CVE-2017-10202

What is this vulnerability?

CVE-2017-10202 is categorized as a critical security flaw with a CVSS base score of 9.9. Based on our vulnerability intelligence, this issue occurs when the application fails to securely handle untrusted data boundaries.

Vulnerability in the OJVM component of Oracle Database Server. Supported versions that are affected are 11.2.0.4, 12.1.0.2 and 12.2.0.1. Easily exploitable vulnerability allows low privileged attacker having Create Session, Create Procedure privilege with network access via multiple protocols to compromise OJVM. While the vulnerability is in OJVM, attacks may significantly impact additional products. Successful attacks of this vulnerability can result in takeover of OJVM. Note: This score is for Windows platforms. On non-Windows platforms Scope is Unchanged, giving a CVSS Base Score of 8.8. CVSS 3.0 Base Score 9.9 (Confidentiality, Integrity and Availability impacts). CVSS Vector: (CVSS:3.0/AV:N/AC:L/PR:L/UI:N/S:C/C:H/I:H/A:H).

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-2017-10202

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: Unsanitized Input Flow
def process_request(request):
    user_input = request.GET.get('data')
    # Taint sink: processing untrusted data
    execute_logic(user_input)
    return {"status": "success"}

Secure Code Pattern

# ✅ SECURE: Input Validation & Sanitization
def process_request(request):
    user_input = request.GET.get('data')
    
    # Sanitized boundary check
    if not is_valid_format(user_input):
        raise ValueError("Invalid input format")
        
    sanitized_data = sanitize(user_input)
    execute_logic(sanitized_data)
    return {"status": "success"}

How Precogs Detects This

Precogs AI Analysis Engine maps untrusted input directly to execution sinks to catch complex application security vulnerabilities.