CVE-2018-20325 in Definitions Packageinfo

Summary

by MITRE

There is a vulnerability in load() method in definitions/parser.py in the Danijar Hafner definitions package for Python. It can execute arbitrary python commands resulting in command execution.

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Analysis

by VulDB Data Team • 06/20/2023

The vulnerability identified as CVE-2018-20325 resides within the Danijar Hafner definitions package for Python, specifically in the load() method located in definitions/parser.py. This flaw represents a critical security issue that allows attackers to execute arbitrary Python commands through improper input validation and handling. The vulnerability stems from insufficient sanitization of user-supplied data passed to the load() method, creating a path for malicious code injection that can be exploited by remote attackers. The affected package processes configuration or definition files that may contain serialized Python objects, and the insecure parsing mechanism fails to properly validate or restrict the types of objects that can be deserialized.

The technical implementation of this vulnerability aligns with CWE-502, which describes deserialization of untrusted data as a common pathway for arbitrary code execution. When the load() method processes input data, it does not adequately validate the source or content of the serialized objects, allowing attackers to craft malicious payloads that, when deserialized, execute arbitrary commands on the target system. This vulnerability operates at the intersection of software security and input validation, where the parsing logic fails to distinguish between legitimate and malicious serialized content. The attack surface is particularly concerning because it can be exploited through various means including file uploads, network communications, or any input that eventually reaches the vulnerable load() method.

The operational impact of this vulnerability extends beyond simple code execution, potentially enabling full system compromise when exploited in real-world scenarios. Attackers can leverage this vulnerability to gain unauthorized access to systems, escalate privileges, or deploy additional malicious payloads. The vulnerability affects environments where the Danijar Hafner definitions package is used to process untrusted input, making it particularly dangerous in web applications, automated processing systems, or any context where user-supplied data might be parsed through this method. The risk is amplified in cloud environments or containerized applications where such packages might be widely distributed and used across multiple services. Security teams must consider the potential for lateral movement and persistent access when assessing the impact of exploitation.

Mitigation strategies for CVE-2018-20325 should focus on immediate remediation through package updates and implementation of input validation controls. Organizations should prioritize updating to patched versions of the Danijar Hafner definitions package where available, as this represents the most direct solution to address the vulnerability. Additionally, implementing strict input validation and sanitization measures can provide defense-in-depth protection, ensuring that any data processed through the load() method is properly validated before deserialization occurs. The implementation of secure coding practices and regular security assessments of third-party libraries should be enforced to prevent similar vulnerabilities from emerging in other components of the software stack. Organizations should also consider implementing network segmentation and monitoring controls to detect potential exploitation attempts, as the vulnerability may be used as a stepping stone for more extensive attacks. The ATT&CK framework categorizes this type of vulnerability under privilege escalation and execution techniques, highlighting the need for comprehensive security controls across multiple defensive layers.

Reservation

12/21/2018

Disclosure

12/21/2018

Moderation

accepted

CPE

ready

EPSS

0.03530

KEV

no

Activities

very low

Sources

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