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ATLAS Technique Lookup

atlas_technique_lookup
Read-onlyIdempotent

Look up a MITRE ATLAS technique by ID to retrieve AI/ML adversarial attack details, including tactics, maturity, and related ATT&CK 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: 30/hr, Pro: 500/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 already provide readOnlyHint, idempotentHint, destructiveHint. Description adds value by noting 404 on missing ID, rate limits (30/hr free, 500/hr pro), sub-technique inheritance of tactics, and return structure fields. 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 somewhat long but well-structured and front-loaded with purpose. Every sentence adds value, though a slight tightening could improve conciseness 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 single parameter and presence of an output schema (implied by return description), the description is complete: covers use cases, error handling, rate limits, return fields, and relationships to other tools and ATT&CK/D3FEND.

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?

Only one parameter (technique_id) with 100% schema description coverage. The schema already describes the format with examples. The tool description does not add additional parameter meaning beyond what's in the schema, so 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 it looks up MITRE ATLAS techniques, which are AI/ML adversarial attack TTPs. It distinguishes from siblings like atlas_technique_search (search vs lookup) and bulk_atlas_technique_lookup (single vs bulk).

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?

Explicitly says when to use: when user asks about AI/ML threats, LLM red-teaming, or adversarial ML. Also advises using bulk version for multiple techniques and mentions pivoting to D3FEND and CVE search.

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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