Model Safety
A research direction in AI aimed at ensuring that the behavior of AI systems conforms to expectations, that they do not produce harmful outputs, and that they can be audited and repaired.
Timeline (last 90 days)
The primary reason Coinbase trusts OpenAI is that the latter has made deep investments in model safety and has found an appropriate balance point in guardrail mechanisms.
When discussing model security, a reproducible experiment, auditable code, and a complete packet capture record are far more convincing than a deliberately constructed system prompt.
containment focuses on who touches the infrastructure, while traditional model safety (such as alignment and red teaming) focuses on what the model "does"; the two are at different levels and both are indispensable.
LLM agent security focuses on what an LLM system can actually do once it is given tools, credentials and data, which is a different matter from model safety (managing what the model "says").
All Facts (4)
The primary reason Coinbase trusts OpenAI is that the latter has made deep investments in model safety and has found an appropriate balance point in guardrail mechanisms.
50%UnverifiedWhen discussing model security, a reproducible experiment, auditable code, and a complete packet capture record are far more convincing than a deliberately constructed system prompt.
50%Unverifiedcontainment focuses on who touches the infrastructure, while traditional model safety (such as alignment and red teaming) focuses on what the model "does"; the two are at different levels and both are indispensable.
50%UnverifiedLLM agent security focuses on what an LLM system can actually do once it is given tools, credentials and data, which is a different matter from model safety (managing what the model "says").
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