Worked on security hardening for the llvm/torch-mlir repository, focusing on backend reliability and safe data handling. Addressed a critical vulnerability by replacing insecure YAML loading with a safe loader in Python, ensuring that untrusted YAML inputs could not trigger arbitrary code execution. This change reduced the attack surface associated with YAML parsing and aligned the codebase with secure-by-default practices. Updated documentation and internal notes to clarify the motivation and usage of the new YAML handling approach. The work emphasized backend development and Python proficiency, delivering a targeted bug fix that improved production security without introducing new user-facing features.