Scope - What This Guide Covers
This guide explores how the 2025 OWASP Top 10 update impacts your security engineering practices when using AI coding assistants, deploying AI-powered features, or integrating AI models as dependencies. You'll find steps for integrating AI-aware security controls into existing governance frameworks, focusing on CI/CD pipeline automation and dependency management.
In scope: Code generation tools (GitHub Copilot, Cursor, etc.), AI models as application components, datasets as dependencies, and LLM-powered features in your applications.
Out of scope: Adversarial machine learning, model poisoning attacks, or AI ethics frameworks.
Key Concepts and Definitions
AI-Introduced Dependencies: Beyond traditional libraries, your applications now rely on pre-trained models, training datasets, prompt templates, and vector databases, each representing a potential vulnerability.
Policy-Driven Automation: Security controls that execute automatically in your CI/CD pipeline based on predefined rules. These are not manual reviews or post-deployment scans but automated gates that block builds when policy violations occur.
Compounded Risk: When AI coding assistants propagate vulnerable patterns across multiple codebases, or a compromised model affects numerous applications, the impact is greater than traditional vulnerabilities.
Requirements Breakdown
While the 2025 OWASP Top 10 hasn't finalized requirement numbers, prepare by mapping AI-specific concerns to existing categories:
A06:2021 - Vulnerable and Outdated Components now includes:
- Model versions and vulnerabilities
- Dataset provenance and integrity
- Embedding libraries and vector database clients
A08:2021 - Software and Data Integrity Failures expands to:
- Model file checksums and verification
- Training data lineage tracking
- Prompt injection attack surfaces
A03:2021 - Injection adds:
- Prompt injection in LLM-powered features
- Training data poisoning vectors
- Malicious model weights
Your existing OWASP ASVS v4.0.3 controls need updates. For V1.14 (Configuration), add model configuration verification. For V14.2 (Dependency), extend scanning to include model registries.
Implementation Guidance
Step 1: Inventory AI Dependencies
Create a software bill of materials (SBOM) that includes:
- Model name, version, source registry
- Dataset identifiers and update timestamps
- Inference framework versions (TensorFlow, PyTorch, etc.)
- API endpoints for hosted AI services
Your current dependency scanning tools won't automatically catch these. You need explicit instrumentation in your build process.
Step 2: Establish Model Governance Policies
Define acceptable use policies before embedding AI:
- Only use models from approved registries (e.g., Hugging Face Enterprise)
- Model licenses must be MIT, Apache 2.0, or pre-approved commercial
- Models over 500MB require architecture review
- No customer data sent to external LLM APIs without a data processing agreement
Step 3: Automate Security Gates in CI/CD
Your pipeline needs new checks:
Pre-commit: Scan for hardcoded API keys to AI services, embedded model files, and sensitive data in prompt templates.
Build Stage: Verify model checksums, check model licenses against policy, and validate dataset integrity.
Pre-deployment: Test for prompt injection vulnerabilities, ensure model outputs don't leak training data, and confirm inference endpoints use authentication.
Use policy-as-code tools to automate these checks.
Step 4: Extend Threat Modeling
Your threat models need AI-specific abuse cases:
- AI-suggested code containing SQL injection patterns
- Compromised model weights pulled by automated updates
- Prompt injection extracting PII from vector databases
- Training data poisoning causing misclassification
Identify controls to prevent or detect each scenario.
Step 5: Monitor AI Component Usage
Ensure runtime visibility into:
- Models running in production
- Token consumption and cost per service
- Anomalous inference patterns
- Model drift or performance degradation
This is essential if you're subject to SOC 2 Type II CC7.2 or ISO 27001 Annex A.8.16.
Common Pitfalls
Treating AI Dependencies Like Traditional Packages: Your npm audit won't catch a compromised BERT model. Use specialized tooling for model registries.
Assuming AI-Generated Code is Secure: Code suggestions from LLMs reflect patterns in their training data, including vulnerable code. Scrutinize every suggestion.
Missing the Distributed Risk: When multiple teams use the same AI coding assistant with a vulnerable pattern, you get multiple instances of the same vulnerability.
Skipping Model Provenance: Verify package signatures for npm modules and apply the same rigor to models from Hugging Face.
Ignoring Prompt Injection: If your application uses user input in LLM prompts, test it like you'd test SQL injection.
Quick Reference Table
| Risk Category | Traditional Control | AI-Specific Addition |
|---|---|---|
| Vulnerable components | Dependency scanning (Snyk, Dependabot) | Model registry scanning, dataset integrity checks |
| Injection attacks | Input validation, parameterized queries | Prompt sanitization, output filtering |
| Supply chain | Package signature verification | Model checksum validation, registry reputation |
| Data integrity | Database backups, transaction logs | Training data lineage, model versioning |
| Access control | RBAC, API authentication | Model endpoint authentication, token limits |
| Monitoring | Application logs, error tracking | Inference logs, token consumption, model drift |
| Incident response | Rollback procedures, patch management | Model replacement procedures, prompt template updates |
The 2025 update underscores that AI is integral to your application stack, development workflow, and attack surface. Your governance framework must reflect this, with automated controls that match the pace of AI-assisted development.
Start with the inventory. You can't secure what you can't see, and many organizations lack visibility into their AI components in production.
