AI Aabo

O pẹlu aabo sọfitiwia arinrin ati awọn irokeke ti o fojusi ẹkọ tabi ihuwasi awoṣe. Apẹrẹ ti o ni aabo bẹrẹ pẹlu awọn ohun-ini, awọn ọta, ati awọn aala igbẹkẹle ti ohun elo gangan.

Awọn gbigba bọtini

• Irokeke-awoṣe awọn ohun elo ni kikun.
• Gba awọn igbanilaaye ni ita awoṣe.
• Ṣe idanwo awọn idari kọja awọn ayipada eto.

Identify what needs protection: private inputs, training data, model artifacts, credentials, connected accounts, and external actions. Record who can influence each input and what an attacker could gain from a failure. A public chatbot and an internal agent with write access have different threat models. Threats can affect different stages. Poisoned training material can alter learned behavior; adversarial inputs can manipulate predictions; untrusted retrieved content can redirect a tool-using application. Model output can also become dangerous when insert into a database query, webpage, or command without appropriate handling. Apply controls at the software boundary. Enforce authorization in code, keep secrets out of model-visible context where possible, restrict tool scope, and validate outputs before use. A prompt asking a model to behave safely cannot replace account isolation or permission checks. Test representative failure paths in an authorized environment and maintain an incident process. Log enough information to investigate without collecting unnecessary sensitive content. Ṣe ayẹwo awọn iṣakoso lẹhin awọn ayipada si awoṣe, awọn orisun gbigba, awọn irinṣẹ, ati awọn igbẹkẹle. Ṣe apejuwe eewu ti o ku ni otitọ; ko si àlẹmọ kan ṣoṣo ti o fi idi aabo pipe mulẹ.

1. Foju inu wo oluranlọwọ kan ti n wa ile itaja iwe ikọkọ fun olumulo ti o wọle.
2. Lo àlẹmọ wiwọle olumulo ni iṣẹ gbigba ṣaaju ki awọn iwe aṣẹ wọ ipo awoṣe.
3. Ṣe idanwo pẹlu iwe ti o jẹ ti akọọlẹ miiran ki o rii daju pe bẹni awọn akoonu rẹ tabi metadata idanimọ han ninu abajade.

Idanwo yii ti o ni imọran ṣe ayẹwo igbanilaaye laisi ifẹ awoṣe lati tẹle awọn itọnisọna.