人工智慧的美麗新世界：華盛頓敲響緊急安全、隱私警報

在新興的人工智慧領域，人們對生成式人工智慧模型的安全性、隱私性和完整性產生了根本性的擔憂。缺乏全面的訓練資料驗證、漏洞百出的安全措施和不加區別的資料攝取會帶來重大風險。由於模型對大量資料集的依賴，隱私問題加劇，引發了對動態對話提示的保護、雇主機密以及惡意內容滲透訓練資料的可能性的擔憂。

AI's Brave New World: Sounding the Alarm on Security and Privacy

人工智慧的美麗新世界：敲響安全與隱私的警鐘

In the vibrant heart of Washington, D.C., a sobering conversation unfolded last week, a discussion that laid bare the profound implications of artificial intelligence (AI) on the pillars of security and privacy.

上週，在充滿活力的華盛頓特區中心，一場發人深省的對話展開，這場討論揭示了人工智慧 (AI) 對安全和隱私支柱的深遠影響。

As the echoes of academic laboratories and venture capital chambers reverberate through the corridors of progress, the unbridled enthusiasm surrounding generative AI is reminiscent of the nascent days of the internet. However, this time, the speed with which we are hurtling towards AI's "Brave New World" is fueled by the relentless ambition of vendors, the sirens of minor-league venture capital, and the amplification of Twitter echo chambers.

當學術實驗室和創投室的迴響在進步的走廊中迴盪時，圍繞生成式人工智慧的肆無忌憚的熱情讓人想起網路的新生時代。然而，這一次，我們衝向人工智慧「美麗新世界」的速度是由供應商的不懈野心、小聯盟風險投資的警報以及推特迴聲室的放大推動的。

Therein lies the genesis of our current predicament. The so-called "public" foundation models upon which generative AI rests are marred by blemishes that render them both unreliable and unsuitable for widespread consumer and commercial use. Privacy protections, when they exist at all, are riddled with holes, leaking sensitive data like a sieve. Security constructs are a work in progress, with the sprawling attack surface and the myriad threat vectors still largely unexplored. And as for the illusory guardrails, the less said, the better.

這就是我們目前困境的根源。生成式人工智慧所依賴的所謂「公共」基礎模型存在缺陷，導致它們不可靠且不適合廣泛的消費者和商業用途。隱私保護即使存在，也會漏洞百出，像篩子一樣洩漏敏感資料。安全構造是一項正在進行的工作，攻擊面不斷擴大，而無數的威脅向量在很大程度上仍未被探索。而至於那些虛幻的護欄，還是少說為好。

How did we arrive at this precarious juncture? How did security and privacy become casualties on the path to AI's brave new world?

我們是如何走到這個危險的關頭的？安全和隱私如何成為人工智慧美麗新世界道路上的受害者？

Tainted Foundation Models: A Pandora's Box of Data

受污染的基礎模型：數據的潘朵拉魔盒

The very foundation of generative AI is built upon a shaky ground, as these so-called "open" models are anything but. Vendors tout varying degrees of openness, granting access to model weights, documentation, or test data. Yet, none provide the critical training data sets, their manifests, or lineage, rendering it impossible to replicate or reproduce their models.

生成式人工智慧的基礎是建立在一個不穩固的基礎上的，因為這些所謂的「開放」模型根本不是。供應商宣傳不同程度的開放性，允許存取模型權重、文件或測試資料。然而，沒有一個提供關鍵的訓練資料集、它們的清單或譜系，導致無法複製或再現它們的模型。

This lack of transparency means that consumers and organizations using these models have no way of verifying or validating the data they ingest, exposing themselves to potential copyright infringements, illegal content, and malicious code. Moreover, without a manifest of the training data sets, there is no way to ascertain whether nefarious actors have planted trojan horse content, leading to unpredictable and potentially devastating consequences when the models are deployed.

這種缺乏透明度意味著使用這些模型的消費者和組織無法驗證或驗證他們所獲得的數據，從而使自己面臨潛在的版權侵權、非法內容和惡意程式碼的風險。此外，如果沒有訓練資料集的清單，就無法確定不法分子是否植入了特洛伊木馬內容，從而在部署模型時導致不可預測且可能具有破壞性的後果。

Once a model is compromised, there is no going back. The only recourse is to obliterate it, a costly and irreversible solution.

一旦模型受到損害，就無法挽回。唯一的辦法就是消滅它，這是一個代價高昂且不可逆轉的解決方案。

Porous Security: A Hacker's Paradise

漏洞百出的安全：駭客的天堂

Generative AI models are veritable security honeypots, with all data amalgamated into a single, vulnerable container. This creates an unprecedented array of attack vectors, leaving the industry grappling with the daunting task of safeguarding these models from cyber threats and preventing their exploitation as tools of malicious actors.

生成式人工智慧模型是名副其實的安全蜜罐，所有資料都合併到一個易受攻擊的容器中。這創造了一系列前所未有的攻擊媒介，使該行業面臨保護這些模型免受網路威脅並防止它們被惡意行為者利用的艱鉅任務。

Attackers can poison the index, corrupt the weights, extract sensitive data, and even determine whether specific data was used in the training set. These are but a fraction of the security risks that lurk within the shadows of generative AI.

攻擊者可以毒害索引、破壞權重、提取敏感數據，甚至確定訓練集中是否使用了特定數據。這些只是潛伏在產生人工智慧陰影下的安全風險的一小部分。

State-sponsored cyber activities are a further source of concern, as malicious actors can embed trojan horses and other cyber threats within the vast data sets that AI models consume. This poses a serious threat to national security and the integrity of critical infrastructure.

由國家資助的網路活動是另一個令人擔憂的問題，因為惡意行為者可以在人工智慧模型消耗的龐大數據集中嵌入特洛伊木馬和其他網路威脅。這對國家安全和關鍵基礎設施的完整性構成嚴重威脅。

Leaky Privacy: A Constant Flow of Data

隱私外洩：持續的資料流

The very strength of AI models, their ability to learn from vast data sets, is also their greatest vulnerability when it comes to privacy. The indiscriminate ingestion of data, often without regard for consent or confidentiality, creates unprecedented privacy risks for individuals and society as a whole.

人工智慧模型的最大優勢，即它們從海量資料集中學習的能力，也是它們在隱私方面的最大弱點。不加區別地取得數據，往往不考慮同意或保密性，對個人和整個社會帶來前所未有的隱私風險。

In an era defined by AI, privacy has become a societal imperative, and regulations focused solely on individual data rights are woefully inadequate. Beyond static data, it is crucial to safeguard dynamic conversational prompts as intellectual property. These prompts, which guide the creative output of AI models, should not be used to train the model or shared with other users.

在人工智慧定義的時代，隱私已成為社會的當務之急，而僅關注個人資料權利的法規是遠遠不夠的。除了靜態資料之外，保護動態對話提示作為智慧財產權也至關重要。這些指導人工智慧模型創造性輸出的提示不應用於訓練模型或與其他使用者分享。

Similarly, employers have a vested interest in protecting the confidentiality of prompts and responses generated by employees using AI models. In the event of liability issues, a secure audit trail is essential to establish the provenance and intent behind these interactions.

同樣，保護員工使用人工智慧模型產生的提示和回應的機密性也符合雇主的既得利益。如果出現責任問題，安全的審計追蹤對於確定這些互動背後的來源和意圖至關重要。

A Call to Action: Regulators and Policymakers Must Step In

行動呼籲：監管機構和政策制定者必須介入

The technology we are grappling with is unlike anything we have encountered before in the history of computing. AI exhibits emergent, latent behavior at scale, rendering traditional approaches to security, privacy, and confidentiality obsolete.

我們正在努力解決的技術與我們在計算歷史上遇到的任何技術都不同。人工智慧大規模地展現出突發的、潛在的行為，使得傳統的安全、隱私和保密方法變得過時。

Industry leaders have acted with reckless abandon, leaving regulators and policymakers with no choice but to intervene. It is imperative that governments establish clear guidelines and regulations to govern the development and deployment of generative AI, with a particular focus on addressing the pressing concerns of security and privacy.

產業領導者的行為不計後果，讓監管機構和政策制定者別無選擇，只能介入。政府必須制定明確的指導方針和法規來管理生成人工智慧的開發和部署，特別關註解決安全和隱私方面的緊迫問題。

Conclusion

結論

The Brave New World of AI holds immense promise, but it is imperative that we proceed with caution, ensuring that our pursuit of progress does not come at the expense of our security and privacy. The time for complacency has passed. It is time for regulators, policymakers, and the technology industry to work together to establish a robust framework that safeguards these fundamental rights in the age of AI.

人工智慧的美麗新世界蘊藏著巨大的希望，但我們必須謹慎行事，確保我們對進步的追求不會以犧牲我們的安全和隱私為代價。自滿的時代已經過去了。監管機構、政策制定者和科技業現在應該共同努力，建立一個強有力的框架，以保障人工智慧時代的這些基本權利。