A Lightweight Method for Intelligent Correction of Weak‐Password Alert Deviations in Security Operations and Maintenance

Authentication is one of the most widely used security mechanisms in information systems, and weak passwords remain a risk that requires attention in enterprise security operations. To identify weak passwords in a timely manner, enterprises commonly deploy security products with weak‐password detection capabilities to monitor account names, passwords, password hashes, and related information in network traffic and generate alerts. In practice, however, the password string reported in some weak‐password alerts does not exactly match the actual weak password, preventing security operators from directly verifying the alert by logging in with the account name and password string provided in the alert. We examined 1892 weak‐password alerts collected from a retail enterprise's production environment and found that 356 alerts, accounting for 18.82%, could not be directly verified. Further analysis showed that most of these cases involved one to three missing or incorrect characters between the alert password string and the actual weak password. Such deviations reduce the credibility of weak‐password alerts and increase the cost of subsequent handling and communication with users. To address this problem, this paper proposes a lightweight correction method for weak‐password alert deviations in security operations. The method targets weak‐password alerts that cannot be directly verified and, with user authorization, uses existing enterprise user registration information and a public weak‐password dictionary to correct the alert password string. Specifically, the first stage generates candidate passwords based on the structural similarity between user registration information and chunks of the alert password string, and then applies recursive combination and Markov‐based character transformation prediction to complete the correction. Samples not corrected in the first stage are processed in a second stage, where similar passwords are filtered, ranked, and verified using the weak‐password dictionary. Through this two‐stage candidate‐generation process and controlled online verification, the method can identify the actual weak password from a relatively small candidate set. We evaluated the proposed method on real‐world enterprise datasets and compared it with Rainbow Table Lookup, Dictionary Traversal, Personal‐PCFG Validation, and PassGPT Validation. The results show that the method performs well overall in terms of correction success rate, candidate‐set size, number of verification attempts, and verification time. Compared with exhaustive traversal of a large weak‐password dictionary or general‐purpose password‐generation models, the method is better suited to targeted correction when the account and alert password string are known, and provides a lightweight aid for verifying and handling weak‐password alerts in enterprise environments.