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AI x Crypto

Why does attesting my LLM inference still let the operator infer what I asked?

80

機会

Trusted Execution Environments can now prove a specific model ran without modification, which is real progress on integrity. But attestation proves integrity, not confidentiality. During transformer inference the memory access patterns, cache timing, and PCIe bus traffic between the CPU and GPU create a measurable side channel that leaks information about the prompt even inside an attested enclave. September 2026 research demonstrates verbatim token leakage through this channel on H100 configurations. Splitting the model across a trusted CPU and an untrusted GPU, which is required at production scale, enlarges the attack surface in ways that current enclave designs do not address.

重要な理由

Side-channel hardening is the missing half of verifiable AI compute because attestation proves integrity but leaves prompt confidentiality undefended.

機会をどう評価するか

Opportunity Scoreは測定値ではなく、私自身の見解です。どれほど痛みを伴うか、どれほど頻繁に影響を与えるか、そして今日時点で解決策がいかに少ないか。スコアが高いほど、構築する価値が高いと私は考えています。

深刻度7/10

それが現れたときにどれほどの痛みをもたらすか。

頻度6/10

実際にどれほど頻繁に人々がそれに直面するか。

ホワイトスペース9/10

今日時点で、それに対する優れたツールがいかに少ないか。

解決する価値のある問題をもっと見る