CVE-2018-16659

What is this vulnerability?

CVE-2018-16659 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.

An issue was discovered in Rausoft ID.prove 2.95. The login page allows SQL injection via Microsoft SQL Server stacked queries in the Username POST parameter. Hypothetically, an attacker can utilize master..xp_cmdshell for the further privilege elevation.

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-2018-16659

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

Rausoft ID contains an unspecified vulnerability (often authentication bypass or RCE). Attackers exploit insecure API endpoints to access sensitive identity records or compromise the application server. Precogs API Security Engine maps all endpoints against strict authentication requirements.

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