CVE-2020-17463

What is this vulnerability?

CVE-2020-17463 is categorized as a critical SQL Injection flaw with a CVSS base score of 9.8. Based on our vulnerability intelligence, this issue occurs when the application fails to securely handle untrusted data boundaries.

FUEL CMS 1.4.7 allows SQL Injection via the col parameter to /pages/items, /permissions/items, or /navigation/items.

This architectural defect enables adversaries to bypass intended security controls, directly manipulating the application's execution state or data layer. Immediate strategic intervention is required.

Risk Assessment

Impact on Systems

✅ Data Exfiltration: Attackers can extract sensitive data from backend databases, configuration files, or internal services.

✅ Authentication Bypass: Exploiting this flaw may allow unauthorized access to protected resources and administrative interfaces.

✅ Lateral Movement: Once initial access is gained, attackers can pivot to internal systems and escalate privileges.

How to Fix and Mitigate CVE-2020-17463

1. Apply Vendor Patches Immediately: This vulnerability is listed in CISA's Known Exploited Vulnerabilities catalog. Apply updates per vendor instructions.
2. Verify Patch Deployment: Confirm all instances are updated using Precogs continuous monitoring.
3. Review Audit Logs: Investigate historical access logs for indicators of compromise related to this attack surface.
4. Implement Defense-in-Depth: Deploy WAF rules, network segmentation, and endpoint detection to limit blast radius.

Defending with Precogs AI

FUEL CMS improperly handles input in the pages/select controller, leading to an unauthenticated remote code execution vulnerability. Attackers inject PHP code via the filter parameter to take over the application server. Precogs API Security Engine maps untrusted parameters directly to evaluation sinks.

Use Precogs to continuously scan your codebase, binaries, APIs, and infrastructure for this vulnerability class and related attack patterns. Our AI-powered detection engine combines static analysis with threat intelligence to identify exploitable weaknesses before attackers do.

Start scanning with Precogs →

Vulnerability Code Signature

Attack Data Flow

Vulnerable Code Pattern

# ❌ VULNERABLE: Direct string concatenation
def get_user(user_id):
    query = "SELECT * FROM users WHERE id = '" + user_id + "'"
    cursor.execute(query)  # Taint sink: unparameterized query
    return cursor.fetchone()

Secure Code Pattern

# ✅ SECURE: Parameterized query
def get_user(user_id):
    query = "SELECT * FROM users WHERE id = %s"
    cursor.execute(query, (user_id,))  # Sanitized binding
    return cursor.fetchone()

How Precogs Detects This

Precogs AI Analysis Engine traces data flow from HTTP request parameters through string concatenation directly into database execution sinks, identifying critical SQL injection vectors via Code Property Graph traversal.\n