📚 Knowledge Base

LLM applications present unique security challenges that align with multiple SAMA CSF domains and NCA ECC controls. Key risks include: (1) Prompt Injection Attacks - where malicious inputs manipulate the model to bypass safety controls or leak sensitive data, requiring input validation controls under SAMA CSF Domain 1 (Cybersecurity Management) and NCA ECC 1-1 (Security Governance); (2) Training Data Poisoning - compromising model integrity through malicious training data, addressed under SAMA CSF Domain 5 (Third Party & Cloud Computing Cybersecurity) when using external models; (3) Sensitive Data Exposure - LLMs may inadvertently memorize and reveal PII, financial data, or confidential information, requiring strict data classification per PDPL Article 6 and SAMA CSF Domain 2 (Cybersecurity Defense); (4) Model Inversion & Extraction - attackers reconstructing training data or stealing model parameters, necessitating access controls per NCA ECC 5-1 (Access Control); (5) Insecure Plugin Integrations - third-party plugins creating attack vectors, requiring vendor risk assessment under SAMA CSF 5.1; (6) Denial of Service - resource exhaustion through expensive queries, requiring capacity management per NCA ECC 2-4 (Operational Security); (7) Supply Chain Vulnerabilities - dependencies on external model providers and APIs, addressed under SAMA CSF Domain 5 and ISO/IEC 27036 supply chain security. Organizations must implement LLM-specific security controls including input sanitization, output filtering, rate limiting, model access governance, continuous monitoring per SAMA CSF 1.7, and alignment with ISO/IEC 42001 AI management system requirements. For financial institutions, SAMA's technology risk management guidelines require comprehensive risk assessments before deploying LLM applications in production environments.

LLM applications introduce unique security challenges that Saudi organizations must address to comply with SAMA CSF, NCA ECC, and PDPL requirements:

Prompt Injection Attacks: Malicious inputs can manipulate LLM behavior to bypass security controls, leak sensitive data, or execute unintended actions. Organizations must implement input validation, sanitization, and privilege separation between user prompts and system instructions.

Data Leakage and Privacy Violations: LLMs may inadvertently expose training data, personal information, or confidential business data in responses. Under PDPL, organizations processing personal data through LLMs must implement data minimization, anonymization, and access controls. SAMA CSF Domain 3 (Data Security and Privacy) requires encryption and classification of sensitive data before LLM processing.

Model Poisoning and Supply Chain Risks: Pre-trained models or fine-tuning datasets may contain backdoors or biased data. NCA ECC Control 5-2-1 mandates secure software development practices, requiring organizations to verify model provenance, audit training data sources, and implement model validation procedures.

Insecure Output Handling: LLM outputs may contain code injection, cross-site scripting (XSS), or SQL injection payloads if not properly sanitized before use in downstream systems. Organizations must treat LLM outputs as untrusted and apply output encoding and validation.

Excessive Agency and Permissions: LLMs with unrestricted access to APIs, databases, or system functions pose significant risk. Apply principle of least privilege, implement function-calling controls, and require human approval for high-risk operations.

Denial of Service and Resource Exhaustion: LLM queries can be computationally expensive. Implement rate limiting, query complexity analysis, and resource quotas to prevent abuse and ensure availability per NCA ECC Control 4-1-1.

Compliance Considerations: For financial institutions under SAMA supervision, LLM applications handling customer data must meet SAMA CSF requirements for AI governance, model risk management, and explainability. Vision 2030's digital transformation goals emphasize responsible AI adoption with robust security controls.