CVE-2025-10433
Published: 15 September 2025
Summary
CVE-2025-10433 is a low-severity Improper Input Validation (CWE-20) vulnerability in Notion (inferred from references). Its CVSS base score is 2.1 (Low).
Operationally, ranked at the 29.6th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-29159
Vulnerability details
A vulnerability was determined in 1Panel-dev MaxKB up to 2.0.2/2.1.0. This issue affects some unknown processing of the file /admin/api/workspace/default/tool/debug. Executing manipulation of the argument code can lead to deserialization. The attack can be executed remotely. The exploit has been…
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publicly disclosed and may be utilized. Upgrading to version 2.1.1 is capable of addressing this issue. It is suggested to upgrade the affected component.
- CWE(s)
Related Threats
No named actor attribution yet. ATT&CK technique mapping in progress for this CVE.
Affected Assets
Mitigating Controls
Likely Mitigating Controls AI
Per-CVE control mapping for this CVE has not run yet; the list below is derived from the weakness types (CWEs) cited in the NVD entry.
Security testing and evaluation at multiple SDLC stages directly detects missing or flawed input validation, with the required remediation process ensuring fixes are applied.
Directly implements checks on information inputs to reject invalid data before processing.
Penetration testing supplies malicious serialized objects, detecting unsafe deserialization and supporting corrective actions.
Security testing and developer training directly verify and enforce proper input validation, reducing exploitability of injection and malformed-data weaknesses.
Untrusted serialized data can be deserialized and observed inside the chamber, blocking gadget-chain exploitation outside the sandbox.
Identifies and blocks malicious code introduced through deserialization of untrusted data at system boundaries.
Integrity verification of serialized information can detect tampering before deserialization occurs.
Spam protection mechanisms perform filtering and detection on inbound/outbound messages, directly compensating for missing or weak input validation of unsolicited content.