📄 Description (English)
A vulnerability was found in ScrapeGraphAI scrapegraph-ai up to 1.74.0. The affected element is the function create_sandbox_and_execute of the file scrapegraphai/nodes/generate_code_node.py of the component GenerateCodeNode Component. The manipulation results in os command injection. The attack may be launched remotely. The exploit has been made public and could be used. The vendor was contacted early about this disclosure but did not respond in any way.
🤖 AI Executive Summary
CVE-2026-5532 is a medium-severity OS command injection vulnerability in ScrapeGraphAI (scrapegraph-ai) versions up to 1.74.0 affecting the GenerateCodeNode component. The vulnerability allows remote attackers to execute arbitrary OS commands through the create_sandbox_and_execute function. With public exploit availability and no vendor patch, immediate mitigation is required for organizations using this library in production environments.
📄 Description (Arabic)
🤖 AI Intelligence Analysis Analyzed: May 18, 2026 17:22
🇸🇦 Saudi Arabia Impact Assessment
Saudi organizations using ScrapeGraphAI for web scraping, data extraction, or automation tasks face direct risk. Primary impact sectors include: Financial Services (SAMA-regulated banks using scraping for market data), Government agencies (NCA oversight) utilizing web intelligence tools, E-commerce platforms, Digital transformation initiatives, and Research institutions. The vulnerability enables complete system compromise if exploited, potentially leading to data exfiltration, lateral movement, and infrastructure takeover. Organizations in regulated sectors (banking/SAMA, government/NCA) face compliance violations if compromised.
🏢 Affected Saudi Sectors
Banking and Financial Services
Government and Public Administration
E-commerce and Retail
Telecommunications
Energy and Utilities
Healthcare
Research and Education
Digital Transformation Initiatives
🎯 MITRE ATT&CK Techniques
⚖️ Saudi Risk Score (AI)
6.8
/ 10.0
🔧 Remediation Steps (English)
IMMEDIATE ACTIONS:
1. Inventory all systems and applications using scrapegraph-ai library versions ≤1.74.0
2. Isolate affected systems from production networks if possible
3. Implement network segmentation to restrict outbound command execution
4. Enable comprehensive logging and monitoring of the GenerateCodeNode component
PATCHING GUIDANCE:
1. Monitor vendor repository for security updates (currently no patch available)
2. Consider upgrading to latest version when available
3. If upgrade not immediately possible, implement input validation and sanitization
COMPENSATING CONTROLS:
1. Implement strict input validation on all user-supplied parameters to create_sandbox_and_execute function
2. Use Web Application Firewall (WAF) rules to detect command injection patterns
3. Run scrapegraph-ai in containerized environments with restricted capabilities (no shell access)
4. Implement least-privilege execution contexts (non-root users, restricted file system permissions)
5. Disable shell metacharacters in input processing
DETECTION RULES:
1. Monitor for suspicious process spawning from Python/scrapegraph-ai processes
2. Alert on execution of shell commands (bash, sh, cmd.exe) from application context
3. Log all function calls to create_sandbox_and_execute with full parameter inspection
4. Detect command injection patterns: backticks, $(), pipe operators, semicolons in user input
5. Monitor file system access patterns for unauthorized modifications
🔧 خطوات المعالجة (العربية)
الإجراءات الفورية:
1. حصر جميع الأنظمة والتطبيقات التي تستخدم مكتبة scrapegraph-ai الإصدارات ≤1.74.0
2. عزل الأنظمة المتأثرة عن شبكات الإنتاج إن أمكن
3. تطبيق تقسيم الشبكة لتقييد تنفيذ الأوامر الصادرة
4. تفعيل السجلات الشاملة ومراقبة مكون GenerateCodeNode
إرشادات التصحيح:
1. مراقبة مستودع البائع للتحديثات الأمنية (لا يوجد تصحيح متاح حالياً)
2. النظر في الترقية إلى أحدث إصدار عند توفره
3. إذا لم يكن الترقية ممكنة فوراً، تطبيق التحقق من صحة المدخلات والتطهير
الضوابط البديلة:
1. تطبيق التحقق الصارم من صحة المدخلات على جميع المعاملات المزودة من المستخدم لدالة create_sandbox_and_execute
2. استخدام قواعد جدار حماية تطبيقات الويب (WAF) للكشف عن أنماط حقن الأوامر
3. تشغيل scrapegraph-ai في بيئات حاويات مع قدرات مقيدة (بدون وصول shell)
4. تطبيق سياق التنفيذ بأقل امتياز (مستخدمون غير جذر، أذونات نظام ملفات مقيدة)
5. تعطيل أحرف shell الفوقية في معالجة المدخلات
قواعد الكشف:
1. مراقبة توليد العمليات المريبة من عمليات Python/scrapegraph-ai
2. التنبيه على تنفيذ أوامر shell (bash, sh, cmd.exe) من سياق التطبيق
3. تسجيل جميع استدعاءات الدوال إلى create_sandbox_and_execute مع فحص المعاملات الكاملة
4. الكشف عن أنماط حقن الأوامر: علامات الاقتباس العكسية، $()، عوامل الأنابيب، الفواصل المنقوطة في مدخلات المستخدم
5. مراقبة أنماط الوصول إلى نظام الملفات للتعديلات غير المصرح بها
📋 Regulatory Compliance Mapping
🟢 NCA ECC 2024
ECC 2024 A.12.6.1 - Management of technical vulnerabilities
ECC 2024 A.14.2.1 - Secure development policy
ECC 2024 A.12.2.1 - Monitoring and logging of access
🔵 SAMA CSF
SAMA CSF ID.RA-1 - Asset Management and Identification
SAMA CSF PR.IP-12 - Software Development Security
SAMA CSF DE.CM-1 - Detection and Analysis
🟡 ISO 27001:2022
ISO 27001:2022 A.12.2.1 - Information and other assets associated with information processing facilities
ISO 27001:2022 A.14.2.1 - Secure development policy and procedures
ISO 27001:2022 A.8.1.1 - Inventory of assets
🟣 PCI DSS v4.0.1
PCI DSS 6.2 - Ensure all system components and software are protected from known vulnerabilities
PCI DSS 11.2 - Run automated vulnerability scanning tools regularly