The analyzed content describes a guest diary entry from the SANS Internet Storm Center detailing a research project by Draden Barwick, an intern in the SANS Bachelor’s Degree in Applied Cybersecurity program. The project involves analyzing passwords collected from DShield honeypots, which are systems designed to attract and log malicious activity such as brute force login attempts. The researcher developed a tool that queries the HaveIBeenPwned (HIBP) API to determine which passwords seen in these attack attempts have not appeared in known data breaches. The HIBP API uses a k-anonymity model by querying only the first five characters of the SHA1 hash of a password, returning suffixes and breach counts without exposing the full password hash, preserving privacy. Using jq, a command-line JSON processor, the researcher extracted unique passwords from honeypot logs and then used a Python script to query HIBP for each password’s breach status. The results identified approximately 7.4% of unique passwords (1,196 out of 16,210) that were not found in the HIBP database, indicating these passwords are either new or less common in breaches. Analysis of these unseen passwords revealed common mutation patterns attackers use to modify base passwords, such as adding years, special characters, or substituting letters with symbols (e.g., replacing 'a' with '@' or 's' with '$'). The research also identified that attackers target specific services like Elasticsearch, Oracle, PostgreSQL, and Ubuntu, with PostgreSQL-related passwords appearing roughly twice as often as Elasticsearch. The researcher automated the process with cron jobs to regularly parse logs and query HIBP, facilitating ongoing monitoring. The project provides valuable insights into attacker password strategies and highlights the importance of understanding password mutation patterns to improve defensive measures. While no direct exploit or vulnerability is described, the findings inform better password security practices and threat intelligence gathering.

For European organizations, the impact of this research lies in its illumination of attacker password guessing tactics and the identification of passwords not previously seen in breaches. Attackers leveraging these insights may improve the effectiveness of brute force and credential stuffing attacks by using mutated passwords that evade traditional breach-based blacklists. This increases the risk of unauthorized access to systems, especially those relying on weak or commonly mutated passwords. The targeting of specific platforms such as PostgreSQL, Elasticsearch, Oracle, and Ubuntu services suggests that organizations using these technologies could face heightened attack attempts. Given the widespread use of these platforms across European enterprises, especially in sectors like finance, government, and technology, the risk of compromise due to password guessing is non-trivial. However, since this is not a direct exploit or vulnerability, the impact is indirect and depends on existing password hygiene and security controls. Organizations with poor password policies or lacking multi-factor authentication (MFA) are more vulnerable. The research also aids defenders by providing actionable intelligence on attacker behavior, enabling more targeted monitoring and response strategies.

1. Implement and enforce strong password policies that discourage predictable mutations and common patterns identified in the research, such as sequential numbers, year suffixes, and simple character substitutions. 2. Employ multi-factor authentication (MFA) across all critical systems to reduce the risk posed by compromised passwords. 3. Integrate threat intelligence feeds and tools that analyze login attempts for patterns matching known password mutation strategies to detect and block suspicious activity early. 4. Regularly audit and update password blacklists to include not only breached passwords but also commonly mutated variants as revealed by this research. 5. Harden and monitor services identified as frequent targets (PostgreSQL, Elasticsearch, Oracle, Ubuntu) with strict access controls, logging, and anomaly detection. 6. Use honeypots or deception technologies to gather ongoing intelligence on attacker password attempts and adapt defenses accordingly. 7. Educate users and administrators about the risks of password reuse and encourage the use of password managers to generate and store complex, unique passwords. 8. Automate log parsing and analysis workflows similar to the described project to maintain up-to-date awareness of attacker password trends. 9. Limit login attempt rates and implement account lockout policies to mitigate brute force attacks exploiting these password patterns. 10. Ensure timely patching and configuration hardening of targeted services to reduce overall attack surface.