Databricks introduced BlackIce at CAMLIS Red 2025, an open-source containerized toolkit that consolidates 14 widely-used AI security tools into a single, reproducible environment.
This innovation addresses critical pain points in AI red teaming by eliminating complex setup procedures and dependency conflicts that traditionally hinder security testing workflows.
AI red teamers face four persistent obstacles that fragment their testing workflows. First, each security tool demands unique configuration and setup procedures, consuming valuable testing time.
Second, dependency conflicts force teams to maintain separate runtime environments for different tools.
Third, managed notebooks restrict operations to single Python interpreters per kernel, limiting testing flexibility.
Finally, the rapidly expanding AI security tool landscape intimidates newcomers navigating unfamiliar capabilities and implementation requirements.
BlackIce addresses these challenges directly by packaging pre-configured tools into a unified Docker container, inspired by Kali Linux’s approach to penetration testing infrastructure.
The toolkit bundles 14 open-source tools selected for coverage across Responsible AI, security testing, and adversarial machine learning domains.
The included tools span from established platforms like LM Eval Harness (10.3K GitHub stars) and Promptfoo (8.6K stars) to specialized utilities such as Microsoft’s PyRIT (2.9K stars) and CyberArk’s Fuzzy AI (800 stars).
This diverse selection ensures comprehensive vulnerability assessment across multiple attack vectors.
| Feature | Description |
|---|---|
| Containerized toolkit | Ships as a version-pinned Docker image for reproducible AI red teaming environments. |
| 14 integrated AI security tools | Bundles leading Responsible AI, adversarial ML, and security testing tools in one image. |
| Unified CLI | Exposes tools through a single command-line interface for shell and notebook workflows. |
| Static and dynamic tool modes | Static tools via simple CLI, dynamic tools with Python-based customization for advanced attacks. |
| Isolated environments for static tools | Uses separate virtual envs/Node.js projects to avoid dependency conflicts. |
| Global environment for dynamic tools | Centralized Python environment managed via a shared requirements configuration. |
| Databricks-native integration | Pre-patched to talk directly to Databricks Model Serving and workspace endpoints. |
| Framework-aligned coverage | Mapped to MITRE ATLAS and DASF to cover prompt injection, jailbreaks, leakage, and hallucinations. |
| Supply chain and artifact scanning | Supports detecting unsafe AI artifacts and malicious model files. |
| Cloud-friendly deployment | Designed to run via Databricks Container Services for scalable AI security testing. |
BlackIce organizes tools into two operational categories. Static tools provide command-line evaluation with minimal programming prerequisites, enabling rapid testing workflows.
Dynamic tools offer equivalent capabilities plus advanced Python-based customization, allowing security engineers to develop sophisticated custom attack scenarios.
Within the container, static tools maintain isolated Python virtual environments or Node.js projects with independent dependencies accessible directly from the command-line interface.
Dynamic tools occupy the global Python environment with conflicts managed through centralized dependency configuration.
Security Coverage Framework
Databricks mapped BlackIce capabilities against MITRE ATLAS and the Databricks AI Security Framework (DASF) to demonstrate comprehensive threat modeling alignment.
The toolkit addresses critical vulnerability classes including prompt injection, LLM jailbreaks, indirect prompt injection via untrusted content in retrieval-augmented generation systems, LLM data leakage, hallucination detection, adversarial example generation, and supply-chain artifact scanning for malicious code.
This multi-layered coverage ensures organizations can identify weaknesses across the AI security landscape, from model-level attacks to application-layer threats and infrastructure vulnerabilities.
BlackIce is available on Databricks’ Docker Hub and can be deployed using standard containerization commands. Users configure Databricks compute clusters with Container Services, specifying the BlackIce image URL during cluster creation.
The toolkit integrates seamlessly with Databricks Model Serving endpoints through custom patches applied during image construction, enabling out-of-the-box compatibility with workspaces.
The GitHub repository provides detailed build documentation, allowing organizations to customize tool selections and manage version updates independently.
A demo notebook demonstrates orchestrating multiple security tools within a unified environment for comprehensive vulnerability assessment.
Access the toolkit via docker pull databricksruntime/blackice:17.3-LTS and configure compute environments through Databricks Container Services.
The accompanying demo notebook and GitHub repository provide implementation examples and architectural guidance for immediate deployment in security testing workflows.
BlackIce represents a significant advancement in democratizing AI security testing, enabling organizations to conduct rigorous vulnerability assessments without navigating complex tool integration challenges.
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