📄 Description (English)
Apache Spark Command Injection Vulnerability — Apache Spark contains a command injection vulnerability via Spark User Interface (UI) when Access Control Lists (ACLs) are enabled.
🤖 AI Executive Summary
Apache Spark contains a critical command injection vulnerability (CVSS 9.0) in its User Interface when ACLs are enabled, allowing authenticated attackers to execute arbitrary commands on the Spark cluster. This vulnerability poses severe risk to organizations running Spark for big data analytics and machine learning workloads. Immediate patching is essential as exploits are publicly available.
📄 Description (Arabic)
🤖 AI Intelligence Analysis Analyzed: Apr 21, 2026 23:51
🇸🇦 Saudi Arabia Impact Assessment
Saudi organizations in financial services (ARAMCO, banking sector), government analytics initiatives (NCA, GOSI), and telecommunications (STC, Mobily) utilizing Apache Spark for data processing face critical risk. The vulnerability enables lateral movement within data infrastructure and potential exfiltration of sensitive datasets. Organizations processing financial transactions, citizen data, or energy sector analytics are particularly vulnerable. The command injection capability could compromise entire Spark clusters and connected systems.
🏢 Affected Saudi Sectors
Banking and Financial Services
Government and Public Administration
Energy and Utilities (ARAMCO)
Telecommunications (STC, Mobily)
Healthcare and Insurance
E-commerce and Retail
Insurance and Pension Funds (GOSI)
🎯 MITRE ATT&CK Techniques
⚖️ Saudi Risk Score (AI)
9.2
/ 10.0
🔧 Remediation Steps (English)
1. IMMEDIATE: Identify all Apache Spark deployments in your environment, particularly those with ACLs enabled
2. PATCH: Upgrade to Apache Spark 3.2.2, 3.3.1, or later versions that contain the security fix
3. NETWORK CONTROLS: Restrict access to Spark UI to trusted networks only; implement network segmentation
4. AUTHENTICATION: Enforce strong authentication for Spark UI access; disable anonymous access
5. MONITORING: Deploy detection rules to monitor for suspicious command patterns in Spark logs (look for shell metacharacters in UI parameters)
6. COMPENSATING CONTROL: If immediate patching is not possible, disable ACL functionality temporarily and implement reverse proxy authentication
7. VALIDATION: After patching, verify Spark version and test ACL functionality to ensure proper remediation
🔧 خطوات المعالجة (العربية)
1. فوري: حدد جميع نشرات Apache Spark في بيئتك، خاصة تلك التي تحتوي على قوائم التحكم بالوصول المفعلة
2. التصحيح: قم بالترقية إلى Apache Spark 3.2.2 أو 3.3.1 أو إصدارات أحدث تحتوي على إصلاح الأمان
3. عناصر التحكم بالشبكة: قيد الوصول إلى واجهة Spark على الشبكات الموثوقة فقط؛ تنفيذ تقسيم الشبكة
4. المصادقة: فرض مصادقة قوية لوصول واجهة Spark؛ تعطيل الوصول المجهول
5. المراقبة: نشر قواعد الكشف لمراقبة الأنماط المريبة للأوامر في سجلات Spark (ابحث عن أحرف shell في معاملات واجهة المستخدم)
6. التحكم البديل: إذا لم يكن التصحيح الفوري ممكناً، قم بتعطيل وظيفة ACL مؤقتاً وتنفيذ مصادقة وكيل عكسي
7. التحقق: بعد التصحيح، تحقق من إصدار Spark واختبر وظيفة ACL للتأكد من الإصلاح الصحيح
📋 Regulatory Compliance Mapping
🟢 NCA ECC 2024
A.5.1.1 - Information Security Policies (patch management)
A.8.1.1 - User Access Management (authentication controls)
A.12.2.1 - Change Management (secure deployment of patches)
A.12.6.1 - Management of Technical Vulnerabilities
🔵 SAMA CSF
ID.AM-2 - Asset Management (inventory of Spark deployments)
PR.AC-1 - Access Control Policy (authentication and authorization)
PR.PT-2 - Protective Technology (network segmentation)
DE.CM-1 - Detection and Analysis (monitoring for exploitation)
🟡 ISO 27001:2022
A.12.2.1 - Change management procedures
A.12.6.1 - Management of technical vulnerabilities
A.14.2.1 - Secure development policy
A.5.1.1 - Information security policies and procedures
🟣 PCI DSS v4.0
6.2 - Security patches and updates
6.5.1 - Injection flaws prevention
8.1 - User access control
🔗 References & Sources 0
No references.
📦 Affected Products / CPE 1 entries
Apache:Spark