CVE-2017-16615 in MLAlchemy
Summary
by MITRE
An exploitable vulnerability exists in the YAML parsing functionality in the parse_yaml_query method in parser.py in MLAlchemy before 0.2.2. When processing YAML-Based queries for data, a YAML parser can execute arbitrary Python commands resulting in command execution because load is used where safe_load should have been used. An attacker can insert Python into loaded YAML to trigger this vulnerability.
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Analysis
by VulDB Data Team • 01/06/2023
The vulnerability identified as CVE-2017-16615 represents a critical security flaw in the MLAlchemy library's YAML parsing implementation, specifically within the parse_yaml_query method located in parser.py. This issue affects versions prior to 0.2.2 and demonstrates a classic example of unsafe deserialization that can lead to remote code execution. The vulnerability arises from the improper use of Python's YAML parsing functions, where the insecure load() function is employed instead of the safer safe_load() alternative. This fundamental error in the codebase creates a pathway for malicious actors to inject arbitrary Python code into the YAML parsing process, effectively bypassing normal security boundaries and executing commands with the privileges of the affected application.
The technical exploitation of this vulnerability occurs through the manipulation of YAML input data that is processed by the affected MLAlchemy library. When the parse_yaml_query method processes user-supplied YAML content, the use of load() instead of safe_load() allows attackers to embed Python objects and code within the YAML structure. This enables the execution of arbitrary Python commands as part of the YAML parsing operation, since the load() function does not restrict the types of objects that can be created during deserialization. The flaw essentially transforms a legitimate data parsing operation into a potential command execution vector, where attackers can craft malicious YAML payloads that, when processed, result in arbitrary code execution on the target system. This vulnerability directly maps to CWE-502, which describes unsafe deserialization, and aligns with ATT&CK technique T1203, covering legitimate credentials and T1059, covering command and scripting interpreter.
The operational impact of CVE-2017-16615 extends beyond simple code execution to encompass potential system compromise and data breach scenarios. An attacker who can influence the YAML input processed by MLAlchemy applications can gain unauthorized access to the underlying system, potentially escalating privileges and accessing sensitive data. The vulnerability is particularly dangerous in environments where MLAlchemy is used for processing user-provided data, as it allows for remote code execution without requiring authentication or specific user interaction. Organizations using MLAlchemy in production environments may face significant security risks, including unauthorized access to databases, system file manipulation, and potential lateral movement within network infrastructures. The vulnerability's impact is amplified by the fact that YAML parsing is often used in configuration management and data processing workflows, making it a prime target for attackers seeking to establish persistent access or execute malicious payloads within legitimate application contexts.
Mitigation strategies for CVE-2017-16615 focus on immediate remediation through version updates and code-level fixes. The primary solution involves upgrading to MLAlchemy version 0.2.2 or later, which contains the necessary patches to address the unsafe YAML parsing implementation. Organizations should also implement input validation and sanitization measures to reduce the attack surface, ensuring that any YAML data processed by applications is properly validated before deserialization. The use of safe_load() instead of load() should be enforced throughout all YAML parsing operations, particularly in libraries and applications that process external data sources. Additionally, implementing proper access controls and privilege separation can help limit the potential damage from successful exploitation attempts. Security teams should also consider deploying monitoring solutions to detect unusual YAML processing patterns or command execution attempts that may indicate exploitation attempts. The vulnerability serves as a reminder of the critical importance of secure coding practices and the need for thorough security reviews of third-party libraries, particularly those handling untrusted data inputs.