> ## Documentation Index
> Fetch the complete documentation index at: https://hydroxai.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# MITRE ATLAS

> MITRE ATLAS — Adversarial Threat Landscape for AI Systems, a knowledge base of adversarial tactics and techniques targeting AI.

## Overview

**MITRE ATLAS** (Adversarial Threat Landscape for Artificial-Intelligence Systems) is a knowledge base of adversarial tactics, techniques, and case studies for machine learning systems. Modeled after the widely-used MITRE ATT\&CK framework for cybersecurity, ATLAS provides a structured approach to understanding how AI systems can be attacked.

ATLAS helps security teams, red-teamers, and AI engineers identify and defend against real-world adversarial threats to machine learning models and AI applications.

## Tactics

ATLAS organizes adversarial behavior into tactics — the "why" behind an attack:

| Tactic                   | Description                                                           |
| ------------------------ | --------------------------------------------------------------------- |
| **Reconnaissance**       | Gathering information about the target ML system                      |
| **Resource Development** | Acquiring resources for the attack (datasets, models, infrastructure) |
| **Initial Access**       | Gaining initial access to the ML system or its components             |
| **ML Model Access**      | Obtaining access to the target model (API access, model extraction)   |
| **Execution**            | Running adversarial techniques against the ML system                  |
| **Persistence**          | Maintaining access or influence over the ML system                    |
| **Defense Evasion**      | Avoiding detection by security controls and monitoring                |
| **Discovery**            | Learning about the ML system's architecture and behavior              |
| **Collection**           | Gathering data from the ML system (model outputs, training data)      |
| **ML Attack Staging**    | Preparing attack payloads (adversarial examples, poisoned data)       |
| **Exfiltration**         | Extracting data or model information from the target system           |
| **Impact**               | Disrupting, degrading, or destroying the ML system's function         |

## Key techniques

| Technique                | Description                                                                     |
| ------------------------ | ------------------------------------------------------------------------------- |
| **Adversarial examples** | Crafted inputs that cause the model to misclassify or produce incorrect outputs |
| **Data poisoning**       | Contaminating training data to introduce backdoors or biases                    |
| **Model extraction**     | Querying a model to reconstruct a functionally equivalent copy                  |
| **Model inversion**      | Recovering training data or sensitive information from model outputs            |
| **Prompt injection**     | Manipulating LLM behavior through crafted inputs                                |
| **Backdoor attacks**     | Embedding hidden triggers in models that activate under specific conditions     |
| **Membership inference** | Determining whether specific data was used in model training                    |
| **Model evasion**        | Crafting inputs specifically to bypass model-based security controls            |

## How Know Your AI maps to MITRE ATLAS

| ATLAS Tactic / Technique       | Know Your AI Coverage                               |
| ------------------------------ | --------------------------------------------------- |
| **Reconnaissance / Discovery** | System prompt extraction datasets                   |
| **ML Model Access**            | API & website evaluation modes                      |
| **Execution**                  | Red-team attack datasets (15+ methods)              |
| **ML Attack Staging**          | Curated attack datasets in the Marketplace          |
| **Adversarial examples**       | Jailbreak, CIPHER, DAN, and other evasion methods   |
| **Data poisoning / Bias**      | Bias detection datasets                             |
| **Model extraction**           | Data extraction attack datasets                     |
| **Prompt injection**           | Prompt injection datasets (PAIR, ADAPTIVE, etc.)    |
| **Defense evasion**            | Multi-method attack testing to find guardrail gaps  |
| **Exfiltration**               | PII leakage and data extraction testing             |
| **Impact**                     | Security scoring and compliance analysis            |
| **Continuous monitoring**      | SDK monitoring and tracing for production detection |

## ATLAS vs. ATT\&CK

| Aspect         | MITRE ATT\&CK                             | MITRE ATLAS                                            |
| -------------- | ----------------------------------------- | ------------------------------------------------------ |
| **Focus**      | Traditional IT systems                    | AI and ML systems                                      |
| **Targets**    | Networks, endpoints, cloud                | Models, training pipelines, inference APIs             |
| **Techniques** | Malware, exploits, phishing               | Adversarial examples, prompt injection, data poisoning |
| **Adoption**   | Industry standard for security operations | Growing adoption for AI security                       |

## Resources

* [MITRE ATLAS Website](https://atlas.mitre.org/)
* [ATLAS Navigator](https://atlas.mitre.org/matrices/ATLAS)
* [ATLAS Case Studies](https://atlas.mitre.org/studies)

<CardGroup cols={2}>
  <Card title="Datasets" icon="database" href="/datasets">
    Browse datasets aligned with ATLAS techniques.
  </Card>

  <Card title="Firewall" icon="fire" href="/firewall">
    Real-time defense against adversarial attacks.
  </Card>
</CardGroup>