A malicious actor who has been authenticated and granted specific permissions in Apache Superset may use the import dataset feature in order to conduct Server-Side Request Forgery attacks and query internal resources on behalf of the server where Superset is deployed. This vulnerability exists in Apache Superset versions up to and including 2.0.1.
The severity of this vulnerability is classified as medium, with a CVSS score of 4.9. It is essential for organizations using Apache Superset to understand the implications of this vulnerability, as it allows attackers to potentially access sensitive internal resources.
Risk to organizations includes unauthorized access to internal resources due to the Server-Side Request Forgery attack vector. Given the potential impact, organizations should prioritize patching immediately.
As of now, there are no public exploits confirmed for this vulnerability, and it is not listed as actively exploited in the KEV database.
Vulnerability Details
The vulnerability is classified under CWE-918, which pertains to Server-Side Request Forgery (SSRF). The CVSS score from NVD is 6.5, indicating a medium severity level, while Apache's secondary metric gives a slightly lower score of 4.9. This discrepancy highlights the variations in scoring methodologies.
Affected product includes Apache Superset up to version 2.0.1, published on April 17, 2023. Organizations using vulnerable versions should take immediate action.
Technical Analysis
The root cause of this vulnerability stems from improper validation of user input within the import dataset feature. This flaw allows authenticated users, with high privileges, to craft requests that the server processes, leading to unauthorized queries to internal resources.
The attack vector is network-based, requiring low complexity for exploitation. The attacker needs high privileges, but user interaction is not required. This vulnerability has a high confidentiality impact, with no integrity or availability impact.
Risk & Impact Analysis
Real-world deployment risk is significant as this vulnerability allows access to internal systems that may contain sensitive data. Organizations utilizing Apache Superset should assess the potential blast radius of such an attack, especially in environments where sensitive operations are performed.
The urgency assessment is moderate, given the CVSS score of 4.9 and the fact that this vulnerability is not listed as actively exploited in the KEV database. However, organizations should still prioritize remediation efforts in their patch cycle.
Exploitation Status
Signal | Status |
|---|---|
Known Exploit | No |
Public PoC | No |
Actively Exploited | No |
Ransomware Use | No |
Affected Versions
All versions prior to vendor patch (2.0.1) are affected by this vulnerability.
Mitigation & Remediation
Organizations should prioritize patching immediately by upgrading to the latest version of Apache Superset. If a patch is unavailable, consider implementing configuration hardening by limiting access to the import dataset feature based on user roles.
Monitoring should also be enhanced to detect any unauthorized access attempts, especially concerning internal resource queries.
Detection Guidance
Log indicators should include any unexpected requests made to internal resources from authenticated users. Behavioral anomalies might indicate an ongoing attack, especially if requests are made outside of normal usage patterns.
AppSecure Threat Intelligence Insight
This vulnerability underscores the importance of effective access controls and monitoring within applications like Apache Superset. Security teams should ensure that implementation of the principle of least privilege is enforced and regularly reviewed.
For further insights into enhancing security practices, organizations can explore our penetration testing methodology and consider implementing regular security assessments.
Furthermore, reviewing and adapting security policies in light of emerging threats is crucial for maintaining robust security posture.
Disclaimer: This content was generated using AI. While we strive for accuracy, please verify critical information with official sources.
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