CVE-2022-24757 is a vulnerability in the Jupyter Server, specifically versions prior to 1.15.4. Jupyter Server acts as the backend for Jupyter web applications, providing core services, APIs, and REST endpoints. The vulnerability arises from the server's logging behavior when a 5xx server error occurs. During such errors, the server logs sensitive information including authentication cookies and other HTTP header values by default. These logs are accessible without requiring root privileges, meaning that any unauthorized user or attacker with access to the log files can potentially extract sensitive authentication tokens. This exposure enables attackers to impersonate legitimate users and gain unauthorized access to the Jupyter Server environment. Since Jupyter Server is widely used in data science, research, and development environments, unauthorized access could lead to exposure of sensitive data, intellectual property, or allow further lateral movement within an organization’s infrastructure. The vulnerability is classified under CWE-532, which pertains to the insertion of sensitive information into log files. The issue was patched in version 1.15.4 of Jupyter Server. No known exploits have been reported in the wild, and no workarounds exist other than upgrading to the patched version. The vulnerability does not require user interaction but does require that an attacker can access the logs, which may be possible if the attacker has some level of access to the system or can trigger 5xx errors remotely to induce logging of sensitive data. Overall, this vulnerability represents a risk of credential leakage through improper logging practices in a widely used open-source server component.

For European organizations, the impact of this vulnerability can be significant, especially for institutions relying heavily on Jupyter Server for data analysis, scientific research, or machine learning workloads. Unauthorized access to Jupyter Server instances could lead to exposure of sensitive research data, intellectual property, or personal data protected under GDPR. Attackers leveraging stolen authentication cookies could bypass authentication controls, leading to potential data theft, manipulation, or disruption of critical workflows. Given that Jupyter Server is often deployed in academic, governmental, and private sector environments, the risk extends to sectors such as healthcare, finance, and critical infrastructure where data confidentiality and integrity are paramount. Additionally, since logs are typically accessible to multiple users or services, insider threats or compromised accounts could exploit this vulnerability to escalate privileges or move laterally within networks. The lack of known exploits in the wild reduces immediate risk but does not eliminate the potential for targeted attacks, especially as threat actors often reverse-engineer disclosed vulnerabilities. The vulnerability’s exploitation could also undermine trust in data science platforms and delay research or operational activities due to incident response and remediation efforts.

The primary and most effective mitigation is to upgrade all Jupyter Server instances to version 1.15.4 or later, where the logging of sensitive authentication information during 5xx errors has been corrected. Organizations should implement strict access controls on log files to limit who can read them, ideally restricting access to only trusted administrators. Monitoring and alerting should be enhanced to detect unusual 5xx error rates or suspicious access patterns to log files. Additionally, organizations should consider isolating Jupyter Server environments within segmented network zones to reduce the risk of unauthorized log access. Employing centralized log management solutions with role-based access control can further protect sensitive log data. Regular audits of logging configurations and log contents should be conducted to ensure no sensitive information is inadvertently recorded. Finally, organizations should educate developers and administrators about secure logging practices, emphasizing the risks of logging sensitive data and encouraging the use of secure authentication mechanisms such as short-lived tokens or multi-factor authentication to reduce the impact of potential credential leakage.