CVE-2023-0785
Mayurik Best Online News Portal 1.0
Raw vector
CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:N/A:NSummary
CVE-2023-0785 is a low-severity Exposure of Sensitive Information Through Data Queries (CWE-202) vulnerability in Mayurik Best Online News Portal. Its CVSS base score is 3.7 (Low).
Operationally, exploitation aligns with the MITRE ATT&CK technique Data from Information Repositories (T1213); ranked in the top 45% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2023-12799
Vulnerability Data
A vulnerability classified as problematic was found in SourceCodester Best Online News Portal 1.0. Affected by this vulnerability is an unknown functionality of the file check_availability.php. The manipulation of the argument username leads to exposure of sensitive information through data…
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queries. The attack can be launched remotely. The complexity of an attack is rather high. The exploitation appears to be difficult. The exploit has been disclosed to the public and may be used. The identifier VDB-220645 was assigned to this vulnerability.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Mitigating Controls (NIST CSF 2.0) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→CSF cross-walk (authority under review) — links open the control.
Least-privilege query permissions directly limit the data an attacker can request or infer.
Behavior analytics on query activity can detect inference attempts but does not prevent exposure at query time.
Protecting data-in-use reduces what remains available for inference via queries.
Mitigating Controls (ISO/IEC 27001:2022 Annex A) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→ISO cross-walk (authority under review) — links open the control.
Access control limits who can run queries that could expose sensitive information via inference.
Granular access rights reduce the ability of users to craft inference queries.
Data masking prevents inference by obscuring sensitive values returned in query results.
Information access restriction directly limits query scope that could lead to inference.
Classification helps identify sensitive data that must be protected from inference attacks.
DLP can detect and block queries or result sets that risk exposing sensitive information.