How Many Skills Are Mapped to MITRE ATLAS for AI-Driven Attack Simulations?

The Anthropic Cybersecurity Skills repository contains 81 distinct skills mapped to the MITRE ATLAS framework, providing structured coverage of adversarial machine learning techniques for AI-driven attack simulations.

The Anthropic Cybersecurity Skills library maintains a comprehensive dataset of security scenarios for testing AI model robustness. Understanding the coverage of skills mapped to MITRE ATLAS allows red teams to evaluate defenses against standardized adversarial-ML tactics targeting large language models and machine learning pipelines.

MITRE ATLAS Coverage Scope

The 81-Skill Mapping

According to ATTACK_COVERAGE.md in the repository root, the library maintains exactly 81 skills with explicit MITRE ATLAS mappings. These entries connect practical attack simulations to specific adversarial techniques defined in the ATLAS framework.

The coverage encompasses critical AI attack vectors including agentic-AI context poisoning, tool-invocation abuse, and malicious model deployment. Each skill references specific ATLAS technique IDs to enable precise threat modeling and standardized evaluation criteria.

Framework Version and Integration

As documented in README.md, the repository maps skills to MITRE ATLAS v5.4, covering 16 tactics and 84 distinct techniques. This ATLAS integration operates as one component of a five-framework coverage strategy that also includes:

• MITRE ATT&CK for traditional enterprise threats
• MITRE D3FEND for defensive countermeasures
• NIST CSF 2.0 for cybersecurity risk management
• NIST AI RMF for AI governance and risk frameworks

Locating ATLAS Mappings in the Repository

ATTACK_COVERAGE.md

The ATTACK_COVERAGE.md file serves as the canonical reference for framework statistics. This file explicitly documents the count of 81 ATLAS-mapped skills and categorizes them by adversarial technique families.

Skill Definition Files

Individual skill mappings reside in skills/**/SKILL.md files throughout the repository. Each file contains YAML front matter with an atlas_techniques array linking the skill to specific ATLAS technique identifiers.

For cross-framework analysis, skills/**/references/standards.md documents how each skill maps across all five supported frameworks (ATT&CK, ATLAS, D3FEND, NIST CSF, and NIST AI RMF), providing holistic threat context.

Querying ATLAS-Mapped Skills

Using the Skills CLI

Discover ATLAS-associated skills via command line after installing the skill set:

# Install the skill set (once)

npx skills add mukul975/Anthropic-Cybersecurity-Skills

# List all skills that have ATLAS technique IDs

skills list --filter atlas_techniques

Programmatic Extraction

Analyze mappings programmatically by parsing the YAML front matter from skill definitions:

import pathlib
import yaml

def load_atlas_skills():
    skills_dir = pathlib.Path("skills")
    atlas_skills = []
    
    for skill_path in skills_dir.rglob("SKILL.md"):
        content = skill_path.read_text()
        # Extract YAML front matter between --- delimiters

        front_matter = content.split("---", 2)[1]
        data = yaml.safe_load(front_matter)
        
        if data.get("atlas_techniques"):
            atlas_skills.append(data["name"])
            
    return atlas_skills

count = len(load_atlas_skills())
print(f"ATLAS-mapped skills: {count}")

This implementation recursively searches the skills/ directory, extracting ATLAS technique references from each skill's metadata to quantify adversarial-ML coverage as implemented in mukul975/Anthropic-Cybersecurity-Skills.

Summary

• The repository contains 81 skills mapped to MITRE ATLAS for AI-driven attack simulations.
• Framework mappings are defined in ATTACK_COVERAGE.md and individual skills/**/SKILL.md files.
• Coverage aligns with MITRE ATLAS v5.4 (16 tactics, 84 techniques) and integrates with four additional security frameworks.
• Skills address specific adversarial-ML vectors including context poisoning and malicious tool invocation.

Frequently Asked Questions

What is MITRE ATLAS and why is it used for AI security testing?

MITRE ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems) is a knowledge base of tactics and techniques targeting AI and ML systems. Security teams use it to standardize red team operations against AI models because it provides specific taxonomy for ML attacks like model evasion, training data poisoning, and prompt injection that traditional frameworks like ATT&CK do not cover.

How do I determine which specific ATLAS techniques a skill simulates?

Check the atlas_techniques array in the skill's SKILL.md front matter. This array contains the specific ATLAS technique IDs (e.g., AML.T0015 for ML Supply Chain Compromise) that the skill exercises, allowing precise alignment with the MITRE framework's adversarial-ML taxonomy.

What version of MITRE ATLAS does the Anthropic Cybersecurity Skills repository reference?

The repository references MITRE ATLAS version 5.4, which encompasses 16 tactics and 84 techniques according to the README.md framework overview. This version includes contemporary adversarial-ML tactics relevant to large language models and autonomous AI agents.

How does ATLAS coverage interact with other frameworks in the repository?

ATLAS provides AI-specific offensive tactics, while MITRE ATT&CK covers traditional cyber threats, MITRE D3FEND provides defensive mappings, and NIST frameworks address risk governance. Together, these five frameworks enable comprehensive evaluation spanning AI attack simulation, enterprise security, defensive architecture, and regulatory compliance.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}