CVE-2017-2810 in Tablib
Summary
by MITRE
An exploitable vulnerability exists in the Databook loading functionality of Tablib 0.11.4. A yaml loaded Databook can execute arbitrary python commands resulting in command execution. An attacker can insert python into loaded yaml to trigger this vulnerability.
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Analysis
by VulDB Data Team • 12/08/2022
The vulnerability identified as CVE-2017-2810 represents a critical security flaw in the Tablib library version 0.11.4, specifically within its Databook loading functionality. This issue stems from the library's improper handling of YAML data structures during the loading process, creating a path for arbitrary code execution. The vulnerability manifests when applications using Tablib process untrusted YAML input through the Databook module, allowing attackers to inject malicious Python code that gets executed during the parsing phase.
The technical root cause of this vulnerability lies in the unsafe deserialization of YAML content within the Tablib library. When the Databook module processes YAML files, it utilizes Python's pickle module or similar deserialization mechanisms that can execute arbitrary Python code during the parsing process. This behavior aligns with CWE-502, which describes unsafe deserialization vulnerabilities where untrusted data is deserialized without proper validation, allowing attackers to execute malicious code. The flaw occurs because the library does not sanitize or validate the YAML content before processing it, enabling attackers to craft malicious YAML payloads that contain Python object references or code execution directives.
The operational impact of CVE-2017-2810 is severe and far-reaching, as it allows remote attackers to achieve arbitrary code execution on systems running vulnerable applications. This vulnerability can be exploited through various attack vectors including web applications that accept user-uploaded YAML files, configuration management systems, or any application that processes external YAML data through Tablib's Databook functionality. The attack chain typically involves crafting a malicious YAML file containing Python code that gets executed when the Databook module processes the file, potentially leading to complete system compromise, data exfiltration, or lateral movement within network environments. This vulnerability directly maps to ATT&CK technique T1059.001 for Command and Scripting Interpreter, as it enables adversaries to execute arbitrary commands through the Python interpreter.
Mitigation strategies for this vulnerability require immediate action from affected organizations. The primary recommendation is to upgrade to a patched version of Tablib where the Databook loading functionality has been hardened against unsafe deserialization attacks. Organizations should also implement proper input validation and sanitization measures, particularly when processing external YAML files, and consider using safer serialization formats such as JSON instead of YAML where possible. Additionally, network segmentation and access controls should be implemented to limit the potential impact of successful exploitation, and security monitoring should be enhanced to detect suspicious YAML processing activities. The vulnerability demonstrates the critical importance of secure coding practices and proper input validation in preventing remote code execution vulnerabilities, particularly in libraries that handle data serialization and deserialization processes.