Full Report
Artificial intelligence (AI) is changing the shape of the application attack surface. A traditional application assessment usually starts with familiar questions, such as:
Analysis Summary
# Best Practices: Offensive Testing of AI Systems
## Overview
As AI integration matures, the attack surface shifts from simple web endpoints to complex ecosystems involving LLMs, Retrieval-Augmented Generation (RAG), vector databases, and agentic workflows. These practices address two distinct offensive methodologies: **Architecture-led Penetration Testing** (focused on design and component security) and **Threat-led Red Teaming** (focused on simulating realistic adversary behavior).
## Key Recommendations
### Immediate Actions
1. **Inventory AI Components:** Identify all AI-enabled features, including LLMs, vector databases (Pinecone, Milvus), model registries, and data pipelines.
2. **Define Testing Objectives:** Determine if you need comprehensive technical assurance (Penetration Test) or a simulation of business-impact risks (Red Team).
3. **Sanitize Inputs:** Implement strict validation for all inputs, recognizing that "untrusted data" now includes retrieved documents and third-party API tool responses.
4. **Map Trust Boundaries:** Identify where the AI system interacts with enterprise data or executes actions (tools/plugins).
### Short-term Improvements (1-3 months)
1. **Conduct STRIDE Threat Modeling:** Apply the STRIDE framework specifically to AI artifacts, prompt layers, and MLOps infrastructure.
2. **Test for Prompt Injection:** Perform hands-on testing for both direct (user-led) and indirect (data-led) prompt injection.
3. **Audit Agent Permissions:** Ensure AI agents and MCP (Model Context Protocol) servers follow the principle of least privilege when invoking tools.
4. **Secure the RAG Pipeline:** Verify that retrieved content cannot bypass intended application controls or manipulate model logic.
### Long-term Strategy (3+ months)
1. **Establish an AI Red Teaming Program:** Transition from point-in-time tests to threat-led assessments that simulate multi-stage attacks on business logic.
2. **Integrate MLOps Security:** Incorporate model registry scanning, supply chain security for model weights, and automated security testing into the CI/CD pipeline.
3. **Implement Robust Logging & Monitoring:** Monitor AI outputs for downstream application security issues and unauthorized data disclosure.
## Implementation Guidance
### For Small Organizations
- Focus on **Architecture-led Penetration Testing** for customer-facing AI features.
- Use the OWASP Top 10 for LLM Applications as a baseline checklist.
- Prioritize securing the "Prompt Layer" and basic API authentication.
### For Medium Organizations
- Implement **STRIDE-based threat modeling** before deploying new AI workflows.
- Assess RAG pipelines for data leakage from vector databases.
- Conduct focused testing on third-party connectors and enterprise platform integrations.
### For Large Enterprises
- Execute **Threat-led Red Team Assessments** to test cross-domain resilience (e.g., can a compromised AI agent pivot to the internal network?).
- Standardize security across the entire **MLOps infrastructure**.
- Establish internal "Red Zones" for testing autonomous agentic orchestration and tool-calling capabilities.
## Configuration Examples
*Technical configurations involve verifying the following:*
- **Rate Limiting:** Implement strict tokens-per-minute (TPM) and requests-per-minute (RPM) limits at the API gateway.
- **Access Control:** Configure Vector Databases with Metadata Filtering to ensure users only retrieve documents they are authorized to see.
- **Orchestration Checks:** Ensure AI agents require manual approval ("Human-in-the-loop") for high-impact actions like data deletion or external emails.
## Compliance Alignment
- **NIST AI Risk Management Framework (AI RMF):** For overall risk governance.
- **OWASP Top 10 for LLM Applications:** For technical vulnerability mapping.
- **MITRE ATLAS:** For mapping adversary tactics and techniques against AI systems.
- **OWASP Machine Learning Security Top 10:** For data and model-specific security.
- **OWASP Agentic Top 10:** Specifically for AI agents and tool-calling security.
## Common Pitfalls to Avoid
- **Payload-Only Testing:** Relying on traditional web payloads (XSS/SQLi) while ignoring AI-specific logic like "Indirect Prompt Injection."
- **Implicit Trust in RAG:** Assuming that documents retrieved from internal databases are "safe" and do not need sanitization.
- **Ignoring the Supply Chain:** Failing to vet the security of model artifacts or third-party MCP servers.
- **Overscope/Underscope:** Not defining whether the test is architecture-focused or threat-focused, leading to misaligned stakeholder expectations.
## Resources
- **OWASP Top 10 for LLM:** [https://genai.owasp.org/]
- **MITRE ATLAS:** [https://atlas.mitre.org/]
- **STRIDE Threat Modeling:** [https://owasp.org/www-community/Threat_Modeling_Process]
- **LevelBlue SpiderLabs Research:** [https://www.levelblue.com/blogs/spiderlabs-blog]