Vulnerability Assessment
| Weakness |
CWE
OWASP Top 10:2025
A07:2025 - Authentication Failures
ASVS 5.0
V6 - Authentication Brute-force prevention, rate limiting
|
| Impact | The CVSS v4.0 score of 2.7 reflects a network-accessible vulnerability with low complexity (AC:L), no authentication required (PR:N), and limited integrity impact (VI:L) but no confidentiality or availability impact. The EPSS and KEV status are not provided, and no public exploit code has been independently referenced in available data. However, risk assessment must account for multiple factors: (1) brute-force attacks against authentication systems are highly automatable and do not require sophisticated tooling, making real-world exploitation probability high despite low CVSS; (2) the attack requires no user interaction and operates at the network layer, removing barriers to exploitation; (3) the target user base consists of external healthcare professionals with high credential reuse likelihood, increasing the utility of successful compromise; (4) as a medical imaging AI platform, compromised accounts could enable tampering with training datasets or model integrity across multiple healthcare institutions. The low CVSS score underestimates practical risk-this is a high-priority vulnerability for environments handling sensitive healthcare data despite formal severity metrics. |
| Exploit ScenarioDerived | An attacker obtains a list of email addresses of FLIP users from public healthcare institution employee directories or conference registrations, then uses automated tools to attempt password guesses against the FLIP login endpoint. Because the platform lacks rate limiting, the attacker can submit thousands of credential combinations per hour without triggering account lockout or IP-based blocking. Upon successful compromise of a user account (especially administrative or research lead roles), the attacker gains access to federated training datasets, model weights, or institutional affiliations within the medical imaging AI pipeline, potentially enabling data exfiltration, model poisoning, or lateral movement to connected healthcare systems. |
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Alert meAnalysisAIv1 – Mar 27, 2026
The FLIP login page in versions 0.1.1 and prior lacks rate limiting and CAPTCHA protection, enabling unauthenticated remote attackers to conduct brute-force and credential-stuffing attacks against user accounts. The vulnerability affects the Federated Learning and Interoperability Platform, an open-source medical imaging AI training system where users are typically external to host organizations, amplifying the risk of credential reuse. While the CVSS score is low (2.7), the attack vector is network-based, requires no authentication or interaction, and directly enables unauthorized account access with potential integrity impact.
RemediationAI
Implement immediate compensating controls: deploy a web application firewall or reverse proxy with rate-limiting rules (e.g., 5 failed attempts per 15 minutes per source IP, account lockout after 10 failures) and enable CAPTCHA challenges after 3 consecutive failed login attempts. Upgrade to the patched version once available-check the GitHub security advisory at https://github.com/londonaicentre/FLIP/security/advisories/GHSA-p34f-488j-5cwv for release details. In the interim, consider requiring multi-factor authentication for all user accounts to reduce the impact of credential compromise, and audit login logs for suspicious authentication patterns indicating active brute-force attempts. No vendor-released patch version is independently confirmed at time of analysis; contact London AI Centre via the GitHub advisory channel for patch availability and timeline.
Affected VersionsENISA
Affected Packages
Source: OSV (Open Source Vulnerabilities)
Affects your stack
Affects 1 package in euvd
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Technical ContextAI · show details
The vulnerability stems from inadequate input validation and missing rate-limiting controls on the authentication endpoint (CWE-307: Improper Restriction of Rendered UI Layers or Frames). FLIP is a federated learning platform designed for collaborative medical imaging AI model training across healthcare institutions. The login mechanism lacks protective mechanisms such as exponential backoff, account lockout, or CAPTCHA challenges that would slow or block repeated authentication attempts. This is a classic implementation flaw in the authentication layer where the platform prioritizes availability over brute-force resilience. The affected product is distributed as open-source software via GitHub (CPE: cpe:2.3:a:londonaicentre:flip:*:*:*:*:*:*:*:*), making the vulnerability and attack surface publicly visible to all developers and potential attackers.
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Severity by source
CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:N/VI:L/VA:N/SC:N/SI:N/SA:N/E:U
Primary rating from Vendor (GitHub_M) · only source for this CVE.
CVSS VectorVendor: GitHub_M
DescriptionCVE.org
Federated Learning and Interoperability Platform (FLIP) is an open-source platform for federated training and evaluation of medical imaging AI models across healthcare institutions. The FLIP login page in versions 0.1.1 and prior has no rate limiting or CAPTCHA, enabling brute-force and credential-stuffing attacks. FLIP users are external to the organization, increasing credential reuse risk. As of time of publication, it is unclear if a patch is available.
Executive ViewAI
CVE-2026-33879 is a low-severity vulnerability in page in (CVSS 2.7). The limited severity suggests constrained impact, typically requiring specific conditions or local access for exploitation. Apply vendor updates when available as part of routine maintenance.
Apply vendor patch during routine maintenance. Standard security controls provide adequate interim protection.
Threat Intelligence Overview
How this decision was reached
The engine reads each signal below and applies a fixed list of rules from top to bottom. The first rule that matches sets the recommended action – like a statement, every line is shown.
What every action level means
SSVC AssessmentENISA
Priority Score
Attack Technique
Information disclosure occurs when an application unintentionally exposes sensitive data that aids attackers in reconnaissance or directly compromises security.
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EUVD-2026-16818
NVD
CVE.org
EPSS
Vulnrichment
GitHub
VulDB
Exploit-DB