Sparse attention is an attention pattern in a transformer that limits which tokens can interact with each other. Instead of comparing every token to every other token, the model uses local windows, selected global tokens, or other restricted connections. This cuts compute and memory use, especially on long inputs where dense self-attention becomes expensive.
In cyber security, sparse attention matters because long prompts, logs, code, and threat reports can be costly to process at scale. Efficient attention can make local or private deployment more practical for sensitive data, but poor sparsity design can also hide important context. Defenders use it to lower latency in security assistants and analysis pipelines; attackers may try to place malicious instructions outside the model’s limited view, making context design part of the security boundary.