CVE-2026-55891 (GCVE-0-2026-55891)
Vulnerability from cvelistv5 – Published: 2026-08-28 20:23 – Updated: 2026-08-31 18:47
VLAI
EPSS
VEX
Title
PrivateBin: Reflected JSON injection in backend responses via unescaped REQUEST_URI
Summary
PrivateBin is an online pastebin where the server has zero knowledge of pasted data. Prior to 2.0.5, Request::getRequestUri() in lib/Request.php passes $_SERVER['REQUEST_URI'] through FILTER_SANITIZE_URL, which does not remove quotation marks, angle brackets, or apostrophes, and Controller::_init() stores the attacker-controlled value in Controller::$_urlBase. Controller::_jsonld() in lib/Controller.php then uses str_replace() to insert that value without JSON escaping into js/types.jsonld, js/paste.jsonld, and the other JSON-LD templates used by /?jsonld= and /?pasteid. A raw quotation mark delivered by an HTTP client, proxy, or structured-data crawler that does not normalize the request target can break out of the JSON string and inject arbitrary key-value data into a CORS-open application/ld+json response. The jsonld branch in Controller::__construct() returns before _setCacheHeaders(), so the response also lacks X-Content-Type-Options: nosniff, Content Security Policy, X-Frame-Options, and Referrer-Policy. Direct script execution was not demonstrated, but manipulated responses can affect structured-data consumers or combine with less strict clients. This issue is fixed in version 2.0.5.
Severity
SSVC
Exploitation: poc
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-08-31 18:47 UTC
CWE
- CWE-116 - Improper Encoding or Escaping of Output
Assigner
References
3 references
| URL | Tags |
|---|---|
| https://github.com/PrivateBin/PrivateBin/security… | x_refsource_CONFIRM |
| https://github.com/PrivateBin/PrivateBin/commit/1… | x_refsource_MISC |
| https://github.com/PrivateBin/PrivateBin/releases… | x_refsource_MISC |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| PrivateBin | PrivateBin |
Affected:
< 2.0.5
|
guessed |
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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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