CVE-2026-48157 (GCVE-0-2026-48157)
Vulnerability from cvelistv5 – Published: 2026-06-15 20:59 – Updated: 2026-06-16 12:21
VLAI
EPSS
VEX
Title
Slim has Reflected XSS in the HtmlErrorRenderer
Summary
Slim is a PHP micro framework that enables users to write simple web applications and APIs. In versions 4.4.0 through 4.15, if an application uses HttpException::setTitle() and/or setDescription() to include untrusted/request-derived data in the error title or description (e.g. "No products found matching '{$query}'."), an attacker could inject arbitrary HTML/JavaScript that executes in the victim's browser when they encounter an HTML error page generated by Slim. The vulnerability is present even with displayErrorDetails = false as the unescaped title and description are rendered on this error path. Built-in exceptions (HttpNotFoundException, HttpBadRequestException, etc.) ship plain-text defaults, so a vanilla Slim app with no user code is not exploitable. Only applications that feed untrusted data into setTitle() and/or setDescription() are affected. The issue has been fixed in 4.15.2. If developers are unable to immediately update their applications, they can work around this issue by avoiding passing untrusted/request-derived data into HttpException::setTitle() and setDescription() and using static, plain-text error copy instead.
They should also register a custom error renderer (an ErrorRendererInterface implementation, or a subclass of HtmlErrorRenderer that escapes the title and description) for the HTML media type.
Severity
6.1 (Medium)
SSVC
Exploitation: none
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-06-16 12:21 UTC
CWE
- CWE-79 - Improper Neutralization of Input During Web Page Generation ('Cross-site Scripting')
Assigner
References
2 references
| URL | Tags |
|---|---|
| https://github.com/slimphp/Slim/security/advisori… | x_refsource_CONFIRM |
| https://github.com/slimphp/Slim/releases/tag/4.15.2 | 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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