CVE-2025-66303
Dec. 2, 2025, 5:16 p.m.
4.9
Medium
Description
Grav is a file-based Web platform. Prior to 1.8.0-beta.27, A Denial of Service (DoS) vulnerability has been identified in Grav related to the handling of scheduled_at parameters. Specifically, the application fails to properly sanitize input for cron expressions. By manipulating the scheduled_at parameter with a malicious input, such as a single quote, the application admin panel becomes non-functional, causing significant disruptions to administrative operations. The only way to recover from this issue is to manually access the host server and modify the backup.yaml file to correct the corrupted cron expression. This vulnerability is fixed in 1.8.0-beta.27.
Product(s) Impacted
| Vendor | Product | Versions |
|---|---|---|
| Grav |
|
|
Weaknesses
Common security weaknesses mapped to this vulnerability.
CWE-400
Uncontrolled Resource Consumption
The product does not properly control the allocation and maintenance of a limited resource, thereby enabling an actor to influence the amount of resources consumed, eventually leading to the exhaustion of available resources.
*CPE(s)
Affected systems and software identified for this CVE.
| Type | Vendor | Product | Version | Update | Edition | Language | Software Edition | Target Software | Target Hardware | Other Information |
|---|---|---|---|---|---|---|---|---|---|---|
| a | grav | grav | <1.8.0-beta.27 | / | / | / | / | / | / | / |
Tags
CVSS Score
CVSS Data - 3.1
- Attack Vector: NETWORK
- Attack Complexity: LOW
- Privileges Required: HIGH
- Scope: UNCHANGED
- Confidentiality Impact: NONE
- Integrity Impact: NONE
- Availability Impact: HIGH
CVSS:3.1/AV:N/AC:L/PR:H/UI:N/S:U/C:N/I:N/A:H
Timeline
Published: Dec. 1, 2025, 10:15 p.m.
Last Modified: Dec. 2, 2025, 5:16 p.m.
Last Modified: Dec. 2, 2025, 5:16 p.m.
Status : Undergoing Analysis
CVE has been recently published to the CVE List and has been received by the NVD.
More infoSource
security-advisories@github.com
*Disclaimer: Some vulnerabilities do not have an associated CPE. To enhance the data, we use AI to infer CPEs based on CVE details. This is an automated process and might not always be accurate.