Artificial Intelligence (AI) and Large Language Models (LLMs) are increasingly integrated into high-stakes applications, yet their susceptibility to adversarial prompts poses significant security risks. In this work, we introduce Mātṛkā, a framework for systematically evaluating jailbreak vulnerabilities in open-source multilingual LLMs. Using the open-source dataset across nine sensitive categories, we constructed adversarial prompt sets that combine translation, mixed-language encoding, homoglyph signatures, numeric enforcement, and structural variations. Experiments were conducted on state-of-the-art open-source models from Llama, Qwen, GPT-OSS, Mistral, and Gemma families. Our findings highlight transferability of jailbreaks across multiple languages with varying success rates depending on attack design. We provide empirical insights, a novel taxonomy of multilingual jailbreak strategies, and recommendations for enhancing robustness in safety-critical environments.