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* 防禦者指南：前沿 AI 對資安的影響...

# 防禦者指南：前沿 AI 對資安的影響

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By [Lee Klarich](https://www.paloaltonetworks.com/blog/author/lee-klarich/?lang=zh-hant&ts=markdown "Posts by Lee Klarich")  
May 27, 2026  
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最新前沿 AI 模型的發布，標誌著資安領域的一個[轉捩點](https://www.paloaltonetworks.com/perspectives/weaponized-intelligence/?ts=markdown)。Palo Alto Networks 已針對最新的前沿 AI 模型進行早期測試，包括作為 [Project Glasswing](https://urldefense.proofpoint.com/v2/url?u=https-3A__www.anthropic.com_glasswing&d=DwMFog&c=V9IgWpI5PvzTw83UyHGVSoW3Uc1MFWe5J8PTfkrzVSo&r=rlV3QLKXfVd47fRPwA7WrCCSjqFsgIzvhSPB3-jJ4YQ&m=nRhLxjABHkXUES6mzW0B-JhxRSH84w3jKEv7ECcKpuM2BKy8-MGX-nfg0ujkjEBo&s=QmMriWCAa2xl2jrIj776bJHxYupmgVICKMU_kECHXKs&e=) 一部分的 Anthropic Mythos 模型，以及作為 [Trusted Access for Cyber 計畫](https://urldefense.proofpoint.com/v2/url?u=https-3A__openai.com_zh-2DHant_index_accelerating-2Dcyber-2Ddefense-2Decosystem_&d=DwMFog&c=V9IgWpI5PvzTw83UyHGVSoW3Uc1MFWe5J8PTfkrzVSo&r=rlV3QLKXfVd47fRPwA7WrCCSjqFsgIzvhSPB3-jJ4YQ&m=nRhLxjABHkXUES6mzW0B-JhxRSH84w3jKEv7ECcKpuM2BKy8-MGX-nfg0ujkjEBo&s=9HetmWR_fSPs7bQsMSHuAwpxK-fRCPXHetFeCWdCPlw&e=)一部分的 OpenAI 最新模型。結論相當明確：這些模型在發現漏洞並生成相應攻擊利用程式方面具備極其強大的能力。

這種世代性的程式撰寫能力提升，將直接轉化為漏洞發現與攻擊利用程式生成能力的重大進展。無論這些能力受到何種防護機制限制，都不可能永遠被限制在安全範圍內。類似的進展也將出現在其他主要 AI 實驗室、中國模型以及開源模型中。攻擊者將會找到這些防護機制中的縫隙。他們將利用先進 AI 大規模發現零時差漏洞、近乎即時地生成攻擊利用程式，並開發出前所未見的自主攻擊代理。

在六個月內，具備深度資安能力的先進 AI 模型將變得普遍。尚未建立適當防護措施的組織，將在企業環境與關鍵基礎設施中面臨一種全新的風險類型。

**前沿** **AI** **：程式理解與撰寫能力的重大飛躍**

正如你可能已經看到的，最新的無限制模型，如 Mythos，相較於 Anthropic 先前的領先模型，在程式撰寫效率上約有 50% 的提升。Palo Alto Networks 已取得這些無限制模型的早期存取權，並能夠運用這項程式能力的大幅提升，帶動掃描與攻擊能力的重大飛躍。

我們數百位頂尖資安工程師一直在評估這些能力，並制定有效運用這些能力的最佳實務。結果揭示了幾項核心事實：

* 大規模漏洞發現：前沿 AI 在識別程式碼漏洞方面極為有效。在不到三週的時間內，它完成了相當於一整年滲透測試工作的成果。
* 攻擊路徑判定：或許比發現個別漏洞更令人印象深刻的是，前沿 AI 擅長進行漏洞串接，能將多個較低嚴重性的問題組合成關鍵等級的攻擊路徑。例如，將兩個中度嚴重性漏洞與一個低度嚴重性漏洞串接成單一關鍵攻擊利用路徑。
* 全堆疊邏輯分析：前沿 AI 能夠分析應用程式的完整暴露面，包括 SaaS 與對外公開平台，並識別傳統工具可能遺漏的邏輯型漏洞。

**對資安環境的影響**

攻擊者多年來一直在使用大型語言模型，但根據我們對前沿 AI 模型的測試，它們將在三個關鍵領域對資安環境產生重大影響：

1. 漏洞洪流：前沿 AI 模型將大幅加快漏洞被發現的速度，無論是防禦者或攻擊者皆然。這在開源領域尤其明顯，而更關鍵的是，隨之而來的大量修補程式本身也將帶來風險。每一個未能立即套用的修補程式，都會成為已知且可被鎖定攻擊的漏洞。組織將需要加快並自動化其修補流程，重新思考如何排列修補優先順序與執行方式，並確保部署最佳等級的防護措施，以在漏洞修復前降低風險。
2. 由內而外攻擊的崛起：近期針對 LiteLLM 和 Trivy 等工具的供應鏈攻擊，顯示出一種日益明顯的趨勢：攻擊會讓敵手直接進入組織的基礎設施內部，繞過多個傳統攻擊步驟，並減少防禦者可採取預防措施的機會。AI 基礎設施的快速部署，使這個問題更加嚴峻，因為 AI 供應鏈，包括執行環境、通訊基礎設施與模型依賴項，往往未受到充分保護。雖然開源使用與修補實務必須變得更加健全，但組織也需要透過零信任、身分現代化、對外連線限制以及橫向移動防護，對潛在攻擊進行結構性圍堵。
3. 更快速的 AI 輔助攻擊週期：我預期前沿 AI 模型帶來最具影響力的轉變，是攻擊將從 AI 輔助走向 AI 驅動。攻擊者將打造自主攻擊代理，大幅壓縮攻擊週期。過去需要熟練人員花費數天或數週手動完成的工作，很快將能在幾分鐘內執行。這種進階攻擊能力的普及，意味著防禦者也必須以近乎即時的偵測與回應速度相匹配，而這只有在整個資安營運中廣泛導入 AI 與自動化才可能實現。若組織的平均偵測時間與平均回應時間無法以低個位數分鐘計算，將會被攻擊者超越。

**防禦者指南：評估、防護、平台化**

防禦 AI 驅動威脅的架構並非全然全新，但執行標準必須絕對到位。所謂「大致受到保護」的組織，實際上等同於未受保護。以下是一套分階段的方法，包括評估、防護與平台化，組織應並行推進這些工作，在攻擊者利用缺口之前先行補強。

\*\*評估：\*\*每個組織都應使用最新 AI 模型，評估其完整的程式碼與應用程式環境，並建立全面的資產與暴露面清單。

**關鍵優先事項：**

* Ÿ 運用 AI 模型，在攻擊者之前識別程式碼庫、應用程式與基礎設施中的漏洞。
* Ÿ 以完整脈絡評估暴露風險，包括漏洞如何相互串接並形成關鍵攻擊路徑。
* Ÿ 稽核開源供應鏈，包括 AI 基礎設施、執行環境與模型依賴項。
* Ÿ 盤點目前的感測器涵蓋範圍。偵測、防禦與遙測資料缺口都代表關鍵盲點。

\*\*防護與修復：\*\*修復並降低暴露風險已是基本門檻。過去可能因跨組織協作摩擦而難以快速發現與修補的問題，如今應在高階管理層對這些新 AI 模型的重視下加速推進。但這還必須更進一步，擴展至全面部署最佳等級的攻擊防禦能力；新的標準是達到 100% 涵蓋與最佳化。

* 全面部署 XDR，重點在於以即時機器學習為基礎的攻擊偵測與防禦；涵蓋所有本地端與雲端主機。
* 代理式端點安全，用以保護企業內大規模採用 vibe coding 與 AI 安全的環境，例如 Prisma AIRS 以及我們近期收購 Koi，現在已成為保護代理式端點的必要能力。
* 如今平均有 85% 的工作在瀏覽器中進行，因此具備即時安全能力的安全企業瀏覽器，已成為攻擊防禦的必備要素。
* 零信任與身分安全，是保護每一位使用者與每一個連線的基礎。

\*\*即時資安營運：\*\*隨著攻擊週期快速縮短，傳統的資安營運方式已不再適用。各自為政的工具以孤島方式分析資料，再搭配人工流程，必須被全面導入 AI 與自動化的方式取代。Cortex XSIAM 是我們的 AI 驅動 SOC 平台，我認為它代表了下一代做法的黃金標準，能將平均偵測時間與平均回應時間縮短至個位數分鐘。

* 攻擊偵測必須由 AI／機器學習驅動，才能大規模偵測即使是頻繁變化與新型的攻擊。
* 這些 AI 偵測能力必須能運作於廣泛的第一方與第三方資料來源之上；最佳等級的 AI SOC 必須能在所有相關資料來源上運作。
* 要達成個位數分鐘的平均回應時間，必須在 SOC 生命週期中原生整合並全面導入自動化；而這種自動化將日益具備代理式特性。
* 這必須以平台方式交付，才能消除點狀解決方案之間的縫隙與缺口。

**我們在此提供協助**

要達到這種層級的韌性，需要正確的平台與專業能力。

為協助你因應這項轉變，Palo Alto Networks推出 [Unit 42 Frontier AI Defense](https://www.paloaltonetworks.com/unit42/ai-advantage?ts=markdown)。這項新服務旨在於攻擊者行動之前，發現並修復你目前的暴露風險，強化可降低暴露與控制影響範圍的管控措施，並現代化營運，使團隊能以機器速度進行偵測與回應。

這正是我們一直在準備迎接的時刻。威脅從未如此複雜，但前進的道路也從未如此清晰；我們將在下一階段與你攜手同行。

*** ** * ** ***

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