CVE-2025-1796 identifies a vulnerability in the langgenius/dify application version 0.10.1, where the password reset mechanism relies on Python's random.randint function to generate reset codes. This function is not designed for cryptographic security and produces pseudo-random numbers that can be predicted if an attacker can observe or extract the PRNG state or outputs. The vulnerability falls under CWE-338, which concerns the use of weak PRNGs in security-sensitive contexts. An attacker with access to the application's workflow tools can monitor or extract the PRNG outputs, allowing them to predict future password reset codes. This capability enables the attacker to reset passwords for any user account, including administrators, effectively taking full control of the application. The vulnerability impacts confidentiality, integrity, and availability by allowing unauthorized access, potential data theft, and disruption of services. The CVSS v3.0 score of 7.5 reflects a high severity, with network attack vector, high attack complexity, low privileges required, no user interaction, and full impact on confidentiality, integrity, and availability. No patches or known exploits are currently reported, but the weakness is fundamental and easily exploitable once the PRNG state is known or inferred. The root cause is the improper use of a non-cryptographically secure PRNG for generating security tokens, which should be replaced by a secure alternative such as Python's secrets module or os.urandom. The vulnerability highlights the critical importance of using cryptographically secure random number generators in authentication and authorization workflows.

For European organizations using langgenius/dify, this vulnerability poses a significant risk of unauthorized account takeover, including administrative accounts, leading to full compromise of the application environment. Confidential data could be exposed or altered, and service availability could be disrupted by attackers. Organizations in sectors with strict data protection requirements, such as finance, healthcare, and government, face heightened regulatory and reputational risks if exploited. The ability to predict password reset codes undermines trust in authentication mechanisms and can facilitate lateral movement within networks. Since the vulnerability requires some level of access to workflow tools, insider threats or compromised internal accounts increase the risk. The widespread adoption of langgenius/dify in European tech startups and enterprises could lead to cascading impacts if exploited at scale. The lack of known exploits currently provides a window for proactive mitigation, but the fundamental weakness means exploitation could become widespread once tools or exploits are developed.

1. Immediately replace the use of Python's random.randint for generating password reset codes with a cryptographically secure PRNG, such as the secrets module (e.g., secrets.randbelow) or os.urandom. 2. Restrict and monitor access to workflow tools that can expose PRNG outputs to minimize the risk of attackers obtaining the internal state. 3. Implement multi-factor authentication (MFA) for all accounts, especially administrators, to reduce the impact of compromised reset codes. 4. Conduct code audits and security reviews to identify and remediate other uses of weak PRNGs or insecure token generation. 5. Deploy anomaly detection to identify unusual password reset requests or patterns indicative of exploitation attempts. 6. Educate developers and security teams on cryptographic best practices to prevent recurrence. 7. If possible, rotate all password reset tokens and force password resets after patching the vulnerability. 8. Monitor threat intelligence sources for emerging exploits targeting this vulnerability and respond accordingly.