Record summary
A quick snapshot of what this page covers.
Risk profile
How this risk is described and categorized.
While AI safety focuses on threats emanating from generative AI systems, security centers on threats posed to these systems. The most extensively discussed issue in this context are jailbreaking risks, which involve techniques like prompt injection or visual adversarial examples designed to circumvent safety guardrails governing model behavior. Sources delve into various jailbreaking methods, such as role play or reverse exposure. Similarly, implementing backdoors or using model poisoning techniques bypass safety guardrails as well. Other security concerns pertain to model or prompt thefts.
Suggested mitigations
Defenses that may help with related attacks.
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
Privileged AI Agent Permissions Configuration
Single-User AI Agent Permissions Configuration
AI Agent Tools Permissions Configuration
Human In-the-Loop for AI Agent Actions
Restrict AI Agent Tool Invocation on Untrusted Data
Segmentation of AI Agent Components
Memory Hardening
Limit Model Artifact Release
Control Access to AI Models and Data at Rest
Sanitize Training Data
Validate AI Model
AI Bill of Materials
Maintain AI Dataset Provenance
Code Signing
Verify AI Artifacts
Model Hardening
Use Ensemble Methods
Input Restoration
Adversarial Input Detection
Source
Research source for this risk, when available.
Included resource
Mapping the Ethics of Generative AI: A Comprehensive Scoping Review
Original source
MIT AI Risk Repository
Open the public repository used for AI risk records and taxonomy fields.