CVE-2018-20710 in yaml-cpp
Summary
by MITRE
The SingleDocParser::HandleFlowSequence function in yaml-cpp (aka LibYaml-C++) 0.6.2 allows remote attackers to cause a denial of service (stack consumption and application crash) via a crafted YAML file.
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Analysis
by VulDB Data Team • 07/29/2023
The vulnerability identified as CVE-2018-20710 resides within the yaml-cpp library version 0.6.2, specifically within the SingleDocParser::HandleFlowSequence function. This flaw represents a classic stack-based buffer overflow vulnerability that can be exploited through maliciously crafted YAML input files. The yaml-cpp library serves as a popular C++ parser for YAML formatted data, widely used in applications requiring configuration management, data serialization, and inter-application communication. When processing flow sequences in YAML documents, the parser fails to properly validate input structure, creating a condition where recursive parsing operations can consume excessive stack memory. The vulnerability manifests when an attacker crafts a YAML file containing deeply nested flow sequences that trigger recursive parsing behavior, leading to uncontrolled stack consumption. This issue falls under the Common Weakness Enumeration category CWE-129, which encompasses weaknesses related to improper validation of input boundaries, and more specifically maps to CWE-772, concerning missing release of memory following improper use of a resource. The operational impact of this vulnerability extends beyond simple denial of service, as it can be leveraged in broader attack scenarios including application crash exploitation and potential privilege escalation in environments where yaml-cpp is used for processing untrusted input. The attack vector requires remote exploitation through the delivery of malicious YAML content, making it particularly dangerous in web applications, configuration management systems, and any software that accepts YAML input from external sources. The stack consumption behavior directly aligns with ATT&CK technique T1499.004, which involves network denial of service attacks, specifically targeting resource exhaustion through memory consumption. Applications utilizing yaml-cpp for parsing user-provided content, configuration files, or data exchange protocols become vulnerable to this attack, as the parser does not implement adequate stack depth limits or input validation. The vulnerability's severity is compounded by the fact that yaml-cpp is widely integrated into numerous software ecosystems including game engines, web frameworks, and enterprise applications. When exploited, the vulnerability causes the target application to crash or become unresponsive due to stack overflow conditions, effectively rendering the service unavailable to legitimate users. The memory consumption pattern suggests that the parser recursively processes nested structures without sufficient safeguards against excessive recursion depth, creating a predictable exploitation scenario. The affected version 0.6.2 represents a critical security gap in the library's input handling mechanisms, where proper bounds checking and recursion limiting measures are absent. Organizations using yaml-cpp in production environments must implement immediate mitigations including library version updates, input validation layers, and runtime protection mechanisms. The recommended remediation strategy involves upgrading to yaml-cpp version 0.6.3 or later, which includes proper stack depth limiting and input validation fixes. Additionally, implementing application-level input sanitization, setting maximum recursion depth limits, and employing stack protection mechanisms such as stack canaries can provide defense-in-depth against similar vulnerabilities. The vulnerability demonstrates the critical importance of proper input validation in parsing libraries and highlights the need for robust resource management in recursive parsing algorithms. Security architects should consider implementing automated testing for parsing libraries, including fuzz testing scenarios that specifically target recursive input structures to identify similar vulnerabilities before they can be exploited in production environments.