Record summary
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Risk profile
How this risk is described and categorized.
"Various new or existing text encodings, such as Base64, can be employed to craft jailbreak attacks that bypass safety training [13]. Low-resource language inputs also appear more likely to circumvent a model’s safeguards [229]. Since safety fine-tuning might not involve this encoding data or may only do so to a limited extent, harmful natural language prompts could be translated into less frequently used encodings [214]."
Suggested mitigations
Defenses that may help with related attacks.
Control Access to AI Models and Data in Production
AI Telemetry Logging
Generative AI Guardrails
Generative AI Guidelines
Generative AI Model Alignment
Control Access to AI Models and Data at Rest
Sanitize Training Data
Validate AI Model
Code Signing
Maintain AI Dataset Provenance
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.