Raw vector
CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:N/VI:N/VA:L/SC:N/SI:N/SA:N/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:AmberSummary
CVE-2026-42923 is a medium-severity Inefficient Algorithmic Complexity (CWE-407) vulnerability in Nlnetlabs Unbound. Its CVSS base score is 6.9 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Endpoint Denial of Service (T1499); ranked at the 27th 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 SC-5 (Denial-of-service Protection) and SC-6 (Resource Availability) — see the control section below for these in your framework.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-31081
Vulnerability Data
NLnet Labs Unbound up to and including version 1.25.0 has a vulnerability in the DNSSEC validator where the code path to consult the negative cache for DS records does not take into account the limit on NSEC3 hash calculations introduced…
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in 1.19.1. This leads to degradation of service during the attack. An adversary that controls a DNSSEC signed zone can exploit this by signing NSEC3 records with acceptably high iterations for child delegations and querying a vulnerable Unbound. Unbound will keep performing the allowed hash calculations on the NSEC3 records and will not limit the work by the mitigation introduced in 1.19.1. As a side effect, a global lock for the negative cache will be held for the duration of the hashing, blocking other threads that need to consult the negative cache. Coordinated attacks could raise the vulnerability to denial of service. Unbound 1.25.1 contains a patch with a fix to bound the vulnerable code path with the existing limit for NSEC3 hash calculations.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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V1.2.9
Mitigating Controls (NIST 800-53 r5) AI
Denial-of-service protection directly reduces the impact of resource exhaustion triggered by worst-case algorithmic inputs.
Resource availability allocation limits blast radius when an inefficient algorithm is forced into its worst case.
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 (code review, complexity analysis, safe algorithm selection) prevent introduction of exploitable worst-case behavior.
Runtime monitoring of software and resources can detect the performance impact of triggered worst-case complexity.
Identifying and recording algorithmic-complexity vulnerabilities directly addresses the root cause before exploitation.
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 uncover performance issues stemming from algorithmic complexity.
Redundancy of processing facilities can absorb resource exhaustion from inefficient algorithms.
Monitoring activities can identify anomalous resource consumption indicative of algorithmic complexity attacks.
Secure development life cycle includes design reviews that can catch inefficient algorithms before deployment.
Secure system architecture principles encourage selection of algorithms with acceptable worst-case complexity.
Secure coding practices can include guidelines to avoid or mitigate inefficient algorithms.