Cyber Resilience

CVE-2026-41278

Info Disclosure in Flowiseai Flowise ≤ 3.1.0

Public PoCInfo Disclosure
Published
23 April 2026
Modified
24 April 2026
Patch / advisory
CVSS Score v4 8.7
Click a component to see what it means
Raw vectorCVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:H/VI:N/VA:N/SC:N/SI:N/SA:N/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:X
EPSS Score 0.0042 36th percentile
Risk Priority 44 floored blend · peak EPSS

CVSS and EPSS are reproduced from their sources (NVD, FIRST EPSS). Risk Priority is our own derived reading, not an NVD score.

Summary

CVE-2026-41278 is a high-severity Exposure of Sensitive Information to an Unauthorized Actor (CWE-200) vulnerability in Flowiseai Flowise. Its CVSS base score is 8.7 (High).

Operationally, exploitation aligns with the MITRE ATT&CK technique Token Impersonation/Theft (T1134.001); ranked at the 36th 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; in the Privacy and Disclosure risk domain.

The strongest mitigations our analysis identified map to AC-3 (Access Enforcement) and AC-4 (Information Flow Enforcement) — 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-2026-41278 is a vulnerability in Flowise, an open-source drag-and-drop user interface for building customized large language model (LLM) flows. In versions prior to 3.1.0, including the v3.0.13 Docker image, the GET /api/v1/public-chatflows/:id endpoint exposes the full chatflow object without sanitization for public chatflows. Additionally, the public-chatbotConfig endpoint returns completely raw flowData, disclosing sensitive information such as credential IDs, plaintext API keys, and password-type fields. This issue stems from the absence of the sanitizeFlowDataForPublicEndpoint function in released images and is classified as CWE-200 (Exposure of Sensitive Information to an Unauthorized Actor), with a CVSS v3.1 base score of 7.5 (AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:N).

The vulnerability can be exploited by any unauthenticated attacker with network access who knows or guesses the public chatflow ID. By sending a simple GET request to the affected endpoints, attackers can retrieve raw flowData containing highly sensitive credentials, enabling unauthorized access to integrated services like LLM APIs. No user interaction or privileges are required, making it straightforward to extract and misuse API keys or other secrets embedded in the chatflows.

The Flowise security advisory at https://github.com/FlowiseAI/Flowise/security/advisories/GHSA-w47f-j8rh-wx87 confirms the issue and states that it is fixed in version 3.1.0, recommending immediate upgrades for all prior installations, particularly those exposing public chatflows.

This vulnerability is particularly relevant to AI/ML deployments, as Flowise is designed for LLM orchestration, potentially exposing keys to external AI services in production environments. No public evidence of real-world exploitation has been reported as of the CVE publication on 2026-04-23.

OWASP Top 10 for Web (2025)

EU & UK References

Vulnerability Data

Flowise is a drag & drop user interface to build a customized large language model flow. Prior to 3.1.0, the GET /api/v1/public-chatflows/:id endpoint returns the full chatflow object without sanitization for public chatflows. Docker validation revealed this is worse than…

more

initially assessed: the sanitizeFlowDataForPublicEndpoint function does NOT exist in the released v3.0.13 Docker image. Both public-chatflows AND public-chatbotConfig return completely raw flowData including credential IDs, plaintext API keys, and password-type fields. This vulnerability is fixed in 3.1.0.

CWE(s)

AI Security AnalysisAI

AI Category
LLM Application Platforms
Risk Domain
Privacy and Disclosure
OWASP Top 10 for LLMs 2025
None mapped
Classification Reason
Matched keywords: flowise, large language model

Related Threats

MITRE ATT&CK Enterprise Techniques

T1134.001 Token Impersonation/Theft Stealth
Adversaries may duplicate then impersonate another user's existing token to escalate privileges and bypass access controls.
T1615 Group Policy Discovery Discovery
Adversaries may gather information on Group Policy settings to identify paths for privilege escalation, security measures applied within a domain, and to discover patterns in domain objects that can be manipulated or used to blend in the…
T1007 System Service Discovery Discovery
Adversaries may try to gather information about registered local system services.
T1016 System Network Configuration Discovery Discovery
Adversaries may look for details about the network configuration and settings, such as IP and/or MAC addresses, of systems they access or through information discovery of remote systems.
T1018 Remote System Discovery Discovery
Adversaries may attempt to get a listing of other systems by IP address, hostname, or other logical identifier on a network that may be used for Lateral Movement from the current system.
T1033 System Owner/User Discovery Discovery
Adversaries may attempt to identify the primary user, currently logged in user, set of users that commonly uses a system, or whether a user is actively using the system.
Derived from this CVE’s CWE(s) via the direct CWE→ATT&CK cross-walk.

CVEs Like This One

CVE-2026-46443Same product: Flowiseai Flowise
CVE-2026-70478Same product: Flowiseai Flowise
CVE-2026-73604Same product: Flowiseai Flowise
CVE-2026-70473Same product: Flowiseai Flowise
CVE-2026-41266Same product: Flowiseai Flowise
CVE-2026-8028Same product: Flowiseai Flowise
CVE-2026-8026Same product: Flowiseai Flowise
CVE-2026-46440Same product: Flowiseai Flowise
CVE-2024-36421Same product: Flowiseai Flowise
CVE-2026-70472Same product: Flowiseai Flowise

Affected Assets

flowiseai
flowise
≤ 3.1.0

Mitigating Controls

Control response

Prevent
Stop it (NIST 800-53)

—

Detect
Catch it (NIST detect / respond)

—

Harden
Shrink the surface (DISA STIG)
  • 6 hardening rules · 5 OS baselines
Validate
Prove the fix (OWASP ASVS)
  • V10.4.9
  • V11.7.1
  • V14.1.2
  • V14.2.4

Mitigating Controls (NIST 800-53 r5) AI

Access enforcement directly stops unauthorized actors from obtaining sensitive information.

Information flow enforcement structurally prevents sensitive data from reaching unauthorized recipients.

Protection of information at rest prevents unauthorized exposure of stored sensitive data.

Transmission confidentiality mechanisms stop exposure of sensitive data on the wire.

Least privilege reduces the set of actors who can reach sensitive information.

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.AA-05 mostly match
prevents

PR.AA-05 directly enforces least-privilege authorization that blocks most unauthorized disclosures, yet CWE-200 also arises from logging, error messages, and side-channel paths that access controls alone do not address.

PR.DS-10 mostly match
prevents

PR.DS-10 mostly prevents CWE-200 by directly eliminating unauthorized access to sensitive data-in-use, yet only partially addresses the weakness because CWE-200 spans many other exposure vectors outside runtime protection.

PR.IR-01 mostly match
prevents

PR.IR-01's segmentation/zero-trust controls largely eliminate network-level unauthorized access paths that enable exposure, yet CWE-200 spans many additional vectors (API responses, logs, app logic) that network controls alone cannot close.

PR.PS-06 mostly match
prevents

Secure SDLC practices catch most exposure flaws via design, testing and release controls, yet CWE-200 spans runtime/config issues a single development outcome cannot fully close.

PR.AA-01 partial match
prevents

PR.AA-01 supplies proper credential lifecycle controls that reduce unauthorized access paths, yet leaves many other exposure vectors (error messages, logging, side channels, etc.) unaddressed.

PR.AA-03 partial match
prevents

Authentication verifies actor identity and is a prerequisite for access decisions, yet addresses only one facet of the broad set of exposure vectors in CWE-200.

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.

prevents

Clear rules on handling copies, storage, and disposal of classified information lower the likelihood that sensitive data will be left accessible to unauthorized actors.

prevents

Requiring the return of all physical and electronic assets plus secure deletion of organizational data from personal devices reduces the chance that sensitive information remains accessible to departing personnel.

prevents

By requiring owners to assign sensitivity labels and corresponding handling rules, the control ensures that information is not left unmarked and therefore reduces the chance that sensitive data will be exposed to unauthorized actors.

prevents

By requiring classification labels and metadata on all information assets, the control makes it harder for sensitive data to be inadvertently exposed because downstream systems and users can recognize and enforce handling rules based on those labels.

prevents

Requiring encryption, access controls, and recipient authentication for transfers directly reduces the chance that sensitive data reaches an unauthorized observer.

prevents

Secure delivery, protected storage, and confidentiality of allocation records limit exposure of authentication material to unauthorized observers.

Hardening callouts derived

Configuration rules from DISA STIG baselines that bear on weaknesses of the type cited by this CVE. Each rule is shown with the relationship its mapping actually records, against the CWE it was authored against. Derived via CVE→CWE over `controls_xwalks` (higher-evidence rows only; rows rated `none` are excluded).

Ubuntu 22.04 (1 rule)
  • V-260470 Ubuntu 22.04 LTS, when booted, must require authentication upon booting into single-user and maintenance modes. prevents CWE-200
Ubuntu 24.04 (2 rules)
  • V-270647 Ubuntu 24.04 LTS must not have the telnet package installed. prevents CWE-200
  • V-270675 Ubuntu 24.04 LTS when booted must require authentication upon booting into single-user and maintenance modes. prevents CWE-200
Windows 10 (1 rule)
  • V-220737 Administrative accounts must not be used with applications that access the Internet, such as web browsers, or with potential Internet sources, such as email. prevents CWE-200
Windows Server 2016 (1 rule)
  • V-224974 Domain-created Active Directory Organizational Unit (OU) objects must have proper access control permissions. prevents CWE-200
Windows Server 2019 (1 rule)
  • V-205743 Windows Server 2019 organization created Active Directory Organizational Unit (OU) objects must have proper access control permissions. prevents CWE-200

References