Sensitive information leakage through model outputs

https://taxonomy.eticas.ai/risk/sensitive-information-leakage

Maturity: established

Personal or confidential/proprietary information present in training data, inference-time context, or connected systems, surfacing in model outputs through memorisation, context disclosure, cross-tenant contamination, or system-prompt extraction. Covers personal data and organisational or commercial confidentiality alike.

Also known as: PII leakage rate · Privacy leakage

System type: ADM and LLM systems
Lifecycle stages: In Processing, Post Processing

Mappings to external frameworks

Status. Each mapping shows what it rests on. Reviewed means a reviewer has confirmed the mapping against the concept’s current definition and the framework edition the taxonomy currently references; when the only confirmation is that of the person who wrote the mapping, it reads Reviewed (author’s confirmation). Outdated means it was reviewed, but the concept’s definition has changed since, so the review has to be repeated. The other statuses mean that no review has been recorded yet. Inherited mappings have been in the taxonomy since before v5.7.0 and are unchanged. They carry no record of who made them or how, and some were checked by hand when the taxonomy first published its mappings. Agent-proposed mappings come from a review carried out by AI agents, which read the framework’s text and the concept’s definition and agreed on the relation; no person has confirmed them yet. Provisional mappings were made without access to the framework’s text. Unreviewed mappings are older mappings whose target or relation was revised in v5.7.0, and any other mapping without a review. Mappings to a superseded edition of a framework are not shown: where the provision continues, they are carried to the current edition. Mappings a review has changed or rejected are not shown.

Standards & frameworks

Framework Reference Status
EU AI Act (Regulation 2024/1689) Article 15(5) — resilience against attempts by unauthorized third parties to alter use, outputs or performance Agent-proposed
AIUC-1 — AI Underwriting Company Standard Prevent PII leakage Inherited
NIST AI 600-1 — Generative AI Risk Profile Data Privacy Inherited
TC260 AI Safety Governance Framework (v2.0) §3.1.2(d) Data and personal information leakage Agent-proposed

Taxonomies & vocabularies

Framework Reference Status
MIT AI Risk Repository Compromise of privacy by obtaining, leaking or correctly inferring sensitive information Agent-proposed
AIR 2024 Unauthorized disclosure Agent-proposed
W3C Data Privacy Vocabulary — AI Extension Unauthorised Data Disclosure Inherited

Source: HRA project taxonomy