CVE-2018-15447

What is this vulnerability?

CVE-2018-15447 is categorized as a medium SQL Injection flaw with a CVSS base score of 6.5. Based on our vulnerability intelligence, this issue occurs when the application fails to securely handle untrusted data boundaries.

A vulnerability in the web framework code of Cisco Integrated Management Controller (IMC) Supervisor could allow an unauthenticated, remote attacker to execute arbitrary SQL queries. The vulnerability is due to a lack of proper validation of user-supplied input in SQL queries. An attacker could exploit this vulnerability by sending crafted URLs that contain malicious SQL statements to the affected application.

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-2018-15447

1. Apply Vendor Patches: Upgrade affected components to their latest, non-vulnerable versions immediately.
2. Implement Input Validation: Ensure all user-supplied data is validated, sanitized, and type-checked before processing.
3. Deploy Runtime Protection: Use Precogs continuous monitoring to detect exploitation attempts in real time.
4. Audit Dependencies: Review and update all third-party libraries and transitive dependencies.

Defending with Precogs AI

Precogs AI Analysis Engine identifies this vulnerability class through semantic code analysis powered by Code Property Graph (CPG) technology, performing inter-procedural taint tracking to detect injection flaws, broken authentication, and insecure data flows across your entire codebase.

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.

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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.