Cyber Resilience

CVE-2024-36420

Flowiseai Flowise 1.4.3

Public PoC
Published
01 July 2024
Modified
21 November 2024
Patch / advisory
View advisory
CVSS Score v3.1 7.5
Click a component to see what it means
Raw vectorCVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:N
EPSS Score 0.018 76th percentile
Risk Priority 80 floored blend · peak EPSS

Summary

CVE-2024-36420 is a high-severity Injection (CWE-74) vulnerability in Flowiseai Flowise. Its CVSS base score is 7.5 (High).

Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked in the top 24% 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 Privacy and Disclosure risk domain.

The strongest mitigations our analysis identified map to 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 1.4.3, a drag-and-drop interface for constructing customized large language model flows, is affected by an arbitrary file read vulnerability in the /api/v1/openai-assistants-file endpoint. The flaw resides in packages/server/src/index.ts and stems from missing sanitization of the fileName body parameter, corresponding to CWE-74 and carrying a CVSS 3.1 score of 7.5 for unauthenticated network access that impacts confidentiality.

Remote attackers without credentials can submit crafted POST requests to the endpoint and retrieve arbitrary files from the underlying server filesystem, exposing sensitive configuration or data. The attack requires no user interaction and can be performed directly over the network.

Public references, including the GitHub Security Lab advisory GHSL-2023-232 and the affected source lines, document the injection vector, while the vulnerability record states that no patches are available. The associated EPSS score of 0.5832 reflects sustained exploitation interest for this LLM-related component.

OWASP Top 10 for Web (2025)

A05:2025 Injection

EU & UK References

Vulnerability Data

Flowise is a drag & drop user interface to build a customized large language model flow. In version 1.4.3 of Flowise, the `/api/v1/openai-assistants-file` endpoint in `index.ts` is vulnerable to arbitrary file read due to lack of sanitization of the `fileName`…

more

body parameter. No known patches for this issue are available.

CWE(s)
CWE-74

AI Security AnalysisAI

AI Category
LLM Application Platforms
Risk Domain
Privacy and Disclosure
OWASP Top 10 for LLMs 2025
None mapped
Classification Reason
Flowise is a drag-and-drop user interface for building customized large language model (LLM) flows, specifically integrating with OpenAI Assistants (e.g., /api/v1/openai-assistants-file endpoint), fitting the Enterprise AI Assistants category as a platform for developing and deploying LLM-based assistants.

Related Threats

MITRE ATT&CK Enterprise Techniques

T1190 Exploit Public-Facing Application Initial Access
Adversaries may attempt to exploit a weakness in an Internet-facing host or system to initially access a network.
attack.mitre.org →
T1221 Template Injection Stealth
Adversaries may create or modify references in user document templates to conceal malicious code or force authentication attempts.
attack.mitre.org →
T1659 Content Injection Initial Access
Adversaries may gain access and continuously communicate with victims by injecting malicious content into systems through online network traffic.
attack.mitre.org →
T1674 Input Injection Execution
Adversaries may simulate keystrokes on a victim’s computer by various means to perform any type of action on behalf of the user, such as launching the command interpreter using keyboard shortcuts, typing an inline script to be executed,…
attack.mitre.org →
T1059 Command and Scripting Interpreter Execution
Adversaries may abuse command and script interpreters to execute commands, scripts, or binaries.
attack.mitre.org →
T1059.001 PowerShell Execution
Adversaries may abuse PowerShell commands and scripts for execution.
attack.mitre.org →
Derived from this CVE’s CWE(s) via the direct CWE→ATT&CK cross-walk.

CVEs Like This One

CVE-2023-39659Shared CWE-74
CVE-2023-39661Shared CWE-74
CVE-2023-36188Shared CWE-74
CVE-2023-38896Shared CWE-74
CVE-2023-32786Shared CWE-74
CVE-2023-29374Shared CWE-74
CVE-2023-23749Shared CWE-74
CVE-2023-48835Shared CWE-74
CVE-2023-51939Shared CWE-74
CVE-2023-33242Shared CWE-74

Affected Assets

flowiseai
flowise
1.4.3

Mitigating Controls

Control response

Prevent
Stop it (NIST 800-53)

—

Detect
Catch it (NIST detect / respond)

—

Harden
Shrink the surface (DISA STIG)

—

Validate
Prove the fix (OWASP ASVS)
  • V1.2.1
  • V1.2.3
  • V1.2.5
  • V1.2.8

Mitigating Controls (NIST 800-53 r5) AI

SI-10 Information Input Validation good match

SI-10 directly requires validation of information inputs to reject malformed or special-element content before it reaches downstream parsers.

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 mostly match
prevents

Secure SDLC practices directly require input validation and output encoding that prevent injection flaws.

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.

A.8.29 Security testing in development and acceptance mostly match
finds

Security testing in development catches injection vulnerabilities before release.

A.8.15 Logging partial match
finds

Logging supports detection of injection attempts but does not prevent the weakness.

A.8.16 Monitoring activities partial match
finds

Monitoring activities can identify active injection attacks after they occur.

A.8.25 Secure development life cycle partial match
prevents

Secure development life cycle mandates input validation and output encoding that directly prevent injection flaws.

A.8.26 Application security requirements partial match
prevents

Application security requirements explicitly call for controls against injection attacks in software design.

A.8.27 Secure system architecture and engineering principles partial match
prevents

Secure architecture principles reduce injection surfaces but do not prescribe specific neutralization techniques.

References