Focus on theunwindai vulnerabilities and metrics.
Last updated: 16 Apr 2026, 22:25 UTC
This page consolidates all known Common Vulnerabilities and Exposures (CVEs) associated with theunwindai. 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 theunwindai CVEs: 1
Earliest CVE date: 30 Mar 2026, 18:16 UTC
Latest CVE date: 30 Mar 2026, 18:16 UTC
Latest CVE reference: CVE-2026-29872
30-day Count (Rolling): 1
365-day Count (Rolling): 1
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 | 1 |
| 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 theunwindai, sorted by severity first and recency.
A cross-session information disclosure vulnerability exists in the awesome-llm-apps project in commit e46690f99c3f08be80a9877fab52acacf7ab8251 (2026-01-19). The affected Streamlit-based GitHub MCP Agent stores user-supplied API tokens in process-wide environment variables using os.environ without proper session isolation. Because Streamlit serves multiple concurrent users from a single Python process, credentials provided by one user remain accessible to subsequent unauthenticated users. An attacker can exploit this issue to retrieve sensitive information such as GitHub Personal Access Tokens or LLM API keys, potentially leading to unauthorized access to private resources and financial abuse.