FKIE_CVE-2026-48290
Vulnerability from fkie_nvd - Published: 2026-07-14 22:17 - Updated: 2026-07-16 19:03
Severity
Summary
CAI Content Credentials is affected by a Server-Side Request Forgery (SSRF) vulnerability that could result in arbitrary code execution in the context of the current user. An attacker could exploit this vulnerability to inject malicious scripts into a web page, potentially gaining elevated access or control over the victim's account or session. Exploitation of this issue requires user interaction in that a victim must visit a maliciously crafted URL or interact with a compromised web page. Scope is changed.
References
{
"affected": [
{
"affectedData": [
{
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"product": "Content Credentials Rust SDK",
"vendor": "Adobe",
"versions": [
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"status": "affected",
"version": "0",
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},
{
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}
]
},
{
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"vendor": "Adobe",
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{
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"status": "affected",
"version": "0",
"versionType": "custom"
},
{
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"version": "c2patool-v0.26.65",
"versionType": "custom"
}
]
},
{
"defaultStatus": "affected",
"product": "Content Credentials JS SDK",
"vendor": "Adobe",
"versions": [
{
"lessThanOrEqual": "@contentauth/c2pa-web@0.7.0",
"status": "affected",
"version": "0",
"versionType": "custom"
},
{
"status": "unaffected",
"version": "@contentauth/c2pa-web@0.9.0",
"versionType": "custom"
}
]
}
],
"source": "psirt@adobe.com"
}
],
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"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "CAI Content Credentials is affected by a Server-Side Request Forgery (SSRF) vulnerability that could result in arbitrary code execution in the context of the current user. An attacker could exploit this vulnerability to inject malicious scripts into a web page, potentially gaining elevated access or control over the victim\u0027s account or session. Exploitation of this issue requires user interaction in that a victim must visit a maliciously crafted URL or interact with a compromised web page. Scope is changed."
}
],
"id": "CVE-2026-48290",
"lastModified": "2026-07-16T19:03:27.127",
"metrics": {
"cvssMetricV31": [
{
"cvssData": {
"attackComplexity": "LOW",
"attackVector": "LOCAL",
"availabilityImpact": "NONE",
"baseScore": 8.2,
"baseSeverity": "HIGH",
"confidentialityImpact": "HIGH",
"integrityImpact": "HIGH",
"privilegesRequired": "NONE",
"scope": "CHANGED",
"userInteraction": "REQUIRED",
"vectorString": "CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:C/C:H/I:H/A:N",
"version": "3.1"
},
"exploitabilityScore": 1.8,
"impactScore": 5.8,
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"type": "Secondary"
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"options": [
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{
"automatable": "no"
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{
"technicalImpact": "total"
}
],
"role": "CISA Coordinator",
"timestamp": "2026-07-14T23:32:21.640378Z",
"version": "2.0.3"
}
}
]
},
"published": "2026-07-14T22:17:00.840",
"references": [
{
"source": "psirt@adobe.com",
"tags": [
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],
"url": "https://helpx.adobe.com/security/products/content-authenticity-sdk/apsb26-80.html"
}
],
"sourceIdentifier": "psirt@adobe.com",
"vulnStatus": "Analyzed",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-918"
}
],
"source": "psirt@adobe.com",
"type": "Secondary"
}
]
}
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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.
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