CVE-2021-2138 in Cloud Infrastructure Data Science Notebook Sessions
Summary
by MITRE • 03/03/2021
Vulnerability in the Oracle Cloud Infrastructure Data Science Notebook Sessions. Easily exploitable vulnerability allows low privileged attacker with access to the physical communication segment attached to the hardware where the Oracle Cloud Infrastructure Data Science Notebook Sessions executes to compromise Oracle Cloud Infrastructure Data Science Notebook Sessions. Successful attacks of this vulnerability can result in unauthorized update, insert or delete access to some of Oracle Cloud Infrastructure Data Science Notebook Sessions accessible data as well as unauthorized read access to a subset of Oracle Cloud Infrastructure Data Science Notebook Sessions accessible data. All affected customers were notified of CVE-2021-2138 by Oracle. CVSS 3.1 Base Score 4.6 (Confidentiality and Integrity impacts). CVSS Vector: (CVSS:3.1/AV:A/AC:L/PR:L/UI:N/S:U/C:L/I:L/A:N)
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Analysis
by VulDB Data Team • 03/14/2021
The vulnerability identified as CVE-2021-2138 affects Oracle Cloud Infrastructure Data Science Notebook Sessions, representing a significant security weakness within cloud-based machine learning environments. This flaw resides in the network communication layer of the data science platform, specifically targeting the physical communication segment that connects to the hardware executing these notebook sessions. The vulnerability's classification as easily exploitable indicates that attackers with minimal privileges and physical access to the network infrastructure can leverage this weakness to compromise the targeted system. The security implications extend beyond simple network access, as the vulnerability creates pathways for unauthorized modifications to data within the platform's accessible datasets.
The technical nature of this vulnerability stems from insufficient network segmentation and access controls within the Oracle Cloud Infrastructure environment. Attackers with access to the physical communication segment can potentially intercept, modify, or inject malicious data into the notebook session communications. This weakness operates at the network layer where the platform's data science sessions interact with underlying infrastructure, creating opportunities for data manipulation and unauthorized access. The vulnerability's impact is particularly concerning as it affects both confidentiality and integrity aspects of the system, allowing attackers to perform unauthorized update, insert, or delete operations on sensitive data while also enabling read access to data subsets that should remain protected.
From an operational standpoint, this vulnerability poses substantial risks to organizations utilizing Oracle Cloud Infrastructure Data Science services, particularly those handling sensitive analytical datasets and machine learning workloads. The attack vector requires physical access to the communication segment, which suggests that the threat model includes insider risks or attackers who have gained physical access to data center infrastructure. The CVSS score of 4.6 indicates a moderate severity level, yet the potential for unauthorized data modification and access makes this vulnerability particularly dangerous in environments where data integrity and confidentiality are paramount. Organizations relying on notebook sessions for critical data analysis, model training, or research activities face significant exposure through this vulnerability.
The security implications extend beyond immediate data compromise to encompass potential long-term impacts on data science workflows and research integrity. Attackers could manipulate training datasets, modify analytical outputs, or introduce malicious code into the notebook environments, potentially affecting the reliability of machine learning models and analytical conclusions. The vulnerability's impact on both confidentiality and integrity aligns with CWE-284, which addresses improper access control mechanisms, and reflects patterns commonly seen in network-level privilege escalation vulnerabilities. Organizations should consider this vulnerability within the context of broader cloud security frameworks and ensure proper network segmentation practices are implemented to prevent unauthorized access to critical communication channels. Mitigation strategies should include enhanced network monitoring, implementation of secure communication protocols, and regular security assessments of cloud infrastructure components to prevent exploitation of similar vulnerabilities in the future.
The CVSS vector AV:A/AC:L/PR:L/UI:N/S:U/C:L/I:L/A:N provides specific insights into the attack characteristics and impact scope. The Attack Vector of A indicates that physical access to the network segment is required, while the Low Attack Complexity suggests that the exploitation process does not require specialized tools or extensive technical knowledge. The Low Privilege Requirement reflects that attackers need only basic network access rather than elevated system privileges, making this vulnerability particularly concerning for environments where physical security controls may be insufficient. The Unchanged Scope indicates that the attack does not escalate beyond the targeted system, though the Confidentiality and Integrity impacts remain significant. This vulnerability demonstrates the importance of defense-in-depth strategies and highlights the critical need for organizations to implement robust network security controls even within cloud environments where traditional perimeter-based security models may not be sufficient.