Raw vector
CVSS:4.0/AV:N/AC:H/AT:N/PR:L/UI:N/VC:H/VI:H/VA:H/SC:H/SI:H/SA:H/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-2026-69253 is a critical-severity Eval Injection (CWE-95) vulnerability in Flowiseai Flowise. Its CVSS base score is 9.0 (Critical).
Operationally, exploitation aligns with the MITRE ATT&CK technique Command and Scripting Interpreter (T1059); ranked at the 49th percentile by exploit likelihood (below the median); 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.
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.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-52739
Vulnerability Data
Flowise is a drag-and-drop user interface for building customized large language model (LLM) flows. Prior to version 3.1.3, several custom-tool components — AgentAsTool, ChatflowTool, and ExecuteFlow — ran code in the in-process vm2 sandbox. To build that code, they inserted…
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a user-controlled baseURL value straight into the JavaScript source, for example const url = "${baseURL}/..."; . The only check on baseURL was isValidURL , but a valid-looking URL can still contain characters that break out of a code string. An authenticated user could craft a baseURL that passed this check, closed the surrounding string, and injected their own JavaScript into the sandboxed script (code injection, CWE-94). The vm2 sandbox runs in the same Node.js process as Flowise and exposes risky dependencies. As a result, the injected code could escape the sandbox and run arbitrary code on the Flowise server as the Flowise process user. Exploitation only requires an authenticated session. The issue is fixed in version 3.1.3, which passes the URL to the sandbox as data instead of inserting it into code and adds stricter URL validation.
- CWE(s)
AI Security AnalysisAI
- AI Category
- LLM Application Platforms
- Risk Domain
- N/A
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: large language model, llm
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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V1.3.2
Mitigating Controls (NIST 800-53 r5) AI
Developer testing and code analysis can discover eval-injection flaws but does not stop their introduction.
Input validation explicitly requires neutralizing untrusted data before it reaches dynamic evaluation constructs such as eval.
Secure-development standards and tools can mandate safe coding patterns that avoid unsafe dynamic evaluation.
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 input neutralization and avoidance of unsafe dynamic evaluation.
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.
Secure development life cycle mandates input validation and safe coding practices that directly prevent eval injection.
Application security requirements include rules against dynamic code execution of untrusted input.
Secure architecture principles discourage unsafe dynamic evaluation constructs.
Secure coding explicitly requires neutralization of input before dynamic evaluation, directly mitigating eval injection.
Security testing in development can detect eval injection vulnerabilities before deployment.