CVE-2024-47168
Gradio Project Gradio ≤ 4.44.0
Raw vector
CVSS:4.0/AV:N/AC:L/AT:P/PR:L/UI:N/VC:L/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:XCVSS and EPSS are reproduced from their sources (NVD, FIRST EPSS). Risk Priority is our own derived reading, not an NVD score.
Summary
CVE-2024-47168 is a low-severity Always-Incorrect Control Flow Implementation (CWE-670) vulnerability in Gradio Project Gradio. Its CVSS base score is 2.3 (Low).
Operationally, ranked at the 26th 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 SA-11 (Developer Testing and Evaluation) and SA-15 (Development Process, Standards, and Tools) — see the control section below for these in your framework.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2024-0066
Vulnerability Data
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…
more
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.
- 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 Python package for quick prototyping of web interfaces for machine learning models, fitting as an 'Other Platforms' category in the AI ecosystem.
Related Threats
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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V9.2.1
Mitigating Controls (NIST 800-53 r5) AI
Developer testing and evaluation at post-design stages directly exercises control-flow paths and reveals mismatches between implemented and intended behavior.
Requiring a documented development process and supporting tools reduces the chance that incorrect control-flow logic is introduced in the first place.
Flaw identification and remediation processes can locate and correct control-flow errors once they manifest as incorrect runtime behavior.
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 prevent incorrect control-flow implementations via reviews, testing, and static analysis.
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 in development and acceptance can detect paths that deviate from intended logic.
Secure development lifecycle processes can catch incorrect control-flow logic during design and code review.
Secure coding standards and reviews directly target flawed control-flow implementations.
Change-management gates may prevent deployment of flawed logic but do not address the coding defect itself.