Focus on dify vulnerabilities and metrics.
Last updated: 16 Apr 2025, 22:25 UTC
This page consolidates all known Common Vulnerabilities and Exposures (CVEs) associated with dify. We track both calendar-based metrics (using fixed periods) and rolling metrics (using gliding windows) to give you a comprehensive view of security trends and risk evolution. Use these insights to assess risk and plan your patching strategy.
For a broader perspective on cybersecurity threats, explore the comprehensive list of CVEs by vendor and product. Stay updated on critical vulnerabilities affecting major software and hardware providers.
Total dify CVEs: 2
Earliest CVE date: 20 Mar 2025, 10:15 UTC
Latest CVE date: 20 Mar 2025, 10:15 UTC
Latest CVE reference: CVE-2025-0185
30-day Count (Rolling): 2
365-day Count (Rolling): 2
Calendar-based Variation
Calendar-based Variation compares a fixed calendar period (e.g., this month versus the same month last year), while Rolling Growth Rate uses a continuous window (e.g., last 30 days versus the previous 30 days) to capture trends independent of calendar boundaries.
Month Variation (Calendar): 0%
Year Variation (Calendar): 0%
Month Growth Rate (30-day Rolling): 0.0%
Year Growth Rate (365-day Rolling): 0.0%
Average CVSS: 0.0
Max CVSS: 0
Critical CVEs (≥9): 0
| Range | Count |
|---|---|
| 0.0-3.9 | 2 |
| 4.0-6.9 | 0 |
| 7.0-8.9 | 0 |
| 9.0-10.0 | 0 |
These are the five CVEs with the highest CVSS scores for dify, sorted by severity first and recency.
A vulnerability in the Dify Tools' Vanna module of the langgenius/dify repository allows for a Pandas Query Injection in the latest version. The vulnerability occurs in the function `vn.get_training_plan_generic(df_information_schema)`, which does not properly sanitize user inputs before executing queries using the Pandas library. This can potentially lead to Remote Code Execution (RCE) if exploited.
langgenius/dify version 0.9.1 contains a Server-Side Request Forgery (SSRF) vulnerability. The vulnerability exists due to improper handling of the api_endpoint parameter, allowing an attacker to make direct requests to internal network services. This can lead to unauthorized access to internal servers and potentially expose sensitive information, including access to the AWS metadata endpoint.