CVE-2026-56329
Raw vector
CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:N/VI:L/VA:L/SC:N/SI:L/SA:L/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:XSummary
CVE-2026-56329 is a medium-severity Interpretation Conflict (CWE-436) vulnerability. Its CVSS base score is 5.3 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 15th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
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-2026-42885
Vulnerability Data
Capgo before 12.128.2 contains a cross-tenant preview namespace collision vulnerability caused by non-bijective decoding of double underscores to dots in preview hostname parsing. Attackers can register app IDs with underscores that collide with other tenants' dotted app IDs, causing preview…
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misrouting and denial of preview access for victim applications.
- 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.