CVE-2026-28288 identifies an information disclosure vulnerability in the Dify platform, an open-source tool for developing applications using large language models (LLMs). Prior to version 1.9.0, the Dify API responses differ noticeably when queried with email addresses associated with existing accounts compared to those that do not exist. This discrepancy enables an unauthenticated remote attacker to perform user enumeration attacks by systematically testing email addresses and observing the API's differing responses. The vulnerability is categorized under CWE-204, which involves observable response discrepancies that leak information about system state or data. The CVSS 4.0 vector indicates the attack requires no privileges, no user interaction, and can be executed remotely over the network with low complexity. The impact is limited to confidentiality as it leaks information about registered users, potentially aiding social engineering or brute force attacks. The vendor addressed this issue in Dify version 1.9.0 by standardizing API responses to prevent enumeration. No public exploits have been reported yet, but the vulnerability poses a privacy risk and could be leveraged in multi-stage attacks targeting organizations using Dify for LLM app development.

The primary impact of this vulnerability is the unauthorized disclosure of registered user email addresses, compromising user privacy and potentially violating data protection regulations. Attackers can leverage this information to craft targeted phishing campaigns, credential stuffing, or social engineering attacks, increasing the risk of account compromise. For organizations, this can lead to reputational damage, loss of customer trust, and potential legal consequences under privacy laws such as GDPR or CCPA. While the vulnerability does not directly allow account takeover or system compromise, it serves as an enabler for more sophisticated attacks. Since Dify is an open-source platform used globally for LLM app development, organizations relying on affected versions may face increased reconnaissance activity. The lack of authentication or user interaction requirements makes exploitation straightforward, increasing the likelihood of automated enumeration attacks at scale.

To mitigate this vulnerability, organizations should upgrade all instances of Dify to version 1.9.0 or later, where the issue is resolved. In addition, implement rate limiting and anomaly detection on API endpoints to detect and block enumeration attempts. Employ generic error messages and uniform response codes for authentication and account-related API calls to prevent information leakage. Monitor logs for unusual patterns of email address queries and investigate suspicious activity promptly. Consider adding CAPTCHA or other challenge-response mechanisms on account-related endpoints to deter automated attacks. For organizations deploying Dify in production, conduct regular security assessments and code reviews focusing on information disclosure risks. Finally, educate users about phishing risks and encourage strong, unique passwords alongside multi-factor authentication to reduce the impact of potential follow-on attacks.