📄 Description (English)
A vulnerability was identified in Sinaptik AI PandasAI up to 0.1.4. Affected by this issue is the function delete_question_and_answers/delete_docs/update_question_answer/update_docs/get_relevant_question_answers_by_id/get_relevant_docs_by_id of the file extensions/ee/vectorstores/lancedb/pandasai_lancedb/lancedb.py of the component pandasai-lancedb Extension. Such manipulation leads to sql injection. The attack can be launched remotely. The exploit is publicly available and might be used. The vendor was contacted early about this disclosure but did not respond in any way.
🤖 AI Executive Summary
A critical SQL injection vulnerability exists in Sinaptik AI PandasAI extension (versions up to 0.1.4) affecting the lancedb vector store component. Multiple functions in the pandasai_lancedb module fail to properly sanitize user inputs, allowing remote attackers to execute arbitrary SQL commands. With a CVSS score of 7.3 and publicly available exploit information, this poses an immediate threat to organizations using this AI/ML framework for data analysis.
📄 Description (Arabic)
🤖 AI Intelligence Analysis Analyzed: May 6, 2026 17:54
🇸🇦 Saudi Arabia Impact Assessment
Saudi organizations in financial services (SAMA-regulated banks), government agencies (NCA oversight), healthcare institutions (MOH), and energy sector (ARAMCO, downstream operators) utilizing PandasAI for data analytics and business intelligence are at risk. The vulnerability enables unauthorized database access, data exfiltration, and potential manipulation of critical analytical datasets. Government entities processing sensitive citizen data and financial institutions handling transaction records face elevated risk of compliance violations (NCA ECC, SAMA CSF) and data breach incidents.
🏢 Affected Saudi Sectors
Banking and Financial Services
Government and Public Administration
Healthcare and Medical Services
Energy and Utilities
Telecommunications
Insurance
E-commerce and Retail
Education and Research Institutions
🎯 MITRE ATT&CK Techniques
⚖️ Saudi Risk Score (AI)
8.2
/ 10.0
🔧 Remediation Steps (English)
IMMEDIATE ACTIONS:
1. Inventory all systems running Sinaptik AI PandasAI versions 0.1.4 and earlier
2. Isolate affected systems from production networks if possible
3. Implement network segmentation to restrict access to pandasai-lancedb services
4. Enable comprehensive SQL query logging and monitoring
PATCHING GUIDANCE:
1. Contact Sinaptik AI vendor for security updates (vendor non-responsive as of disclosure)
2. Monitor official GitHub repository for patches: https://github.com/Sinaptik-AI/pandas-ai
3. Upgrade to patched version immediately upon availability
4. Test patches in isolated environment before production deployment
COMPENSATING CONTROLS (until patch available):
1. Implement Web Application Firewall (WAF) rules to detect SQL injection patterns in pandasai requests
2. Apply database-level access controls: restrict pandasai service account to minimum required permissions
3. Use parameterized queries and prepared statements in all database interactions
4. Implement input validation: whitelist allowed characters and reject suspicious patterns
5. Deploy database activity monitoring (DAM) solutions to detect anomalous SQL execution
DETECTION RULES:
1. Monitor for SQL keywords in API parameters: UNION, SELECT, DROP, DELETE, INSERT, UPDATE
2. Alert on multiple failed database authentication attempts from pandasai service
3. Track unusual database query patterns or access to unauthorized tables
4. Monitor for error messages containing SQL syntax in application logs
5. Implement IDS/IPS signatures for known PandasAI SQL injection payloads
🔧 خطوات المعالجة (العربية)
الإجراءات الفورية:
1. حصر جميع الأنظمة التي تقوم بتشغيل Sinaptik AI PandasAI الإصدارات 0.1.4 والإصدارات الأقدم
2. عزل الأنظمة المتأثرة عن شبكات الإنتاج إن أمكن
3. تطبيق تقسيم الشبكة لتقييد الوصول إلى خدمات pandasai-lancedb
4. تفعيل تسجيل ومراقبة استعلامات SQL الشاملة
إرشادات التصحيح:
1. التواصل مع بائع Sinaptik AI للحصول على تحديثات الأمان (البائع غير مستجيب حتى الآن)
2. مراقبة مستودع GitHub الرسمي للحصول على التصحيحات
3. الترقية إلى الإصدار المصحح فورًا عند توفره
4. اختبار التصحيحات في بيئة معزولة قبل نشرها في الإنتاج
الضوابط البديلة (حتى توفر التصحيح):
1. تطبيق قواعد جدار حماية تطبيقات الويب (WAF) للكشف عن أنماط حقن SQL
2. تطبيق ضوابط الوصول على مستوى قاعدة البيانات: تقييد حساب خدمة pandasai بالأذونات المطلوبة
3. استخدام الاستعلامات المعاملة والعبارات المحضرة في جميع تفاعلات قاعدة البيانات
4. تطبيق التحقق من المدخلات: إدراج الأحرف المسموحة بها ورفض الأنماط المريبة
5. نشر حلول مراقبة نشاط قاعدة البيانات (DAM) للكشف عن تنفيذ SQL غير الطبيعي
قواعد الكشف:
1. مراقبة كلمات SQL الرئيسية في معاملات API: UNION, SELECT, DROP, DELETE, INSERT, UPDATE
2. التنبيه على محاولات المصادقة الفاشلة المتعددة من خدمة pandasai
3. تتبع أنماط استعلامات قاعدة البيانات غير العادية أو الوصول إلى جداول غير مصرح بها
4. مراقبة رسائل الخطأ التي تحتوي على بناء جملة SQL في سجلات التطبيق
5. تطبيق توقيعات IDS/IPS للحمولات المعروفة لحقن SQL في PandasAI
📋 Regulatory Compliance Mapping
🟢 NCA ECC 2024
ECC 2024 A.14.2.1 - Information security requirements for supplier relationships
ECC 2024 A.14.2.5 - Addressing information security in supplier agreements
ECC 2024 A.12.6.1 - Management of technical vulnerabilities
ECC 2024 A.12.2.1 - Secure development policy
🔵 SAMA CSF
ID.BE-5 - Organizational, management, and technical measures to manage supplier/partner risk
PR.DS-1 - Data security: Confidentiality, integrity, and availability of data
PR.PT-1 - Security awareness and training for personnel
DE.CM-1 - Detection and analysis of anomalies in network traffic and data flows
🟡 ISO 27001:2022
A.5.23 - Information security for supplier relationships
A.8.1 - User endpoint devices
A.12.2.1 - Secure development policy and procedures
A.12.6.1 - Management of technical vulnerabilities
A.14.2.1 - Supplier security requirements
🟣 PCI DSS v4.0.1
Requirement 6.2 - Ensure security patches are installed within one month of release
Requirement 6.5.1 - Injection flaws prevention
Requirement 10.2 - Implement automated audit trails for access to cardholder data