Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:H/A:HSummary
CVE-2025-59528 is a critical-severity Code Injection (CWE-94) vulnerability in Flowiseai Flowise. Its CVSS base score is 10.0 (Critical).
Operationally, exploitation aligns with the MITRE ATT&CK technique Command and Scripting Interpreter (T1059); ranked in the top 0.2% 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.
Flowise version 3.0.5 contains a remote code execution vulnerability in its CustomMCP node, which accepts an mcpServerConfig string from users to configure connections to external MCP servers. The convertToValidJSONString function passes this input directly to the JavaScript Function() constructor for evaluation, granting the executed code full access to the Node.js runtime and modules such as child_process and fs. The flaw is an instance of CWE-94 code injection and carries a CVSS 3.1 score of 10.0.
An attacker with network access to a Flowise instance can supply a malicious configuration string that executes arbitrary JavaScript. Because the code runs with the privileges of the Flowise server process, successful exploitation yields full control over the host, including the ability to spawn processes, read or write files, and exfiltrate data.
The project has released version 3.0.6 to address the issue. The references point to the specific lines in CustomMCP.ts and related server-side node handling code where the unsafe evaluation occurs.
The vulnerability affects an LLM workflow builder and shows an EPSS score that has reached 0.87, indicating substantial and sustained exploitation interest since disclosure.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-29246
Vulnerability Data
Flowise is a drag & drop user interface to build a customized large language model flow. In version 3.0.5, Flowise is vulnerable to remote code execution. The CustomMCP node allows users to input configuration settings for connecting to an external…
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MCP server. This node parses the user-provided mcpServerConfig string to build the MCP server configuration. However, during this process, it executes JavaScript code without any security validation. Specifically, inside the convertToValidJSONString function, user input is directly passed to the Function() constructor, which evaluates and executes the input as JavaScript code. Since this runs with full Node.js runtime privileges, it can access dangerous modules such as child_process and fs. This issue has been patched in version 3.0.6.
- 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: flowise, large language model, mcp
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.