Raw vector
CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:H/I:H/A:HCVSS and EPSS are reproduced from their sources (NVD, FIRST EPSS). Risk Priority is our own derived reading, not an NVD score.
Summary
CVE-2024-5565 is a high-severity Code Injection (CWE-94) vulnerability in Jfrog (inferred from references). Its CVSS base score is 8.1 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Command and Scripting Interpreter (T1059); ranked in the top 4% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog.
This vulnerability is AI-related — categorised as LLM Application Platforms; in the LLM/Generative AI Risks risk domain.
The strongest mitigations our analysis identified map to SA-11 (Developer Testing and Evaluation) and SI-10 (Information Input Validation) — see the control section below for these in your framework.
Deeper analysis AI-assisted summary
Synthesised by an AI model from the NVD description and linked references — a reading aid, not an authoritative source.
The vulnerability CVE-2024-5565 affects the Vanna library and stems from insufficient sanitization of external input passed to its prompt function when rendering visualized results. Specifically, supplying crafted input to the library’s “ask” method with the visualize parameter set to True (the default) permits prompt injection that replaces the intended visualization code with attacker-controlled Python statements, resulting in remote code execution. The issue is tracked under CWE-94 and carries a CVSS 3.1 score of 8.1.
An unauthenticated remote attacker can exploit the flaw over the network by sending malicious input directly to any application or service that invokes Vanna’s ask method with visualization enabled. Successful exploitation grants the attacker the ability to execute arbitrary Python code within the context of the vulnerable process, potentially leading to full compromise of confidentiality, integrity, and availability.
Public references point to detailed analysis published by JFrog, which describes the injection vector and confirms the remote code execution path but does not enumerate specific patches or configuration workarounds in the supplied material. The associated EPSS score has remained flat at 0.0649 with no material increase since disclosure.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2024-1479
Vulnerability Data
The Vanna library uses a prompt function to present the user with visualized results, it is possible to alter the prompt using prompt injection and run arbitrary Python code instead of the intended visualization code. Specifically - allowing external input…
more
to the library’s “ask” method with "visualize" set to True (default behavior) leads to remote code execution.
- CWE(s)
AI Security AnalysisAI
- AI Category
- LLM Application Platforms
- Risk Domain
- LLM/Generative AI Risks
- OWASP Top 10 for LLMs 2025
- None mapped
- AI-specific weaknesses CR
- CWE-1427 — Prompt injection (untrusted input to ask) produces code executed without validation.
Mapped by Cyber Resilience · not in NVD. Poisoning and extraction cases are routed to MITRE ATLAS instead of a synthetic CWE.- Classification Reason
- Vanna is an open-source Python library functioning as an AI SQL agent that uses LLMs for natural language queries to generate SQL and visualizations, making it an AI agent integration vulnerable to prompt injection.
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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V1.3.1
Mitigating Controls (NIST 800-53 r5) AI
Developer testing and evaluation finds code paths that accept and execute externally influenced strings.
Input validation directly stops untrusted data from being used to construct executable code without neutralization.
Least privilege limits the damage an injected code fragment can perform once executed.
Requiring documented secure development standards and tools enforces use of safe code-generation APIs and escaping.
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.
PR.PS-06's SDLC practices directly target injection flaws via secure coding and testing (mostly), yet as a single broad outcome it leaves many code-generation specifics unaddressed (partial).
PR.DS-10 protects runtime data confidentiality/integrity but has no bearing on neutralizing externally influenced input during code generation, so neither direction shows any preventive effect.
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.
Banning unapproved code samples and unauthenticated web services, combined with secure-coding standards and SAST, prevents the dynamic generation or inclusion of attacker-supplied code.