LLM Application Security Checklist

Comprehensive security checklist for developers and security professionals building secure Large Language Model applications

60+

Security Controls

Security Categories

Implementation Phases

15+

Security Tools

Comprehensive Security Checklist

Essential security controls organized by category. Each item represents a critical security measure for LLM applications.

This checklist should be adapted to your specific use case and regulatory requirements. Consider engaging security professionals for complex implementations.

Input Validation & Sanitization

Critical

Protect against prompt injection and malicious inputs

Implement strict input length limits and character filtering

Validate and sanitize all user inputs before processing

Use allowlists for acceptable input patterns and formats

Implement rate limiting to prevent abuse and DoS attacks

Deploy content filtering for harmful or inappropriate content

Validate file uploads and restrict file types and sizes

Implement CSRF protection for all form submissions

Use parameterized queries to prevent injection attacks

Data Privacy & Protection

Critical

Safeguard sensitive information and user data

Implement data classification and handling procedures

Use encryption for data at rest and in transit

Apply data minimization principles - collect only necessary data

Implement proper data retention and deletion policies

Use differential privacy techniques where applicable

Anonymize or pseudonymize personal data before processing

Implement access controls based on least privilege principle

Regular audit of data access and usage patterns

Model Training Security

High

Secure the model development and training process

Validate and sanitize all training data sources

Implement data poisoning detection mechanisms

Use secure, isolated training environments

Version control for datasets and model artifacts

Implement model integrity checks and validation

Use federated learning for sensitive data scenarios

Regular security audits of training pipelines

Implement supply chain security for ML dependencies

Deployment & Infrastructure

Critical

Secure deployment and infrastructure management

Use secure container images and runtime environments

Implement network segmentation and firewall rules

Deploy with least privilege access controls

Use secrets management for API keys and credentials

Implement comprehensive logging and monitoring

Regular security updates and patch management

Use HTTPS/TLS for all communications

Implement backup and disaster recovery procedures

Output Handling & Validation

High

Secure processing and validation of model outputs

Validate and sanitize all model outputs before display

Implement output filtering for sensitive information

Use context-aware output encoding (HTML, JSON, etc.)

Implement content security policies (CSP)

Monitor outputs for potential data leakage

Use structured output formats where possible

Implement human-in-the-loop validation for critical outputs

Regular testing of output validation mechanisms

Access Control & Authentication

Critical

Manage user access and authentication securely

Implement multi-factor authentication (MFA)

Use role-based access control (RBAC)

Regular review and audit of user permissions

Implement session management and timeout policies

Use OAuth 2.0 or similar secure authentication protocols

Implement account lockout policies for failed attempts

Regular security training for users and administrators

Implement privileged access management (PAM)

Monitoring & Incident Response

High

Continuous monitoring and incident response capabilities

Implement real-time security monitoring and alerting

Use SIEM tools for log analysis and correlation

Regular security assessments and penetration testing

Develop and test incident response procedures

Implement anomaly detection for unusual patterns

Monitor for model drift and performance degradation

Regular backup testing and recovery procedures

Maintain security incident documentation and lessons learned

Compliance & Governance

Medium

Ensure regulatory compliance and governance

Understand and comply with relevant regulations (GDPR, CCPA, etc.)

Implement data governance frameworks

Regular compliance audits and assessments

Maintain documentation for audit trails

Implement privacy impact assessments (PIAs)

Establish AI ethics and responsible AI practices

Regular legal and compliance review of AI systems

Implement data subject rights management

Related Security Research

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Critical vulnerability analysis for LLM prompt manipulation techniques

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