27001:2022 Annex A) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→ISO cross-walk (authority under review) — links open the control.
Security testing can detect race conditions, but does not prevent them at design or coding time.
Secure SDLC mandates concurrency controls and synchronization primitives that directly prevent race conditions.
Application security requirements can specify thread-safety and locking rules, but do not prescribe implementation details.
Secure architecture principles require proper synchronization and resource isolation, addressing the root cause of CWE-362.
Secure coding standards explicitly forbid unsafe concurrent access patterns and mandate atomic operations or locks.
Change management reduces introduction of concurrency bugs during updates, yet does not address the weakness itself.