Traditional password attacks followed predictable patterns. Attackers used dictionary wordlists, then applied rule-based mutations (e.g., swapping "a" for "@", adding "123" to the end), and hoped for matches. It was a resource-intensive and relatively slow process. However, AI-powered attacks are different: Pattern recognition at scale: Machine learning models identify subtle patterns in how people construct passwords, including common substitutions, keyboard patterns, and how they integrate personal information, generating guesses that mirror these behaviors. Instead of testing millions of random combinations, AI focuses on a hacker’s computational power on the most probable candidates. Intelligent credential mutation: When attackers obtain breached credentials from third-party services, generative AI can quickly test variations specific to your environment. For example, if "Summer2024!" worked on a personal account, the model can intelligently test "Winter2025!", "Spring2025!", and other likely variations rather than random permutations. Automated reconnaissance: Large language models can analyze publicly available information about your organization, for example, press releases, LinkedIn profiles, and product names, and incorporate that context into targeted phishing campaigns and password spray attacks. What used to take human analysts hours can now happen much more quickly. Lower barrier to entry: Pre-trained models and cloud computing infrastructure mean attackers no longer require deep technical expertise or expensive hardware.