Self-attention is a transformer mechanism that lets each token score its relationship to every other token in the same sequence. Those scores help the model decide which words, symbols, or code fragments matter most when building an internal representation. Unlike fixed window rules, self-attention can connect distant parts of an input, which is why it is useful for long prompts, logs, source code, and multi-step reasoning.
In cyber security, self-attention powers many language models used for malware analysis, alert triage, phishing detection, and code review. Its main security relevance is scale: dense self-attention becomes costly as inputs grow, so long attacker-controlled text can raise latency and memory use. Defenders respond with shorter context windows, sparse attention, input filtering, and retrieval systems that limit how much untrusted text reaches the model. Because the model may attend to injected instructions buried in a long sequence, controlling context is part of defending the model itself.