CVE-2018-20027 in Pylearn2
Summary
by MITRE
The yaml_parse.load method in Pylearn2 allows code injection.
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Analysis
by VulDB Data Team • 06/19/2023
The yaml_parse.load method in Pylearn2 presents a critical code injection vulnerability that stems from improper handling of untrusted YAML input. This flaw allows attackers to execute arbitrary code within the context of the application running Pylearn2, potentially leading to complete system compromise. The vulnerability exists because the load method processes YAML data without adequate sanitization or validation, enabling malicious actors to craft YAML payloads that contain executable Python code. When Pylearn2 processes these crafted inputs, the yaml_parse.load method interprets the malicious content as legitimate code rather than data, creating an execution path that bypasses normal security boundaries. This type of vulnerability falls under the category of insecure deserialization as defined by CWE-502, where untrusted data is deserialized without proper validation, allowing attackers to inject malicious code into the application's execution environment. The flaw represents a significant risk to machine learning workflows that rely on Pylearn2 for model configuration and data processing, as attackers could exploit this vulnerability to gain unauthorized access to systems running these applications.
The technical implementation of this vulnerability exploits the inherent capabilities of the Python YAML parser, which can execute arbitrary Python objects during deserialization. When Pylearn2's yaml_parse.load method encounters specially crafted YAML content, it can interpret class constructors, object factories, and other Python-specific features embedded within the YAML structure. Attackers can leverage this by creating YAML files that contain Python object references or executable code snippets that get executed during the parsing process. The vulnerability is particularly dangerous because YAML parsers often support complex features like Python object instantiation, which can be abused to execute system commands, access sensitive files, or establish reverse shells. This attack vector aligns with ATT&CK technique T1059.006 for execution through Python, where adversaries use legitimate system tools to execute malicious code. The flaw essentially transforms a configuration file parsing operation into a potential code execution primitive, undermining the security assumptions of applications that trust their YAML configuration sources.
The operational impact of CVE-2018-20027 extends beyond simple code injection to encompass potential data breaches, system compromise, and disruption of machine learning workflows. Organizations using Pylearn2 for model training, deployment, or configuration management face significant risks when this vulnerability exists in their systems. An attacker who can influence YAML input to Pylearn2 could gain access to sensitive training data, model parameters, or system resources. The vulnerability also poses risks to CI/CD pipelines and automated machine learning processes where YAML configurations are processed without proper input validation. Systems that rely on Pylearn2 for research or production workloads become vulnerable to remote code execution attacks, potentially allowing attackers to escalate privileges, exfiltrate data, or establish persistent access. The vulnerability's impact is amplified in environments where Pylearn2 is used with untrusted or user-provided YAML content, such as web applications that accept configuration files from users or systems that download configuration files from external sources.
Mitigation strategies for CVE-2018-20027 focus on preventing the execution of untrusted YAML content through proper input validation and secure parsing practices. The primary recommendation involves replacing yaml_parse.load with yaml.safe_load or equivalent secure parsing methods that restrict the types of objects that can be deserialized. Organizations should implement strict input validation and sanitization for all YAML content processed by Pylearn2, ensuring that only trusted sources can provide configuration data. Security teams should also consider implementing network segmentation and access controls to limit the potential impact of successful exploitation attempts. Regular security updates and patches should be applied to Pylearn2 installations, with administrators monitoring for any signs of exploitation attempts. Additional protective measures include implementing runtime monitoring for suspicious code execution patterns, employing principle of least privilege for Pylearn2 processes, and establishing secure configuration management practices that prevent unauthorized modification of YAML files. The vulnerability highlights the importance of following secure coding practices and adhering to security standards such as those outlined in the OWASP Top Ten and NIST Cybersecurity Framework to prevent similar issues in machine learning and data processing applications.