CVE-2026-75531 (GCVE-0-2026-75531)
Vulnerability from cvelistv5 – Published: 2026-08-17 21:00 – Updated: 2026-08-18 15:20
VLAI
EPSS
VEX
Title
Stored Cross-Site Scripting in URL Observables via Lookyloo Submission Handler in Pandora
Summary
Pandora contains a stored cross-site scripting (XSS) vulnerability in the rendering of URL observables. A URL extracted from or associated with an analyzed file was inserted directly into the inline JavaScript onclick handler used by the Submit to Lookyloo action.
Although the value was subject to HTML escaping by the template engine, it was embedded inside a JavaScript string within an HTML attribute. An attacker-controlled URL containing specially crafted characters could therefore break out of the JavaScript string and inject arbitrary JavaScript code.
The malicious script would execute in the context of the Pandora web application when a victim interacts with the affected Submit to Lookyloo control. Successful exploitation could allow an attacker to access information available to the victim's browser or perform actions using the victim's authenticated Pandora session.
The patch removes the observable value from the inline JavaScript handler. The URL is instead stored in an HTML data-url attribute and retrieved through the DOM dataset API when needed. Additional uses of innerHTML were also replaced with textContent as defensive hardening.
Severity
SSVC
Exploitation: none
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-08-18 15:20 UTC
CWE
- CWE-79 - Improper Neutralization of Input During Web Page Generation (XSS or 'Cross-site Scripting')
Assigner
References
1 reference
| URL | Tags |
|---|---|
| https://github.com/pandora-analysis/pandora/commi… | patch |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| pandora-analysis | pandora |
Affected:
0 , ≤ 1.12.5
(semver)
|
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.
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.
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