Record summary
A quick snapshot of what this page covers.
Risk profile
How this risk is described and categorized.
"LM predictions that convey true information may give rise to information hazards, whereby the dissemination of private or sensitive information can cause harm [27]. Information hazards can cause harm at the point of use, even with no mistake of the technology user. For example, revealing trade secrets can damage a business, revealing a health diagnosis can cause emotional distress, and revealing private data can violate a person’s rights. Information hazards arise from the LM providing private data or sensitive information that is present in, or can be inferred from, training data. Observed risks include privacy violations [34]. Mitigation strategies include algorithmic solutions and responsible model release strategies."
Suggested mitigations
Defenses that may help with related attacks.
Control Access to AI Models and Data at Rest
Encrypt Sensitive Information
AI Model Distribution Methods
Restrict Library Loading
Code Signing
Vulnerability Scanning
User Training
AI Bill of Materials
Source
Research source for this risk, when available.
Included resource
Taxonomy of Risks posed by Language Models
Original source
MIT AI Risk Repository
Open the public repository used for AI risk records and taxonomy fields.
