MR-056 Model & system behaviour System scope

Lack of robustness to distribution shift and edge cases

The system fails on out-of-distribution, noisy, adversarial, or edge-case inputs, or is brittle to minor input changes.

Risk family: Model & system behaviour
MIT domain: 7. AI System Safety, Failures, & Limitations
MIT subdomain: 7.3 > Lack of capability or robustness
AI type: GPAI, Classical_ML, Agentic
Scope: System
Source standard: MIT AI Risk Repository v4

Provenance

Source standard

MIT AI Risk Repository v4

MIT source entries

20 entries across 11 papers

AIVerify2023Summary Report: Binary Classification Model for Credit Risk26.06.00
Gipiškis2024Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems62.04.02 62.15.00 62.16.05 62.19.08 62.19.11
IBM2025AI Risk Atlas65.08.00 65.09.00 65.18.00
InfoComm2023Cataloguing LLM Evaluations43.01.05
Saghiri2022A Survey of Artificial Intelligence Challenges: Analyzing the Definitions, Relationships, and Evolutions39.04.00
Schnitzer2024AI Hazard Management: A Framework for the Systematic Management of Root Causes for AI Risks59.02.00 59.19.00 59.20.00 59.26.01
Sharma2024Benefits or Concerns of AI: A Multistakeholder Responsibility36.01.00
Sherman2023AI Risk Profiles: A Standards Proposal for Pre-Deployment AI Risk Disclosures12.07.00
Tan2022The Risks of Machine Learning Systems15.01.05
TC2602024AI Safety Governance Framework45.01.03
Zhang2022Towards risk-aware artificial intelligence and machine learning systems: An overview21.01.03

Ev IDs of the entries consolidated into this risk in the MIT AI Risk Repository (V4); the source sheet row appears on hover.

ISO/IEC references

23894 obj A.9; src 8; mech B.2 | 42001 ctrl A.6.2.4

EU AI Act articles

Art. 15

Framework crosswalk

Every framework item mapped to this risk. Items marked partial overlap only in part; definitions appear on hover where the source licence permits.

Sourcesframeworks that contributed to the register

A.9 ISO/IEC 23894 Annex A A.9

A.6.2.4 ISO/IEC 42001 Annex A A.6.2.4

Art. 15

Cross-checksframeworks mapped in to test coverage

ibm-overfitting Overfitting partial

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Part of the Deployer AI Risk Register, an open-source resource powered by MindXO. Version 1.0, 3 July 2026. Derived from the MIT AI Risk Repository (V4, December 2025) under CC BY 4.0; an independent derivative work, not endorsed by or affiliated with MIT. Sub-risk decomposition references MITRE ATLAS™ v5.6.0 (© 2021-2026 The MITRE Corporation, reproduced and distributed with permission). ISO/IEC and EU AI Act references are by number only. License: CC BY 4.0. Full attribution and licensing.