Record summary
A quick snapshot of what this page covers.
Risk profile
How this risk is described and categorized.
"As recent LLMs continue to incorporate licensed, created, and publicly available data sources in their corpora, the potential to mix private data in the training corpora is significantly increased. The misused private data, also named as personally identifiable information (PII) [84], [86], could contain various types of sensitive data subjects, including an individual person’s name, email, phone number, address, education, and career. Generally, injecting PII into LLMs mainly occurs in two settings — the exploitation of web-collection data and the alignment with personal humanmachine conversations [87]. Specifically, the web-collection data can be crawled from online sources with sensitive PII, and the personal human-machine conversations could be collected for SFT and RLHF"
Suggested mitigations
Defenses that may help with related attacks.
Control Access to AI Models and Data at Rest
Sanitize Training Data
Verify AI Artifacts
Maintain AI Dataset Provenance
Source
Research source for this risk, when available.
Included resource
Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems
Original source
MIT AI Risk Repository
Open the public repository used for AI risk records and taxonomy fields.
