Mage AI is widely used by data scientists and developers to build, train, and deploy machine learning models efficiently. It enables users to create powerful AI models with minimal coding efforts, making it accessible for those without deep technical expertise. Organizations utilize Mage AI in various fields, including finance, healthcare, and marketing, for predictive analytics and automated decision-making processes. The software simplifies handling data, allowing for seamless integration into existing project workflows. Additionally, Mage AI includes tools for collaborative model development, enhancing team productivity. It is a valuable asset for companies aiming to leverage artificial intelligence to gain a competitive edge in their respective industries.
The identified vulnerability concerns an insecure default authentication setup within Mage AI. This flaw might be exploited by attackers to gain unauthorized access without initial credential verification. Although the attack requires a relatively high complexity, successful exploitation can have significant implications. The vulnerability arises from improper initialization of authentication parameters, allowing potential remote manipulation. Despite being publicly disclosed, the issue remains unresolved due to the vendor's decision not to address it as a security concern. Users of Mage AI version 0.9.75 are particularly at risk if this default setup is not altered.
The vulnerability lies in the improper setup of authentication settings, which can lead to unauthorized access if not corrected. The focal point of exploitation typically involves endpoints that handle authentication requests. Attackers might manipulate the initial setup to bypass security measures and obtain access to critical resources. This can be executed remotely, highlighting the need for robust configurations. The difficulty in recognition is partly due to the lack of immediate indicators of exploitation, requiring careful monitoring of system access logs. Despite its complexity, the vulnerability underscores the potential risk in default system settings.
If exploited, the vulnerability could lead to unauthorized access to sensitive data and systems. Attackers may leverage this access to execute further attacks, such as data exfiltration or deployment of malicious software. The breach of security could compromise the integrity of AI models and their output, affecting decision-making processes based on these models. Additionally, exploitation might enable attackers to manipulate datasets or training processes, leading to biased or incorrect model results. Organizations could also face reputational damage and potential financial losses due to data breaches.
REFERENCES
- https://nvd.nist.gov/vuln/detail/CVE-2025-2129
- https://github.com/zn9988/publications/blob/main/2.Mage-AI%%%%20-%%%%20Insecure%%%%20Default%%%%20Authentication%%%%20Setup%%%%20Leading%%%%20to%%%%20Zero-Click%%%%20RCE/README.md
- https://vuldb.com/?ctiid.299049
- https://vuldb.com/?id.299049
- https://vuldb.com/?submit.510690
- Implement strong authentication mechanisms such as multi-factor authentication (MFA) to enhance security.
- Regularly review and update default authentication configurations to align with best security practices.
- Conduct thorough access logging and monitoring to detect unauthorized activities promptly.
- Educate users on the risks associated with default settings and encourage regular auditing of security parameters.
- Consider applying patches or updates as recommended by security advisories for related vulnerabilities.
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