CVE-2016-10553

What is this vulnerability?

CVE-2016-10553 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.

sequelize is an Object-relational mapping, or a middleman to convert things from Postgres, MySQL, MariaDB, SQLite and Microsoft SQL Server into usable data for NodeJS. A fix was pushed out that fixed potential SQL injection in sequelize 2.1.3 and earlier.

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-2016-10553

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

Sequelize ORM contains a SQL injection vulnerability when handling nested JSON objects in queries. Attackers bypass input sanitization to execute arbitrary SQL commands against the backend database. Precogs Application Security Module maps untrusted parameters directly to SQL execution 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