Proposal: Add MITRE ATLAS as a named threat classification taxonomy in CycloneDX 2.0
Nobody has claimed this yet.
Assessment
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Newbie friendliness
- 58/100
Research direction
Start by reading the discussion in CycloneDX/specification#956 and the AI evidence context in #333, then locate the threat classification taxonomy enumeration in the specification. Add ATLAS alongside the existing taxonomies and verify that the proposed threat object accepts the classification and technique fields shown in the issue.
Written by the indexing model from the issue text.
Description
Summary
This proposal requests adding MITRE ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems) as a named threat classification taxonomy in CycloneDX 2.0, alongside the existing STRIDE, LINDDUN, MAESTRO, and MITRE ATT&CK taxonomies.
This proposal follows the invitation in CycloneDX/specification#956, where @stevespringett noted that ATLAS is the natural home for AI-specific attack techniques and invited input on the taxonomy addition.
Background
MITRE ATT&CK documents attack techniques against traditional software, networks, and infrastructure. MITRE ATLAS documents attack techniques against AI and machine learning systems specifically. The two are complementary, not substitutes.
As of ATLAS v5.4.0 (February 2026): 16 tactics, 84 techniques, 56 sub-techniques, 32 mitigations, 42 real-world case studies. ATLAS data is available in STIX 2.1 format.
Without ATLAS in the taxonomy enumeration, AI-specific threats such as prompt injection (AML.T0051), model poisoning (AML.T0020), and agentic pipeline compromise (AML.T0052) cannot be formally classified in a CycloneDX BOM.
Proposed Change
Add ATLAS to the permitted threat classification taxonomy enumeration:
Current: STRIDE, LINDDUN, MAESTRO, MITRE ATT&CK
Proposed addition: ATLAS
Example threat object:
{
"threats": [{
"classification": "ATLAS",
"technique": "AML.T0051",
"name": "LLM Prompt Injection",
"description": "Adversary crafts input to manipulate LLM behavior"
}]
}
ATLAS to CWE to OWASP LLM Top 10 Mapping
| ATLAS Technique | ID | CWE | OWASP LLM Top 10 |
|---|---|---|---|
| LLM Prompt Injection | AML.T0051 | CWE-1427 | LLM01 |
| Indirect Prompt Injection | AML.T0051.001 | CWE-1427 | LLM01 |
| Jailbreak | AML.T0054 | CWE-1427 | LLM01 |
| Poison Training Data | AML.T0020 | CWE-1039 | LLM03 |
| Backdoor ML Model | AML.T0018 | CWE-506 | LLM03 |
| Steal ML Model | AML.T0030 | CWE-200 | LLM10 |
| Invert ML Model | AML.T0024 | CWE-200 | LLM06 |
| Evade ML Model | AML.T0015 | CWE-1039 | LLM09 |
| Compromise Agentic Pipeline | AML.T0052 | None yet | LLM08 |
| Manipulate LLM Tool Call | AML.T0056 | None yet | LLM08 |
| Autonomous Workflow Chaining | AML.T0058 | None yet | LLM08 |
Three agentic techniques (AML.T0052, AML.T0056, AML.T0058) have no formal CWE yet - documented as named weakness candidates in CycloneDX/specification#333.
References
- MITRE ATLAS v5.4.0: https://atlas.mitre.org
- CycloneDX/specification#956 (originating discussion)
- CycloneDX/specification#333 (AI evidence techniques)
- CWE-1427: https://cwe.mitre.org/data/definitions/1427.html
- ATLAS STIX 2.1 data: https://github.com/mitre-atlas/atlas-data
- Dominant language
- XSLT
- Stars
- 551
- Forks
- 93
- Avg merge
- 4h 51m
- Merged PRs (30d)
- 42
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
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