CVE-2026-33879
Brute-Force Vulnerability in FLIP Login Due to Missing Rate Limiting
Publication date: 2026-03-27
Last updated on: 2026-04-08
Assigner: GitHub, Inc.
Description
Description
CVSS Scores
EPSS Scores
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Meta Information
Affected Vendors & Products
| Vendor | Product | Version / Range |
|---|---|---|
| aicentre | federated_learning_and_interoperability_platform | to 0.1.1 (exc) |
Helpful Resources
Exploitability
| CWE ID | Description |
|---|---|
| CWE-307 | The product does not implement sufficient measures to prevent multiple failed authentication attempts within a short time frame. |
Attack-Flow Graph
AI Powered Q&A
Can you explain this vulnerability to me?
The vulnerability in CVE-2026-33879 affects the Federated Learning and Interoperability Platform (FLIP), specifically its login page in versions 0.1.1 and prior.
The login page lacks rate limiting and CAPTCHA protections, which allows attackers to perform brute-force and credential-stuffing attacks.
Since FLIP users are external to the organization and may reuse credentials, this increases the risk of unauthorized access.
Currently, only a partial mitigation exists that prevents username enumeration via error messages, but no effective protection against brute-force attacks is implemented.
How can this vulnerability impact me? :
This vulnerability can allow attackers to gain unauthorized access to user accounts by repeatedly attempting to guess passwords without being blocked or slowed down.
Because FLIP users are external and may reuse credentials from other services, successful brute-force or credential-stuffing attacks could lead to compromised accounts.
Such unauthorized access could lead to exposure or manipulation of sensitive medical imaging AI model data shared across healthcare institutions.
How can this vulnerability be detected on my network or system? Can you suggest some commands?
This vulnerability can be detected by monitoring for signs of brute-force or credential-stuffing attacks on the FLIP login page, which lacks rate limiting and CAPTCHA protections.
You can look for repeated failed login attempts from the same IP address or unusual login patterns indicating automated attacks.
Since the FLIP authentication uses AWS Cognito without Advanced Security Mode enabled, checking Cognito logs for multiple failed authentication attempts can help detect exploitation attempts.
Suggested commands include using AWS CLI to review Cognito sign-in events or CloudWatch logs for repeated failures, for example:
- aws cognito-idp list-users --user-pool-id <user-pool-id> --filter "status=""FAILED"""
- aws logs filter-log-events --log-group-name <cognito-log-group> --filter-pattern "Failed authentication"
Additionally, network monitoring tools can be used to detect high volumes of login requests from single IPs.
What immediate steps should I take to mitigate this vulnerability?
Immediate mitigation steps focus on enabling protections to prevent brute-force and credential-stuffing attacks on the FLIP login page.
- Enable AWS Cognito Advanced Security Features using the AWS CLI command `aws cognito-idp update-user-pool` to configure account takeover risk settings.
- Configure account lockout after 5 failed login attempts to block attackers from unlimited tries.
- Activate compromised credential protection and adaptive authentication to require MFA for medium risk and block access for high risk.
- Optionally, implement AWS WAF rate limiting on the Application Load Balancer to limit login attempts per IP address (e.g., 100 requests).
- Integrate CAPTCHA (such as reCAPTCHA v3) on the login form to prevent automated login attempts.
- Implement rate limiting middleware in the FastAPI application (e.g., slowapi or fastapi-limiter) to add an additional layer of protection.
How does this vulnerability affect compliance with common standards and regulations (like GDPR, HIPAA)?:
The vulnerability in FLIP's login page, which lacks rate limiting and CAPTCHA protections, enables brute-force and credential-stuffing attacks. Since FLIP users are external and may reuse credentials, this increases the risk of unauthorized access to sensitive medical imaging AI models and potentially sensitive healthcare data.
Such unauthorized access risks can negatively impact compliance with common standards and regulations like GDPR and HIPAA, which require appropriate security measures to protect personal and health information from unauthorized access and breaches.
The absence of effective rate limiting, account lockout, and compromised credential protection means that the platform does not currently meet best practices for access control and threat mitigation, which are often mandated or recommended by these regulations.