Cross-Tenant Image Metadata Manipulation via Insufficient Validation

Capgo before 12.244.1 contains a cross-tenant integrity vulnerability in the metadata-cleaning worker that trusts image object keys from mutable database rows without validating ownership. An authenticated attacker can place a victim tenant's image key in a row they control, causing the service-role worker to download and re-upload that object with sanitized metadata. Attackers can silently modify metadata in cross-tenant image objects by supplying known victim keys during authorized row updates, bypassing storage access controls through the confused-deputy metadata worker.

Casky was already ahead

This CVE exploits attack patterns that Casky's 283matched skills already investigate — long before this vulnerability was disclosed. Claude's reasoning model maps these techniques to MITRE ATT&CK, so practitioners who ran these skills have already seen the threat behaviour in their findings.

Analysis

CVE-2026-100614 exposes a critical cross-tenant integrity flaw in Capgo's metadata-cleaning worker where insufficient input validation allows authenticated attackers to manipulate image metadata across tenant boundaries. The vulnerability stems from trusting image object keys stored in mutable database rows without verifying ownership or authorization. An attacker with legitimate access can inject a victim tenant's image key into a row under their control, triggering the service-role worker to process that foreign object—effectively gaining unauthorized modification capability over other organizations' data. This affects any multi-tenant SaaS deployment using Capgo and represents a privilege escalation attack that silently corrupts critical image metadata, potentially impacting data integrity, compliance, and user trust across affected organizations.

Casky's 283 matched skills map to MITRE ATT&CK techniques TA0004 (Privilege Escalation) and TA0006 (Credential Access), enabling practitioners to detect this attack pattern through multiple behavioral indicators. Using Claude's extended reasoning, Casky identifies lateral movement signals such as: database row modifications originating from one tenant containing object identifiers from another tenant; service-role workers processing resources outside their authorization scope; metadata transformation events on objects the requesting user doesn't own; and timing correlations between row updates and unexpected object re-uploads. Practitioners reviewing Casky findings would observe anomalous access patterns where authenticated users trigger privileged operations on cross-tenant resources, database queries revealing object key injection attempts, and audit logs showing metadata changes initiated through indirect object references rather than direct user actions—key indicators of this escalation-through-validation-bypass technique.

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Casky Risk Score

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EPSS Probability

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Shodan Exposure

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GreyNoise Signal

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Composite risk scoring from EPSS, CISA KEV, Shodan, and GreyNoise — 21 security APIs correlated into a single Casky Risk Score. Coming in Casky Pro. Join early access →

283 Casky skills cover this attack pattern

These skills use Claude AI's reasoning model to surface findings in the same attack categories as CVE-2026-100614.

abusing-dpapi-for-credential-access

red teaming · high

Run

abusing-shadow-credentials-for-privesc

red teaming · high

Run

Access with Stolen Session Cookie

identity access management · low

Run

MITRE ATT&CK Techniques

TA0004 TA0006

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