Decoding is the generation step that turns a model’s probability scores into actual text. After a language model predicts possible next tokens, the decoding method chooses which token to emit and how much randomness to allow. Common strategies include greedy decoding, beam search, top-k, top-p, and temperature-based sampling.
This matters in cyber security because decoding changes both output quality and risk. Low-randomness decoding can make security tools more deterministic, repeatable, and easier to audit, while higher-randomness decoding can produce more varied text. Attackers may use sampling to mass-produce different phishing messages, social-engineering scripts, or lure variations. Defenders may tune decoding to reduce hallucinations, test prompt robustness, or compare multiple samples to spot repetitive or unsafe model behavior.