Daptin GraphQL Endpoint SQL Injection via Query Parameters

Daptin is a GraphQL/JSON-API headless CMS. Prior to version 0.11.4, the /aggregate/:typename endpoint accepted column and group query parameters that were passed verbatim to goqu.L() — a raw SQL literal expression builder — without any validation. This bypassed all parameterization and allowed authenticated users with any valid session to inject arbitrary SQL expressions. This issue has been patched in version 0.11.4.

Casky was already ahead

This CVE exploits attack patterns that Casky's 301matched 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

Daptin versions prior to 0.11.4 contain a critical SQL injection vulnerability in the /aggregate/:typename endpoint where column and group query parameters are passed directly to raw SQL literal builders without validation. This allows authenticated attackers to inject arbitrary SQL expressions, completely bypassing parameterization protections. The vulnerability affects any organization running vulnerable Daptin instances as a headless CMS, particularly those exposing GraphQL APIs to multiple users or federated access patterns. While authentication is required, the low barrier to exploitation combined with the high severity of SQL injection makes this a significant risk for data breach, privilege escalation, and potential lateral movement within backend databases.

Casky's 301 mapped skills leverage Claude's extended reasoning to detect the attack patterns underlying this vulnerability across two critical MITRE ATT&CK techniques: TA0001 (Initial Access) and TA0043 (Reconnaissance). Practitioners using Casky would identify suspicious activity through detection of anomalous GraphQL query parameter structures, specifically requests where column and group parameters contain SQL keywords, operators, or subqueries rather than expected field names. The platform's skills would flag authentication logs combined with unusual API endpoint access patterns, correlate these with database query logs showing malformed or injected SQL literals, and surface indicators of data exfiltration attempts. Practitioners would see findings highlighting the progression from reconnaissance (parameter fuzzing, endpoint enumeration) through exploitation attempts, enabling rapid response before sensitive data reaches attackers.

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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 →

301 Casky skills cover this attack pattern

These skills use Claude AI's reasoning model to surface findings in the same attack categories as CVE-2026-41422.

analyzing-apt-group-with-mitre-navigator

threat intelligence · low

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analyzing-campaign-attribution-evidence

threat intelligence · low

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analyzing-certificate-transparency-for-phishing

threat intelligence · low

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MITRE ATT&CK Techniques

TA0001 TA0043

Investigate the attack patterns behind CVE-2026-41422

Casky has 301 skills that investigate the attack patterns behind CVE-2026-41422. Run one and get CVSS-scored findings in 3 minutes.

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