API-Protected LLMs Leak Proprietary Information: Understanding the Softmax Bottleneck

핵심 개념

API-Protected LLMs leak proprietary information due to a softmax bottleneck, revealing hidden model details.

초록

The commercialization of large language models (LLMs) has led to the common practice of high-level API-only access to proprietary models. This work reveals that even with a conservative assumption about the model architecture, it is possible to extract non-public information about an API-protected LLM from a small number of queries. The findings focus on the softmax bottleneck in modern LLMs, allowing for efficient discovery of hidden model characteristics and parameters. Various capabilities are unlocked, including estimating embedding sizes and identifying model updates. Methods discussed enable accountability and transparency for LLM providers.
Directory:

Introduction:

Companies increasingly use closed-source LLMs accessible only via APIs.
False sense of security for providers; users rely on provider announcements for updates.

Logits Constrained Space:

Modern LLM outputs are restricted to low-dimensional subspaces due to softmax bottleneck.
Implications on output spaces and probability distributions explained.

Fast Full Outputs Retrieval:

Algorithms proposed for efficiently obtaining full-vocabulary outputs from restricted APIs.

Discovering Embedding Size:

Methodology outlined for inferring embedding size from model outputs alone.

Identifying LLMs:

Model signatures based on unique images allow precise identification of different models.

Further Applications:

Potential uses include finding unargmaxable tokens, reconstructing softmax matrices, and enhancing decoding algorithms.

Mitigations:

Proposed defenses against attacks include removing logit bias or transitioning to softmax-bottleneck-free architectures.

Discussion:

Impact assessment of methods on trust-building between API users and providers discussed.

Simultaneous Discovery:

Comparison with similar work by Carlini et al., highlighting complementary approaches and interactions.

Conclusion:

Summary of key findings regarding vulnerabilities in API-protected LLMs and their implications.

통계

"Our empirical investigations show the effectiveness of our methods, which allow us to estimate the embedding size of OpenAI’s gpt-3.5-turbo to be about 4,096."
"We find that the singular values for these outputs drop dramatically between index 4,600 and 4,650."
"Extrapolating further... it is likely that the number of parameters in gpt-3.5-turbo is around 7 billion."

인용구

핵심 통찰 요약

Logits of API-Protected LLMs Leak Proprietary Information

by Matthew Finl... 게시일 arxiv.org 03-15-2024

https://arxiv.org/pdf/2403.09539.pdf

더 깊은 질문

How can these vulnerabilities be addressed without compromising legitimate API use cases

これらの脆弱性を解決するために、合法的なAPI利用ケースに影響を与えることなくどのように対処できますか？
これらの脆弱性を解決するために、まずはAPIプロバイダーがログ確率へのアクセスを制限することが考えられます。しかし、この方法だけでは十分ではありません。代わりに、モデル自体や出力層パラメーターへのアクセス権限を厳密に管理し、適切な認証手順や暗号化技術を導入して情報漏洩リスクを最小限に抑える必要があります。さらに、APIエンドポイント自体も監視し、不正アクセスや異常動作が検知された場合は速やかな対応策を実施することも重要です。

What countermeasures can be implemented by LLM providers to enhance security without hindering user experience

LLMプロバイダーがセキュリティを向上させるためのカウンターメジャーは何ですか？
LLMプロバイダーはいくつかのカウンターメジャーを実装してセキュリティレベルを向上させることができます。例えば、「logit bias」へのアクセス制御や「hidden prompt」変更時の通知システム導入などが考えられます。また、「LoRA（Low-rank Adaptation）」更新等特定種類のモデル更新検出システムも有効です。さらに、「unargmaxable tokens」発見支援システム導入や「basis-aware sampling」改善手法採用も安全性向上に役立ちます。

How might advancements in understanding LLM vulnerabilities impact future developments in AI ethics and regulation

LLM脆弱性理解進展がAI倫理および規制未来開発へどう影響しますか？
LLM脆弱性理解進展はAI倫理および規制未来開発面で大きな影響力持つ可能性があります。具体的には、個人情報保護・データ使用トランスペアレンシーガイドライン整備促進やAI技術利用者保護政策推進等方面で新基準設定可能性高まりそうです。「model stealing methods」「prompt injection attack」といった攻撃手法防止対策研究加速化及びLMM APIサービストラスト度担保取経済活動円滑化効果期待されています。