Raw vector
CVSS:3.1/AV:N/AC:L/PR:H/UI:N/S:U/C:H/I:H/A:HSummary
CVE-2024-10252 is a high-severity Code Injection (CWE-94) vulnerability in Langgenius Dify. Its CVSS base score is 7.2 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Command and Scripting Interpreter (T1059); ranked in the top 48% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
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.
CVE-2024-10252 is a code injection vulnerability (CWE-94) in langgenius/dify versions up to and including v0.9.1. The issue resides in the Dify sandbox service, where internal SSRF requests can be abused to execute arbitrary Python code with root privileges within the sandbox environment. This flaw carries a CVSS v3.1 base score of 7.2 (AV:N/AC:L/PR:H/UI:N/S:U/C:H/I:H/A:H), indicating high severity due to its potential for significant impact.
Exploitation requires high privileges (PR:H) and is feasible over the network with low complexity and no user interaction. An attacker with sufficient access can leverage the SSRF to inject and run malicious Python code as root in the sandbox, potentially deleting the entire sandbox service and inflicting irreversible damage with high confidentiality, integrity, and availability impacts.
The vulnerability was addressed via a patch in this GitHub commit: https://github.com/langgenius/dify/commit/4ac99ffe0e1c9f4d7c523908e91bbc7739e0a8d4. Further details, including the report, are available on the Huntr bounty page: https://huntr.com/bounties/62c6c958-96cb-426c-aebc-c41f06b9d7b0. Affected deployments should apply the patch by upgrading beyond v0.9.1.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-7125
Vulnerability Data
A vulnerability in langgenius/dify versions <=v0.9.1 allows for code injection via internal SSRF requests in the Dify sandbox service. This vulnerability enables an attacker to execute arbitrary Python code with root privileges within the sandbox environment, potentially leading to the…
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deletion of the entire sandbox service and causing irreversible damage.
- CWE(s)
AI Security AnalysisAI
- AI Category
- LLM Application Platforms
- Risk Domain
- LLM/Generative AI Risks
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: dify
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.
Controls that restrict unauthorized or malicious code from being introduced via external networks or removable media limit opportunities for an attacker to inject and execute arbitrary code.