YAML Parsing Vulnerabilities

Security risks that arise due to improper handling of YAML data, leading to code injection or application crashes.

Understanding YAML Parsing Vulnerabilities

YAML (Yet Another Markup Language) is widely used for configuration files and data serialization. However, insecure YAML parsing can lead to remote code execution (RCE), denial-of-service (DoS), and data manipulation attacks. Attackers exploit unsafe deserialization, type confusion, and excessive resource consumption in YAML parsers.

Common YAML Parsing Vulnerabilities

Insecure Deserialization

Some YAML parsers allow arbitrary object deserialization, which attackers exploit to execute malicious code.
Example: Python’s PyYAML and Ruby’s Psych have suffered from insecure loading functions (yaml.load() instead of yaml.safe_load()).

Arbitrary Code Execution

Attackers craft malicious YAML files to execute system commands.
Example: Passing a !python/object/apply:os.system directive can run system commands in Python-based applications.

Denial-of-Service (DoS) Attacks

Large YAML files with deeply nested structures cause excessive memory usage, leading to service crashes.
Example: The Billion Laughs attack, which uses recursive references to overwhelm YAML parsers.

Type Confusion Attacks

Some YAML parsers automatically convert data types, leading to unexpected behavior or security flaws.
Example: A string "123456" might be interpreted as an integer, affecting authentication logic.

Mitigation Strategies

Use Safe Parsers – Always use safe loading functions (safe_load() in Python, YAML.safe_load() in Java).
Restrict YAML Features – Disable aliases, anchors, and object instantiation when parsing YAML.
Limit File Size and Depth – Set restrictions on YAML input size and nesting depth to prevent DoS attacks.
Validate Input Data – Sanitize and validate YAML files before processing them.
Apply Least Privilege – Run YAML-parsing applications with minimum required permissions to reduce impact.