MIT AI Risk Repository · Risk Sub-Category · 74.01.06

Hallucination

Category: Inherent Risk

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Description

"Despite the rapid advancement of LLMs, hallucinations have emerged as one of the most vital concerns surrounding their use [54, 79, 86, 110, 242]. Hallucinations are often referred to as LLMs’ generating content that is nonfactual or unfaithful to the provided information [54, 79, 86, 242]. Therefore, hallucinations can be typically categorized into two main classes. The first is factuality hallucination, which describes the discrepancy between LLMs’ generated content and real-world facts. For example, if LLMs mistakenly take Charles Lindbergh as the first person who walked on the moon, it is

From A Survey on Responsible LLMs: Inherent Risk, Malicious Use, and Mitigation Strategy (Wang2025), as extracted by the MIT AI Risk Repository (CC BY 4.0).

Classification

Domain: 3. Misinformation
Subdomain: 3.1 False or misleading information
Causal entity: AI
Intent: Unintentional
Timing: Post-deployment

Subdomain definition: AI systems that inadvertently generate or spread incorrect or deceptive information, which can lead to inaccurate beliefs in users and undermine their autonomy. Humans that make decisions based on false beliefs can experience physical, emotional or material harms

Real-world incidents in this subdomain

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How other frameworks describe this risk

Pursuing Consistent Context
Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)
Knowledge Gaps
Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)
Hallucinations
Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)
Faithfulness Errors
Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)
Defective Decoding Process
Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)
Untruthful Content
Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)
Factuality Errors
Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)
Noisy Training Data
Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

Other entries from Wang2025

Inherent Risk Risk Category
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Privacy - Data Extraction Attack (DEA) Risk Sub-Category · 2.2
Privacy - Prompt Inversion Attack (PIA) Risk Sub-Category · 2.2
Privacy - Attribute Inference Attack (AIA) Risk Sub-Category · 2.2
Privacy - Model Extraction Attack (MEA) Risk Sub-Category · 2.2
Hallucination Additional evidence
Value-related risks in LLMs Risk Sub-Category · 7.1
Value-related risks in LLMs Additional evidence
Malicious Use Risk Category
Toxicity in LLM Malicious Use Risk Sub-Category · 1.2
Toxicity in LLM Malicious Use Additional evidence