Cyber Resilience

CVE-2026-26936

Elastic Kibana 8.0.0 – 8.19.11

Published
26 February 2026
Modified
02 March 2026
Patch / advisory
CVSS Score v3.1 4.9
Click a component to see what it means
Raw vectorCVSS:3.1/AV:N/AC:L/PR:H/UI:N/S:U/C:N/I:N/A:H
EPSS Score 0.0032 25th percentile
Risk Priority 38 floored blend · peak EPSS

Summary

CVE-2026-26936 is a medium-severity Inefficient Regular Expression Complexity (CWE-1333) vulnerability in Elastic Kibana. Its CVSS base score is 4.9 (Medium).

Operationally, exploitation aligns with the MITRE ATT&CK technique Endpoint Denial of Service (T1499); ranked at the 25th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.

This vulnerability is AI-related — categorised as Other Platforms; in the Data-Related Vulnerabilities risk domain.

The strongest mitigations our analysis identified map to SA-11 (Developer Testing and Evaluation) and SA-15 (Development Process, Standards, and Tools) — see the control section below for these in your framework.

Deeper analysis AI-assisted summary

Synthesised by an AI model from the NVD description and linked references — a reading aid, not an authoritative source.

CVE-2026-26936 is an Inefficient Regular Expression Complexity vulnerability (CWE-1333) affecting the AI Inference Anonymization Engine in Kibana. Published on 2026-02-26, it enables a Denial of Service through Regular Expression Exponential Blowup (CAPEC-492), where crafted regex inputs trigger excessive computation. The issue carries a CVSS v3.1 base score of 4.9 (AV:N/AC:L/PR:H/UI:N/S:U/C:N/I:N/A:H), indicating moderate severity primarily due to high availability impact.

An attacker with high privileges (PR:H) can exploit this over the network (AV:N) with low complexity (AC:L) and no user interaction required. By submitting specially crafted input to the AI Inference Anonymization Engine, the attacker triggers exponential backtracking in regex processing, leading to high CPU consumption or service crashes that deny availability to legitimate users.

Elastic's security advisory ESA-2026-14, detailed at https://discuss.elastic.co/t/kibana-8-19-11-9-2-5-security-update-esa-2026-14/385250, addresses the vulnerability in affected Kibana versions including 8.19.1 and 9.2.5 through security updates that mitigate the regex complexity issue. Practitioners should apply these patches promptly to prevent exploitation.

EU & UK References

Vulnerability Data

Inefficient Regular Expression Complexity (CWE-1333) in the AI Inference Anonymization Engine in Kibana can lead Denial of Service via Regular Expression Exponential Blowup (CAPEC-492).

CWE(s)

AI Security AnalysisAI

AI Category
Other Platforms
Risk Domain
Data-Related Vulnerabilities
OWASP Top 10 for LLMs 2025
None mapped
Classification Reason
Matched keywords: ai

Related Threats

MITRE ATT&CK Enterprise Techniques

T1499 Endpoint Denial of Service Impact
Adversaries may perform Endpoint Denial of Service (DoS) attacks to degrade or block the availability of services to users.
T1499.003 Application Exhaustion Flood Impact
Adversaries may target resource intensive features of applications to cause a denial of service (DoS), denying availability to those applications.
Derived from this CVE’s CWE(s) via the direct CWE→ATT&CK cross-walk.

CVEs Like This One

CVE-2025-25014Same product: Elastic Kibana
CVE-2024-52974Same product: Elastic Kibana
CVE-2024-37281Same product: Elastic Kibana
CVE-2026-49094Same product: Elastic Kibana
CVE-2026-33464Same product: Elastic Kibana
CVE-2026-33459Same product: Elastic Kibana
CVE-2026-42400Same product: Elastic Kibana
CVE-2026-63260Same product: Elastic Kibana
CVE-2026-63261Same product: Elastic Kibana
CVE-2024-23443Same product: Elastic Kibana

Affected Assets

elastic
kibana
8.0.0 — 8.19.11 · 9.0.0 — 9.2.5

Mitigating Controls

Mitigating Controls (NIST 800-53 r5) AI

Developer testing and evaluation can discover inefficient regex patterns via performance or static analysis.

Development standards and tools can require safe regex construction and forbid known exponential patterns.

Denial-of-service protections limit resource exhaustion caused by expensive regex evaluation.

Input validation can constrain data that would otherwise trigger worst-case regex complexity.

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.

PR.PS-06 mostly match
prevents

Secure SDLC practices directly prevent inefficient regex via reviews, static analysis, and safe libraries.

ID.RA-01 partial match
prevents

Vulnerability identification processes can discover ReDoS issues in existing code but do not stop their introduction.

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.

finds

Security testing can detect and reject regex patterns with exponential worst-case complexity.

prevents

Secure development lifecycle mandates review of algorithmic efficiency, directly addressing ReDoS-prone regex.

prevents

Application security requirements can specify input-validation rules that limit regex complexity.

prevents

Secure architecture principles encourage avoidance of computationally expensive constructs such as catastrophic backtracking.

prevents

Secure coding standards explicitly prohibit or limit the use of inefficient regular expressions.

References