CVE-2026-45617 (GCVE-0-2026-45617)
Vulnerability from cvelistv5 – Published: 2026-06-17 22:14 – Updated: 2026-06-18 15:46
VLAI
EPSS
VEX
Title
LiquidJS: ReDoS via Quadratic Backtracking in `strip_html` Filter Regex
Summary
LiquidJS is a Shopify/GitHub Pages compatible template engine written in pure JavaScript. In versions 10.25.7 and below, the built-in strip_html filter uses a regex containing four flawed lazy-quantified alternatives, leading to ReDoS via quadratic backtracking. When the input contains many <script, <style, or <!-- opener tokens without matching closers, the V8 regex engine performs O(N²) backtracking, blocking the Node.js event loop. A single ~350 KB request ('<script'.repeat(50000)) stalls the process for ~10 seconds; cost grows quadratically with input size. The default memoryLimit: Infinity does not bound regex CPU, and even when configured strip_html only charges str.length to the limit — the regex itself runs unbounded. A single unauthenticated request containing crafted untrusted input can cause severe event-loop blocking and CPU amplification that saturates Node.js workers while bypassing memoryLimit protections. This issue has been fixed in version 10.26.0.
Severity
7.5 (High)
SSVC
Exploitation: poc
Automatable: yes
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-06-18 15:46 UTC
CWE
- CWE-1333 - Inefficient Regular Expression Complexity
Assigner
References
3 references
| URL | Tags |
|---|---|
| https://github.com/harttle/liquidjs/security/advi… | x_refsource_CONFIRM |
| https://github.com/harttle/liquidjs/commit/3616a7… | x_refsource_MISC |
| https://github.com/harttle/liquidjs/releases/tag/… | x_refsource_MISC |
Impacted products
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Experimental. This forecast is provided for visualization only and may change without notice. Do not use it for operational decisions.
Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.
Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
- Seen: The vulnerability was mentioned, discussed, or observed by the user.
- Confirmed: The vulnerability has been validated from an analyst's perspective.
- Published Proof of Concept: A public proof of concept is available for this vulnerability.
- Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
- Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
- Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
- Not confirmed: The user expressed doubt about the validity of the vulnerability.
- Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.
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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the
vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base
model, served locally by ML-Gateway.
They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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Related by attack behaviour
Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.
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