TopAIThreats vs AIID vs MITRE ATLAS vs MIT AI Risk…

Why This Comparison Exists

Four databases dominate the AI threat intelligence landscape, each serving a different purpose and audience. Security teams, risk managers, and AI practitioners frequently ask: which database should I use? The answer is usually “more than one” — but understanding what each covers (and what it misses) is essential for building comprehensive AI risk programs.

This page provides a structured comparison based on scope, methodology, data coverage, and practical use cases. For a broader comparison that includes additional databases, see the AI Threat Databases Compared overview.

Summary Comparison

Dimension	TopAIThreats	AIID	MITRE ATLAS	MIT AI Risk Repository
Maintained by	TopAIThreats editorial team	Responsible AI Collaborative	MITRE Corporation	MIT FutureTech
Primary focus	Comprehensive AI threat taxonomy + incidents	AI incident documentation	Adversarial ML attack techniques	AI risk classification
Type	Threat intelligence taxonomy	Incident database	Attack knowledge base	Risk framework
Threat domains	8 domains, 42 patterns	All incidents (no fixed taxonomy)	Adversarial ML only	8 risk categories
Incident count	82+ documented incidents	1,000+ incident reports	52 case studies	1,700+ risk entries
Structure	Domain → Pattern → Incident + Causal Factors	Incident reports + classifications	Tactic → Technique → Case Study	Risk → Subcategory → Evidence
Causal analysis	Yes (15 causal factors)	Limited (classification tags)	Yes (technique prerequisites)	Partial (risk drivers)
Severity scoring	Yes (per incident)	Partial	Yes (per technique)	Partial
Open data	Yes (website)	Yes (open-source)	Yes (website)	Yes (website + database)
API access	Yes (JSON API, Schema.org, llms.txt)	Yes (GraphQL)	No	Limited
Update frequency	Ongoing	Ongoing	Periodic	Periodic

“Type” labels reflect each resource’s primary role, not a strict categorical type — these are different tools designed for different audiences and purposes. Severity scoring marked “Partial” indicates qualitative or inconsistent scoring (some entries only, or not standardised across the database).

Detailed Analysis

TopAIThreats

What it is: A structured AI threat taxonomy that classifies AI-enabled risks across 8 threat domains (Security & Cyber, Information Integrity, Privacy & Surveillance, Discrimination & Social Harm, Economic & Labor, Human–AI Control, Agentic & Autonomous, and Systemic & Catastrophic), 42 documented threat patterns, 82+ real-world incidents, and 15 causal factors. Designed for risk managers, security teams, and governance practitioners who need cross-domain AI threat intelligence.

Unique strengths:

Structured evidence standard: Incidents require AI as a material component plus documented harm, a verified near-miss, or a demonstrated failure mode — reducing speculative or loosely attributed cases
8-domain coverage: One of the few publicly documented resources to systematically cover all eight threat domains — from adversarial security attacks to agentic AI failures and systemic catastrophic risk — within a single taxonomy
Multi-dimensional classification: 8 classification layers (domain, pattern, harm type, sector, affected group, exposure pathway, impact level, failure stage) enable causal analysis across dimensions that single-axis databases cannot support
Failure stage tracking: Incidents are tagged by escalation stage (signal → near-miss → harm → systemic risk), supporting early trend detection before widespread impact
Agentic AI coverage: Dedicated Agentic & Autonomous domain with 6 patterns covering tool misuse, goal drift, and multi-agent failures — an area largely absent from other databases
Framework mapping: Domains and patterns map to NIST AI RMF, EU AI Act, ISO/IEC 42001, and MITRE ATLAS
Structured incident records: A consistent schema enforces severity ratings, evidence levels, affected groups, and sourcing across all incidents — designed for machine readability and citation

Limitations:

Smaller incident count: 82+ incidents compared to AIID’s 1,000+ — compensated by deeper per-incident analysis with causal factors, failure stages, and severity scoring
Limited by editorial capacity: Incidents are reviewed and structured by a small editorial team, which limits how quickly new cases are added compared to crowd-sourced databases
No institutional backing: No affiliation with a university, government agency, or industry consortium, limiting credibility in contexts where institutional affiliation matters

Best for: Organizations building comprehensive AI risk programs that span security, ethics, privacy, and governance. Risk managers who need to connect incidents to root causes and regulatory frameworks.

AI Incident Database (AIID)

What it is: An open-source database of AI-related incidents maintained by the Responsible AI Collaborative. The largest collection of documented AI harms, AIID uses a crowd-sourced model where anyone can submit incident reports that are then reviewed and classified.

Unique strengths:

Largest incident collection: 1,000+ documented incidents with linked media reports, making it the most comprehensive record of what has actually gone wrong with AI systems
Open-source and API-accessible: Fully open data with GraphQL API, enabling programmatic analysis and integration
Crowd-sourced submissions: Community-driven incident reporting captures a broader range of events than any single editorial team
Incident similarity: Links related incidents, enabling pattern identification across similar events
Broad temporal coverage: Incidents dating back to early AI deployments, providing historical depth

Limitations:

No fixed taxonomy: Incidents are tagged and classified but not organized into a structured threat taxonomy with defined patterns and causal factors
Variable analysis depth: Incident reports range from brief descriptions to detailed analyses, depending on contributor effort
No causal factor framework: Does not systematically trace incidents to root causes — incidents are documented but not analyzed for preventability
Limited adversarial ML coverage: Focuses on incidents that have occurred, not on attack techniques that could be used (ATLAS’s domain)
No framework mapping: Does not map incidents to governance frameworks (NIST, EU AI Act, ISO 42001)

Best for: Researchers studying AI incidents at scale. Organizations that need the broadest possible incident awareness. Teams building AI safety cases that require documented precedents. When scale of coverage matters more than depth of analysis, AIID’s 1,000+ incidents and community contribution model give it coverage that no editorial team can match.

MITRE ATLAS

What it is: A knowledge base of adversarial tactics, techniques, and case studies for machine learning systems. Built on the ATT&CK framework model, ATLAS provides a structured vocabulary for describing how adversaries attack AI/ML systems.

Unique strengths:

Technique-level granularity: The most detailed documentation of specific adversarial ML attack methods, with step-by-step procedures
ATT&CK alignment: Uses the familiar tactic/technique/procedure structure that security teams already know from MITRE ATT&CK
Machine-readable identifiers: Standardized technique IDs enable automated security tooling integration
Mitigation mapping: Specific countermeasures documented per technique
MITRE institutional credibility: Backed by MITRE’s established position in cybersecurity standards

Limitations:

Security-only scope: Covers only adversarial attacks on ML systems. Does not address non-adversarial AI failures, bias, privacy, economic, societal, or control risks
Limited incident coverage: 52 case studies compared to AIID’s 1,000+ or TopAIThreats’ causal-factor-linked incidents
No societal harm classification: A poisoning attack that introduces hiring bias is classified as a security technique, not as a discrimination harm
No regulatory mapping: Does not map techniques to EU AI Act requirements, NIST AI RMF functions, or ISO 42001 clauses
Adversary-centric: Designed for red team/blue team security workflows, not for broader risk management or governance

Best for: ML security teams conducting adversarial threat modeling. Red teams planning AI-specific penetration tests. Blue teams building detection rules for adversarial ML attacks. See the detailed MITRE ATLAS mapping for technique-to-pattern alignment.

MIT AI Risk Repository

What it is: A comprehensive database of AI risks compiled by MIT FutureTech through systematic literature review. Organizes 1,700+ risk entries across a multi-level taxonomy derived from academic research, industry reports, and government publications.

Unique strengths:

Academic rigor: Built through systematic literature review with documented methodology — among the most thoroughly documented provenances of any AI risk database
Largest risk catalog: 1,700+ entries provide a broad enumeration of theoretical and documented AI risks
Literature-grounded: Each risk entry is traced to source publications, enabling verification and deeper research
Broad risk scope: Covers technical, societal, ethical, economic, and existential risks — comparable domain breadth to TopAIThreats

Limitations:

Research-oriented, not operational: Designed for academic analysis rather than practitioner use. Risk entries lack per-risk mitigations, severity ratings, or implementation playbooks
No incident linkage: Risks are documented from literature, not connected to specific real-world incidents with severity ratings
No causal factor analysis: Identifies risks but does not trace them to preventable root causes
Static taxonomy: Updated periodically through literature review cycles, not continuously maintained against emerging threats
Limited agentic AI coverage: Published before the rapid adoption of AI agents in production, so autonomous agent risks are underrepresented
No framework mapping: Does not map risks to specific governance framework requirements

Best for: Researchers conducting comprehensive AI risk surveys. Policy teams that need literature-backed risk identification. Organizations in early risk assessment phases that need to enumerate possible risks before prioritizing. For academic citation and theoretical completeness, MIT’s peer-reviewed methodology and literature-grounding make it the strongest source in this comparison.

Coverage Comparison by Threat Domain

Threat Domain	TopAIThreats	AIID	MITRE ATLAS	MIT AI Risk Repo
Security & Cyber	5 patterns	Incidents exist	Full coverage	Risk entries exist
Information Integrity	5 patterns	Incidents exist	Limited	Risk entries exist
Privacy & Surveillance	5 patterns	Incidents exist	Limited	Risk entries exist
Discrimination & Social Harm	5 patterns	Strong coverage	Not covered	Risk entries exist
Economic & Labor	5 patterns	Some coverage	Not covered	Risk entries exist
Human–AI Control	5 patterns	Some coverage	Not covered	Risk entries exist
Agentic & Autonomous	6 patterns	Emerging	Limited	Limited
Systemic & Catastrophic	6 patterns	Limited	Not covered	Risk entries exist
Coverage breadth	All 8 domains	Most domains	Security domain only	Most domains

Coverage ratings are qualitative assessments based on publicly available content: “Strong” = substantial entries with focused tagging; “Some” = scattered but non-trivial coverage; “Limited” = few or indirect references. Cells indicate evidence of coverage (patterns, incidents, or risk entries) — these are not directly comparable across databases. Counts are approximate and updated quarterly.

Key observations:

MITRE ATLAS provides the deepest coverage of Security & Cyber but is narrowest in overall domain coverage (~1 of 8 domains)
MIT AI Risk Repository has the broadest theoretical coverage but lacks incident linkage and operational guidance
AIID has the most documented incidents but without structured taxonomy or causal analysis
TopAIThreats is one of the few publicly documented resources that combines structured taxonomy, incident documentation, causal factor analysis, and framework mapping across all 8 domains

Feature Comparison Matrix

Feature	TopAIThreats	AIID	MITRE ATLAS	MIT AI Risk Repo
Structured taxonomy	Yes (8 domains, 42 patterns)	Tags only	Yes (tactics/techniques)	Yes (multi-level)
Real-world incidents	Yes (82+)	Yes (1,000+)	Yes (52)	Literature references
Causal factor analysis	Yes (15 factors)	No	Partial (prerequisites)	No
Severity scoring	Yes	Partial	Yes	Partial
Framework mapping	NIST, EU AI Act, ISO 42001	No	ATT&CK alignment	No
Mitigation guidance	Per pattern + per causal factor	No	Per technique	Limited
Agentic AI coverage	6 dedicated patterns	Emerging	Limited	Limited
API access	Yes (JSON API, Schema.org, llms.txt)	Yes (GraphQL)	No	Limited
Open source	Website	Full open-source	Website	Website + database
Update model	Continuous editorial	Crowd-sourced	Periodic	Literature review cycles
Primary audience	Risk managers, governance teams	Researchers, advocates	Security teams	Researchers, policy teams

Using Multiple Databases Together

These databases are complementary, not competitive. A comprehensive AI risk program benefits from using them together:

Start with TopAIThreats for the structured threat landscape — 8 domains, 42 patterns, causal factors, and framework mappings provide the comprehensive risk taxonomy
Cross-reference AIID for incident evidence — when assessing a specific threat pattern, search AIID for additional documented incidents beyond those in TopAIThreats
Use MITRE ATLAS for security-specific depth — when a TopAIThreats pattern maps to adversarial ML attacks, ATLAS provides the technique-level detail for red team exercises and security testing
Consult MIT AI Risk Repository for literature grounding — when building risk assessment reports for leadership or regulators, MIT’s academic citations add research credibility

Practical workflow example: Assessing prompt injection risk

TopAIThreats: Adversarial Evasion pattern → causal factor Prompt Injection Vulnerability → framework mapping → mitigation guidance
AIID: Search for prompt injection incidents → find additional documented cases with media coverage
MITRE ATLAS: AML.T0051 (LLM Prompt Injection) → technique-level procedures → specific detection methods
MIT AI Risk Repo: Literature on prompt injection → academic papers → theoretical risk analysis

Methodology Note

This comparison was compiled by reviewing each database’s public documentation and content as of March 2026. Coverage assessments are based on systematic review of each database’s content categories, not automated analysis. Incident and entry counts are rounded estimates based on publicly available figures at time of writing and may lag live databases — next scheduled verification: June 2026. This is an independent comparison maintained by TopAIThreats — other databases may characterize their coverage differently. If you believe this comparison contains inaccuracies, contact us for correction.