CVE-2017-16616 in PyAnyAPI
Summary
by MITRE
An exploitable vulnerability exists in the YAML parsing functionality in the YAMLParser method in Interfaces.py in PyAnyAPI before 0.6.1. 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-2017-16616 represents a critical security flaw in the PyAnyAPI framework that affects versions prior to 061. This issue resides within the YAML parsing functionality of the Interfaces.py file where the YAMLParser method improperly handles user-supplied input. The root cause stems from the improper use of the yaml.load() function instead of the safer yaml.safe_load() function, creating a pathway for arbitrary code execution through maliciously crafted YAML payloads.
This vulnerability falls under the category of CWE-94, which describes "Improper Control of Generation of Code" and specifically relates to "Code Injection" scenarios where untrusted data is interpreted as executable code. The flaw enables attackers to inject Python code directly into YAML documents that are then processed by the application, allowing for complete system compromise. When the YAML parser encounters malicious content, it executes the embedded Python commands with the privileges of the running application, potentially leading to full system takeover.
The operational impact of this vulnerability extends beyond simple code execution to encompass complete system compromise and data exfiltration capabilities. Attackers can leverage this vulnerability to execute arbitrary commands on the affected system, potentially gaining access to sensitive data, establishing persistence mechanisms, or using the compromised system as a pivot point for further attacks within the network. The vulnerability is particularly dangerous because YAML parsing is commonly used for configuration management, API request handling, and data exchange scenarios where user input is frequently processed.
The attack surface for this vulnerability is broad, as any application using PyAnyAPI versions before 061 that accept user-provided YAML data is at risk. This includes web applications, API endpoints, configuration management systems, and any service that processes YAML input from untrusted sources. The ATT&CK framework categorizes this as a code injection technique under the T1059.001 sub-technique, where adversaries leverage application vulnerabilities to execute malicious code. Organizations should immediately implement mitigations including updating to PyAnyAPI version 061 or later, implementing proper input validation, and restricting YAML processing to trusted sources only.
Mitigation strategies should focus on immediate remediation through version updates, but additional protective measures include implementing proper YAML parsing practices by replacing yaml.load() with yaml.safe_load() throughout the application codebase. Security teams should also consider implementing network-based detection measures and monitoring for suspicious YAML parsing activities. The vulnerability demonstrates the critical importance of following secure coding practices and the principle of least privilege when handling untrusted data, particularly in parsing functions that can execute arbitrary code. Organizations using similar frameworks or libraries should conduct comprehensive audits to identify and remediate similar patterns of insecure YAML processing throughout their codebases.