Authenticated SQL Injection in Fuel CMS Activity Logs

Fuel CMS 1.4.13 contains a blind SQL injection vulnerability that allows authenticated attackers to manipulate database queries by injecting SQL code through the 'col' parameter in the Activity Log interface. Attackers can send requests to the logs endpoint with malicious SQL payloads in the 'col' parameter to extract database information based on response time delays.

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

Fuel CMS versions up to 1.4.13 contain a blind SQL injection vulnerability in the Activity Log interface that allows authenticated attackers to manipulate database queries through the 'col' parameter. This vulnerability is critical because it enables attackers with valid credentials to extract sensitive database information without direct error messages—instead relying on response time delays to infer query results. Organizations running vulnerable Fuel CMS instances are at risk of data exfiltration, particularly if their database contains sensitive customer data, authentication credentials, or business intelligence. The authenticated requirement lowers the immediate risk surface but becomes dangerous in environments with weak access controls or compromised user accounts.

Casky's 301 mapped skills detect the attack patterns underlying this vulnerability by analyzing techniques from TA0001 (Initial Access/Reconnaissance) and TA0043 (Reconnaissance) through Claude's extended reasoning capabilities. Practitioners using Casky would identify suspicious activity patterns including: repeated requests to the logs endpoint with unusual parameter values, systematic variation of the 'col' parameter with time-delay SQL payloads (such as SLEEP() or BENCHMARK() functions), and response time anomalies that deviate significantly from baseline queries. The platform's SQL injection detection skills would flag database query patterns that contain conditional logic designed to extract one bit of information per request—the hallmark of blind SQL injection exploitation. Security teams would see findings correlating parameter manipulation with infrastructure-level timing analysis, enabling faster isolation of affected endpoints and user accounts before widespread data compromise occurs.

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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-2021-47980.

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-2021-47980

Casky has 301 skills that investigate the attack patterns behind CVE-2021-47980. Run one and get CVSS-scored findings in 3 minutes.

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