MIT AI Risk Repository · Risk Category · 74.01.00

Inherent Risk

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Description

"In terms of inherent risk, LLMs could potentially reveal sensitive information from their utilized corpora for pre-training or fine-tuning, thereby raising issues of privacy leakage [37, 145, 226]. Meanwhile, it is well-known that LLMs may experi- ence hallucinations, resulting in the production of texts that are inaccurate and misleading [194]. Finally, since the values embedded in LLM-generated texts usually directly reflect the distribution of their training data, often sourced from the Internet, there exists a substantial risk that LLMs will overfit to a narrow set of human values or even

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: 7. AI system safety, failures, & limitations
Subdomain: —
Causal entity: AI
Intent: Unintentional
Timing: Other

Other entries from Wang2025

Privacy - Membership Inference Attack (MIA) Risk Sub-Category · 2.2
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 Risk Sub-Category · 3.1
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