KV caching significantly improves the efficiency of Large Language Model (LLM) inference by storing attention states from previously processed tokens, enabling faster generation of subsequent tokens. However, as sequence length increases, the KV cache quickly becomes a major memory bottleneck. To address this, we propose PagedEviction, a novel fine-grained, structured KV cache pruning strategy that enhances the memory efficiency of vLLM’s PagedAttention. Unlike existing approaches that rely on attention-based token importance or evict tokens across different vLLM pages, PagedEviction introduces an efficient block-wise eviction algorithm tailored for paged memory layouts. Our method integrates seamlessly with PagedAttention without requiring any modifications to its CUDA attention kernels. We evaluate PagedEviction across Llama-3.1-8B-Instruct, Llama-3.2-1B-Instruct, and Llama-3.2-3B-Instruct models on the LongBench benchmark suite, demonstrating improved memory usage with better accuracy than baselines on long context tasks.

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.