CVE-2023-20215
Cisco Asyncos 11.7.0-406 … 14.5.1-016
Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:C/C:N/I:L/A:NSummary
CVE-2023-20215 is a medium-severity Exposure of Sensitive Information Through Data Queries (CWE-202) vulnerability in Cisco Asyncos. Its CVSS base score is 5.8 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Data from Information Repositories (T1213); ranked at the 47th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2023-24394
Vulnerability Data
A vulnerability in the scanning engines of Cisco AsyncOS Software for Cisco Secure Web Appliance could allow an unauthenticated, remote attacker to bypass a configured rule, allowing traffic onto a network that should have been blocked. This vulnerability is due…
more
to improper detection of malicious traffic when the traffic is encoded with a specific content format. An attacker could exploit this vulnerability by using an affected device to connect to a malicious server and receiving crafted HTTP responses. A successful exploit could allow the attacker to bypass an explicit block rule and receive traffic that should have been rejected by the device.
- 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.