arXiv:2601.22710v2 Announce Type: replace-cross Abstract: Modern LLMs are increasingly accessed via black-box APIs, requiring users to transmit sensitive prompts, outputs, and fine-tuning data to external providers, creating a critical privacy risk at the API boundary. We introduce AlienLM, a deployable API-only \cradd{exposure-reduction layer that reduces plaintext exposure} by translating text into an Alien Language via a vocabulary-scale bijection, enabling lossless recovery on the client side. Using only standard fine-tuning APIs, Alien Adaptation Training (AAT) adapts target models to ope
The increasing reliance on black-box LLM APIs for sensitive data transmission necessitates new privacy-preserving methods as user concerns about data exposure grow.
Sophisticated readers should care because this technology offers a practical solution to a critical privacy and security vulnerability within the burgeoning AI ecosystem.
This technology enables the transmission and processing of sensitive data by black-box LLMs without exposing plaintext information to external providers, fundamentally altering API boundary privacy.
- · Enterprises using LLMs with sensitive data
- · Cloud AI providers offering privacy-focused solutions
- · Privacy-enhancing technology developers
- · Data brokers relying on unprotected API communication
- · Malicious actors intercepting API traffic
Immediate adoption of 'alienization' techniques will enhance data privacy for LLM users.
Increased trust in LLM API services could accelerate the adoption of AI in highly regulated industries.
This could set a new standard for 'privacy by design' in black-box AI services, influencing future regulatory frameworks and competitive landscapes.
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Read at arXiv cs.CL