CVE-2024-20293
Cisco Adaptive Security Appliance Software 9.19.1 – 9.19.1.24
Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:C/C:N/I:L/A:NSummary
CVE-2024-20293 is a medium-severity Interpretation Conflict (CWE-436) vulnerability in Cisco Adaptive Security Appliance Software. Its CVSS base score is 5.8 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 32th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
The strongest mitigations our analysis identified map to SA-11 (Developer Testing and Evaluation) and SI-10 (Information Input Validation) — see the control section below for these in your framework.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2024-18008
Vulnerability Data
A vulnerability in the activation of an access control list (ACL) on Cisco Adaptive Security Appliance (ASA) Software and Cisco Firepower Threat Defense (FTD) Software could allow an unauthenticated, remote attacker to bypass the protection that is offered by a…
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configured ACL on an affected device. This vulnerability is due to a logic error that occurs when an ACL changes from inactive to active in the running configuration of an affected device. An attacker could exploit this vulnerability by sending traffic through the affected device that should be denied by the configured ACL. The reverse condition is also true—traffic that should be permitted could be denied by the configured ACL. A successful exploit could allow the attacker to bypass configured ACL protections on the affected device, allowing the attacker to access trusted networks that the device might be protecting. Note: This vulnerability applies to both IPv4 and IPv6 traffic as well as dual-stack ACL configurations in which both IPv4 and IPv6 ACLs are configured on an interface.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Mitigating Controls (NIST 800-53 r5) AI
Developer testing and evaluation can discover cases where two products interpret the same inputs or state transitions differently.
Strict, consistently applied input validation reduces the chance that one product will accept data the other product rejects or interprets differently.
Applying security engineering principles during design can require unambiguous protocol and data-format specifications that eliminate divergent interpretations between products.
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.
Secure SDLC practices directly reduce the chance of introducing parser or state-machine inconsistencies.
Correlating logs from multiple products can surface discrepancies caused by interpretation conflicts.
Runtime monitoring of software behavior can detect adverse outcomes stemming from differing interpretations.
Supplier risk assessments can identify products whose differing interpretations create systemic exposure.
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.
Security testing can detect and correct cases where one component misinterprets another’s state or messages.
Secure development lifecycle can require consistent interface contracts and canonicalization rules that reduce interpretation conflicts between components.
Explicit application security requirements can mandate unambiguous protocol and data-format specifications that prevent divergent interpretations.
Secure architecture principles include well-defined component boundaries and shared data models that limit conflicting state perceptions.
Secure coding standards can enforce canonical input handling and strict protocol compliance to avoid misinterpretation between products.