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

Implementation Guide

Step-by-step guide to implementing security measures in your LLM application development lifecycle.

Planning & Design

2-4 weeks

Conduct threat modeling and risk assessment

Define security requirements and acceptance criteria

Design secure architecture and data flows

Select appropriate security tools and frameworks

Development

4-8 weeks

Implement input validation and sanitization

Set up secure development environment

Implement authentication and authorization

Develop secure data handling procedures

Testing & Validation

2-3 weeks

Conduct security testing and penetration testing

Validate security controls and measures

Perform code review and security analysis

Test incident response procedures

Deployment

1-2 weeks

Deploy to secure production environment

Configure monitoring and alerting

Conduct final security validation

Document deployment and operational procedures

Maintenance

Ongoing

Regular security updates and patches

Continuous monitoring and threat detection

Periodic security assessments

Update security measures based on new threats

Quick Start Recommendations

Start with Critical Items

• Input validation and sanitization
• Authentication and access control
• Data encryption and privacy
• Secure deployment practices

Build Incrementally

• Implement security by design
• Regular security testing
• Continuous monitoring
• Iterative improvements

Security Tools & Resources

Recommended tools and frameworks to help implement and maintain security in your LLM applications.

OWASP ZAP

Security Testing

Web application security scanner

Learn More

Bandit

Code Analysis

Python security linter

Learn More

TensorFlow Privacy

Privacy

Differential privacy library

Learn More

MLflow

ML Operations

ML lifecycle management

Learn More

Kubeflow

ML Operations

ML workflows on Kubernetes

Learn More

Adversarial Robustness Toolbox

Security Testing

ML security and robustness

Learn More

Additional Security Resources

Comprehensive resources for LLM application security

Standards & Frameworks

Security Communities

Security Validation Methods

Methods and techniques to validate the effectiveness of your security implementations.

Automated Security Testing

Static Analysis

• Code security scanning (SAST)
• Dependency vulnerability scanning
• Configuration security analysis
• Infrastructure as Code scanning

Dynamic Analysis

• Runtime security testing (DAST)
• API security testing
• Penetration testing
• Fuzzing and chaos engineering

Manual Security Assessment

Security Reviews

• Architecture security review
• Code review with security focus
• Threat modeling exercises
• Security control validation

Red Team Exercises

• Adversarial testing scenarios
• Social engineering assessments
• Physical security testing
• Incident response testing

Continuous Monitoring

Security Metrics

• Vulnerability counts
• Security incident rates
• Compliance scores
• Security training completion

Operational Monitoring

• Real-time threat detection
• Anomaly detection
• Performance monitoring
• Access pattern analysis

Compliance Monitoring

• Regulatory compliance
• Policy adherence
• Audit trail integrity
• Data governance metrics

Additional Resources

Comprehensive collection of resources, documentation, and learning materials for LLM application security.

Security Documentation

Essential documentation and guides

OWASP Top 10 for LLM Applications
Comprehensive guide to LLM security vulnerabilities
LLM Security Framework
Complete security framework for LLM applications
AI Agents Attack Matrix
Attack vectors and defense strategies for AI agents

Training & Certification

Educational resources and certifications

SANS Security Training
Professional cybersecurity training programs
ML Security Specialization
Machine learning security courses on Coursera
AI Security Course
Comprehensive AI security education on edX

Research & Publications

Latest research and academic publications

arXiv Cryptography and Security
Latest research papers on security
USENIX Security Conferences
Premier security research conferences
IEEE Security & Privacy
IEEE security research and publications

Industry Reports

Industry insights and threat intelligence

Verizon Data Breach Report
Annual data breach investigations report
CrowdStrike Global Threat Report
Global threat landscape analysis
Microsoft Security Intelligence Report
Microsoft's security intelligence insights

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