CVE-2026-44645 (GCVE-0-2026-44645)
Vulnerability from cvelistv5 – Published: 2026-06-17 22:08 – Updated: 2026-06-18 19:05
VLAI
EPSS
VEX
Title
LiquidJS has a renderLimit DoS guard bypass via empty `{% for %}` body
Summary
LiquidJS is a Shopify/GitHub Pages compatible template engine written in pure JavaScript. In versions 10.25.7 and below, the renderLimit option can be fully bypassed by a {% for %} (or {% tablerow %}) tag whose body is empty. The renderLimit option is documented in docs/source/tutorials/dos.md as the mechanism that "mitigates this by limiting the time consumed by each render() call." The per-iteration time check is reached only when the body contains at least one template node, so a template such as {%- for i in (1..N) -%}{%- endfor -%} iterates the full collection without ever consulting renderLimit. With a configured renderLimit of 50 ms, a single parseAndRenderSync call has been observed to consume 2.26 seconds (~45× over the limit) and scales linearly with N up to memoryLimit, allowing a low-privileged template author to wedge an event-loop thread for an attacker-chosen duration. Deployments that rely on a finite renderLimit for DoS protection (common in multi-tenant template-authoring environments) can still be forced by a single crafted template to monopolize a Node.js event-loop worker for attacker-controlled time, potentially stalling in-flight requests, with availability impact only. This issue has been fixed in version 10.26.0.
Severity
6.5 (Medium)
SSVC
Exploitation: poc
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-06-18 19:05 UTC
CWE
- CWE-400 - Uncontrolled Resource Consumption
Assigner
References
3 references
| URL | Tags |
|---|---|
| https://github.com/harttle/liquidjs/security/advi… | x_refsource_CONFIRM |
| https://github.com/harttle/liquidjs/commit/5b9c34… | 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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