CVE-2023-30899
Published: 09 May 2023
Summary
CVE-2023-30899 is a critical-severity Deserialization of Untrusted Data (CWE-502) vulnerability in Siemens Siveillance Video. Its CVSS base score is 9.9 (Critical).
Operationally, ranked in the top 14.0% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2023-35239
Vulnerability details
A vulnerability has been identified in Siveillance Video 2020 R2 (All versions < V20.2 HotfixRev14), Siveillance Video 2020 R3 (All versions < V20.3 HotfixRev12), Siveillance Video 2021 R1 (All versions < V21.1 HotfixRev12), Siveillance Video 2021 R2 (All versions <…
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V21.2 HotfixRev8), Siveillance Video 2022 R1 (All versions < V22.1 HotfixRev7), Siveillance Video 2022 R2 (All versions < V22.2 HotfixRev5), Siveillance Video 2022 R3 (All versions < V22.3 HotfixRev2), Siveillance Video 2023 R1 (All versions < V23.1 HotfixRev1). The Management Server component of affected applications deserializes data without sufficient validations. This could allow an authenticated remote attacker to execute code on the affected system.
- 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.
Penetration testing supplies malicious serialized objects, detecting unsafe deserialization and supporting corrective actions.
Evaluation of untrusted data handling (deserialization testing) reveals unsafe processing, which the required remediation process addresses.
Untrusted serialized data can be deserialized and observed inside the chamber, blocking gadget-chain exploitation outside the sandbox.
Validates or rejects untrusted serialized data before deserialization occurs.
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.
Provenance of associated data allows detection of untrusted sources before deserialization or processing occurs.