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MIT AI Risk Repository · Risk Sub-Category · 49.03.05

Risks to privacy

Category: Systemic Risks

Open the MIT Risk Navigator All entries from Bengio2024

Description

"General- purpose AI models or systems can ‘leak’ information about individuals whose data was used in training. For future models trained on sensitive personal data like health or financial data, this may lead to particularly serious privacy leaks. General- purpose AI models could enhance privacy abuse. For instance, Large Language Models might facilitate more efficient and effective search for sensitive data (for example, on internet text or in breached data leaks), and also enable users to infer sensitive information about individuals."

From International Scientific Report on the Safety of Advanced AI (Bengio2024), as extracted by the MIT AI Risk Repository (CC BY 4.0).

Classification

Causal entity
AI

Subdomain definition: AI systems that memorize and leak sensitive personal data or infer private information about individuals without their consent. Unexpected or unauthorized sharing of data and information can compromise user expectation of privacy, assist identity theft, or loss of confidential intellectual property.

Real-world incidents in this subdomain

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How other frameworks describe this risk

  • Risks to privacy

    International AI Safety Report 2025 (Bengio2025)

  • Privacy Leakage

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  • Private Training Data

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  • Memorization in LLMs

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  • Association in LLMs

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  • Privacy Leakage

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  • Privacy and regulation violations

    Navigating the Landscape of AI Ethics and Responsibility (Cunha2023)

  • Privacy and Data Leakage

    Towards Safer Generative Language Models: A Survey on Safety Risks, Evaluations, and Improvements (Deng2023)

Other entries from Bengio2024