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AI Risk

Association in LLMs

"Association in LLMs refers to the capability to associate various pieces of information related to a person. According to [68], [86], given a pair of PII entities (xi , xj ), which is associated by a model F. Using a prompt p could force the model F to produce the entity xj , where p is the prompt related to the entity xi . For instance, an LLM could accurately output the answer when given the prompt “The email a...

AI Risk2. Privacy & Security2.1 > Compromise of privacy by leaking or correctly inferring sensitive information1 - Pre-deployment

Record summary

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Techniques2Attack methods connected to this risk.
Mitigations0Defenses that may help with related attacks.
Domain2. Privacy & SecurityThe broad risk area this belongs to.

Risk profile

How this risk is described and categorized.

"Association in LLMs refers to the capability to associate various pieces of information related to a person. According to [68], [86], given a pair of PII entities (xi , xj ), which is associated by a model F. Using a prompt p could force the model F to produce the entity xj , where p is the prompt related to the entity xi . For instance, an LLM could accurately output the answer when given the prompt “The email address of Alice is”, if the LLM associates Alice with her email “alice@email.com”. L"

Domain2. Privacy & Security
Subdomain2.1 > Compromise of privacy by leaking or correctly inferring sensitive information
Entity2 - AI
Intent2 - Unintentional
Timing1 - Pre-deployment
CategoryPrivacy Leakage
SubcategoryAssociation in LLMs

Suggested mitigations

Defenses that may help with related attacks.

No propagated mitigations. No defense is available through the connected attack methods.

Source

Research source for this risk, when available.