Record summary
A quick snapshot of what this page covers.
Risk profile
How this risk is described and categorized.
"Model weights or access to them can be leaked when initial access is granted only to a select group of individuals, such as institutional researchers [209]. This risk can increase as more people gain access, and identifying the source of the leak becomes more difficult. The availability of leaked model weights makes various attacks on systems that use the leaked AI model easier to implement, such as finding adversarial examples, elicitation of dangerous capabilities, and extraction of confidential information present in the training data. The avail- ability of model weights might also enable the misuse of the AI system using the leaked model to produce harmful or illegal content [67]."
Suggested mitigations
Defenses that may help with related attacks.
Control Access to AI Models and Data at Rest
Encrypt Sensitive Information
Control Access to AI Models and Data in Production
Generative AI Guardrails
Generative AI Guidelines
Generative AI Model Alignment
AI Telemetry Logging
Input and Output Validation for AI Agent Components
Model Hardening
Use Ensemble Methods
Use Multi-Modal Sensors
Input Restoration
Adversarial Input Detection
Deepfake Detection
Source
Research source for this risk, when available.
Included resource
Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems
Original source
MIT AI Risk Repository
Open the public repository used for AI risk records and taxonomy fields.
