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atlas_technique_lookup

Read-onlyIdempotent

Look up MITRE ATLAS technique details for AI/ML adversarial attacks. Includes tactics, maturity, and attack references.

Instructions

Look up a MITRE ATLAS technique — the AI/ML adversarial attack catalog. ATLAS catalogues TTPs targeting machine learning systems: prompt injection, model evasion, training data poisoning, model theft, etc. Roughly 80% of ATLAS techniques are AI/ML-specific (no ATT&CK bridge); 20% mirror an enterprise ATT&CK technique via attack_reference_id — use that to pivot to D3FEND defenses (d3fend_defense_for_attack) and CVE search. Sub-techniques inherit tactics from the parent (inherited_tactics=true flag) when ATLAS upstream leaves them empty. Use this tool when the user asks about AI/ML threats, LLM red-teaming, or adversarial ML; for multiple techniques in one call (e.g. drilling into a case study's techniques_used), prefer bulk_atlas_technique_lookup. Returns 404 when the id is not in the synced ATLAS catalog. Free: 100/hr, Pro: 1000/hr. Returns {technique_id, name, description, tactics, inherited_tactics, maturity (demonstrated|feasible|realized), attack_reference_id, attack_reference_url, subtechnique_of, created_date, modified_date, next_calls}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
technique_idYesMITRE ATLAS technique id, format 'AML.T####' or 'AML.T####.###' for sub-techniques (e.g. 'AML.T0000', 'AML.T0051' LLM Prompt Injection, 'AML.T0000.000').

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations indicate readOnly and idempotent, and the description adds rich behavioral context: the ATLAS-ATT&CK relationship, sub-technique inheritance (inherited_tactics flag), 404 on missing id, rate limits, and return field details. No contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with front-loaded purpose and subsequent details. It is moderately concise, with every sentence adding value, though it could be slightly trimmed without losing information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (ATLAS catalog, sub-techniques, rate limits, output schema), the description covers usage, pivots, error conditions, and limits. The output schema is present, so return field details are not required.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with a clear parameter description including format examples. The description reinforces the format but adds minimal new semantic value beyond examples. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool looks up a MITRE ATLAS technique, specifying the resource (AI/ML adversarial attack catalog) and verb. It distinguishes from siblings by mentioning atlas_technique_search and bulk_atlas_technique_lookup, and provides explicit differentiation for multiple techniques.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit guidance on when to use this tool (for AI/ML threats, LLM red-teaming, adversarial ML) and when to prefer an alternative (bulk_atlas_technique_lookup for multiple techniques). It also explains pivoting to D3FEND and CVE via attack_reference_id.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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