CVE-2024-6507 in deeplake
Summary
by MITRE • 07/04/2024
Command injection when ingesting a remote Kaggle dataset due to a lack of input sanitization in the ingest_kaggle() API
Be aware that VulDB is the high quality source for vulnerability data.
Analysis
by VulDB Data Team • 07/06/2024
The vulnerability identified as CVE-2024-6507 represents a critical command injection flaw within the ingest_kaggle() API function that processes remote Kaggle dataset ingestion. This security weakness stems from inadequate input sanitization mechanisms that fail to properly validate or escape user-supplied parameters before incorporating them into system commands. The vulnerability exists in environments where the API accepts external dataset identifiers or configuration parameters from untrusted sources without sufficient security controls to prevent malicious input from being executed as part of command sequences. The flaw specifically impacts systems that utilize Kaggle dataset ingestion workflows where remote dataset references are processed through automated ingestion pipelines.
The technical implementation of this vulnerability allows an attacker to inject malicious commands into the dataset ingestion process by manipulating input parameters passed to the ingest_kaggle() function. When the API processes these parameters, it constructs system commands that include the unsanitized input without proper escaping or validation, creating opportunities for arbitrary code execution. The command injection occurs at the point where the API constructs shell commands or system calls to fetch and process remote datasets, making it possible for attackers to execute unintended operations on the underlying system. This type of vulnerability falls under CWE-77 and CWE-78 categories, representing command injection flaws that can lead to complete system compromise when exploited properly.
The operational impact of CVE-2024-6507 extends beyond simple data theft or service disruption, as successful exploitation can lead to full system compromise and persistent access within the affected environment. Attackers can leverage this vulnerability to execute arbitrary commands with the privileges of the user running the ingestion process, potentially gaining access to sensitive data, establishing backdoors, or using the compromised system as a launch point for further attacks. The vulnerability affects systems that rely on Kaggle dataset integration workflows, particularly those in data science platforms, machine learning environments, or enterprise analytics systems where automated dataset ingestion is common. This creates a significant risk for organizations that handle sensitive data or operate in regulated environments where data integrity and system security are paramount.
Mitigation strategies for CVE-2024-6507 should focus on implementing robust input validation and sanitization mechanisms within the ingest_kaggle() API function. Organizations must ensure that all user-supplied parameters are properly escaped and validated before being incorporated into system commands, utilizing parameterized command execution where possible. The implementation of principle of least privilege should be enforced to limit the system privileges of the ingestion process, reducing the potential impact of successful exploitation. Additionally, organizations should consider implementing input filtering mechanisms that reject suspicious characters or patterns commonly associated with command injection attacks, while also establishing monitoring and logging capabilities to detect anomalous command execution patterns. The use of secure coding practices and regular security testing of API endpoints can help prevent similar vulnerabilities from emerging in future implementations. This vulnerability demonstrates the importance of following secure coding guidelines and implementing defense-in-depth strategies to protect against command injection attacks that can compromise entire systems through seemingly innocuous API functions.