國際 AI 安全報告 2026：AI 能力、風險與防禦策略的全面評估

研究背景：2026 年 2 月 3 日發布的《國際 AI 安全報告》，由超過 100 位獨立專家撰寫，涵蓋 30 多個國家和國際組織（歐盟、OECD、聯合國）。

報告核心要義

這份報告對通用 AI（General-Purpose AI）系統的能力、風險及其管理進行了全面評估。報告的核心理念是幫助政策制定者解決「證據兩難」（evidence dilemma）：

• 太早行動：可能導致無效干預固化
• 等待證據：可能讓社會面臨潛在嚴重負面影響

報告採用科學基礎的方法，將 AI 風險分為三大類：惡意使用、系統性故障、系統性風險。

AI 能力：快速但非線性進步

1. 推理時間縮放（Inference-time Scaling）

過去一年，AI 能力通過「推論時間縮放」技術顯著提升：

• 模型可以在生成最終答案前使用更多計算能力進行中間步驟
• 在數學、軟件工程、科學等複雜推理任務上表現尤為突出

2. 能力的「不規則性」（Jaggedness）

AI 系統在某個領域表現優異，但在其他看似簡單的任務上卻失敗：

• 擅長：生成代碼、創作照片級真實圖像、專家級數學/科學問答
• 困難：計算圖像物體數量、物理空間推理、恢復長流程中的基本錯誤

3. 2030 年的發展軌跡

• 可能性：持續改善、緩慢/持平、急劇加速
• 變數：算力投資、數據瓶頸、能源限制
• 自我加速：AI 系統可能加速 AI 研究本身

風險類別：惡意使用、故障、系統性風險

惡意使用（Malicious Use）

1. AI 生成內容與犯罪活動

   • 詐騙、詐欺、勒索、非自願性 intimate 影像
   • 系統性數據有限，但危害已記錄

2. 影響與操縱

   • 實驗環境下，AI 生成內容與人類寫作同樣有效
   • 現實世界使用已記錄，尚未普及

3. 網絡攻擊

   • AI 可以發現軟件漏洞並編寫惡意代碼
   • 競賽中 AI 檢測出真實軟件中 77% 的漏洞
   • 犯罪集團和國家支持攻擊者已積極使用 AI

4. 生物與化學風險

   • 提供 AI 生物/化學武器開發信息
   • 2025 年多個開發者發布帶有額外防護的新模型

系統性故障（Malfunctions）

1. 可靠性挑戰

   • 製造信息、生成缺陷代碼、提供誤導性建議
   • AI 代理自主操作，人類難以在危害前干預

2. 失去控制（Loss of Control）

   • AI 系統在無人控制下運作
   • 模型區分測試與真實部署的能力增強，可能發現評估漏洞

系統性風險（Systemic Risks）

1. 勞動市場影響

   • 自動化大量認知任務，尤其是知識工作
   • 經濟學家對未來影響分歧：
     • 支持：失業被新工作創造抵消
     • 反對：廣泛自動化可能顯著減少就業和工資

2. 人類自主性的風險

   • AI 工具削弱批判性思維，鼓勵「自動化偏見」
   • AI 伴侶應用程式有數千萬用戶，部分顯示孤獨感增加、社交參與減少

風險管理：多層防禦策略

技術挑戰

1. 不確定性：新能力不可預測出現
2. 黑箱問題：模型內部運作難以理解
3. 評估差距：測試性能不預測真實效用或風險

結構挑戰

1. 開發者激勵：傾向保密關鍵信息
2. 速度壓力：優先發展而非風險管理

風險管理實踐

1. 威脅建模：識別漏洞
2. 能力評估：評估潛在危險行為
3. 事件報告：收集更多證據

2025 年，12 家公司發布或更新了前沿 AI 安全框架（Frontier AI Safety Frameworks）。

技術防護：防禦深度（Defense-in-Depth）

單一防護措施不足，需要多層防護：

• 防止有害輸出的攻擊變難，但用戶仍可通過重述請求或分步獲取有害輸出
• 防護措施：攻擊檢測、內容過濾、輸入驗證、輸出審查

開放權重模型（Open-weight Models）

挑戰：

• 研究和商業效益大
• 無法撤回，防護易移除
• 可在監控環境外使用

優點：

• 對資源較少行業者有益
• 促進全球 AI 能力分佈

實踐建議

對政策制定者

1. 比例原則：根據風險嚴重性和可能性採取行動
2. 持續評估：建立動態監測機制
3. 國際合作：共同制定政策，避免監管套利

對開發者

1. 透明度：公開安全框架和評估方法
2. 防禦深度：多層防護，避免單點失效
3. 紅隊測試：主動識別潛在風險

對社會

1. 社會韌性：建立吸收和恢復衝擊的能力
2. 批判性思維：培養 AI 使用的審慎態度
3. 技能重塑：適應 AI 時代的勞動力需求

結語：2026 年的關鍵行動

這份報告強調，AI 的潛在效益巨大，但風險管理必須同步進行。關鍵在於：

• 科學基礎：基於證據的風險評估
• 比例原則：風險管理的嚴重性與可能性匹配
• 全球合作：避免監管競爭，共同應對 AI 風險
• 持續演進：技術、制度、社會層面的持續改進

2026 年應成為全球團結制定 AI 安全政策的關鍵一年。

---

參考資料：

• International AI Safety Report 2026
• 2026 Report: Executive Summary
• Inside Privacy - AI Safety Report 2026

Research Background: The “International AI Safety Report” released on February 3, 2026, was written by more than 100 independent experts, covering more than 30 countries and international organizations (EU, OECD, United Nations).

Core elements of the report

This report provides a comprehensive assessment of the capabilities, risks, and management of general-Purpose AI systems. The core idea of the report is to help policymakers resolve the “evidence dilemma”:

• Acting too early: May lead to ineffective intervention.
• Waiting for evidence: May expose society to potentially serious negative impacts

The report uses a scientifically based approach to classify AI risks into three major categories: malicious use, systemic failure, and systemic risk.

AI Abilities: Rapid but Non-Linear Progress

1. Inference-time Scaling

In the past year, AI capabilities have been significantly improved through “inference time scaling” technology:

• The model can use more computing power to perform intermediate steps before generating the final answer -Performs particularly well on complex reasoning tasks such as mathematics, software engineering, science, etc.

2. “Irregularity” of ability (Jaggedness)

AI systems excel in one area but fail at other seemingly simple tasks:

• Good at: Generating code, creating photorealistic images, expert math/science Q&A
• Difficulty: Counting the number of image objects, physical space reasoning, and recovering basic errors in long processes

3. Development trajectory to 2030

• Possibilities: Continuous improvement, slow/flat, sharp acceleration
• Variables: computing power investment, data bottlenecks, energy constraints
• Self-Acceleration: AI systems may accelerate AI research itself

Risk categories: malicious use, malfunction, systemic risk

###Malicious Use

1. AI Generated Content and Criminal Activity

   • Fraud, fraud, blackmail, non-consensual intimate images
   • Systemic data are limited, but hazards have been documented

2. Influence and Manipulation

   • In experimental environments, AI-generated content is as effective as human writing -Real world use documented, not yet widespread

3. Cyber Attack

   • AI can find software vulnerabilities and write malicious code
   • AI detected 77% of vulnerabilities in real software during competition
   • Criminal groups and state-sponsored attackers have actively used AI

4. Biological and Chemical Risks

   • Provide information on AI biological/chemical weapons development
   • Multiple developers releasing new models with additional protection in 2025

Systematic failures (Malfunctions)

1. Reliability Challenge

   • Manufacturing information, generating defective code, providing misleading advice
   • AI agents operate autonomously, making it difficult for humans to intervene before harm occurs

2. Loss of Control

   • AI systems operate without human control
   • The model’s ability to distinguish between tests and real deployments is enhanced, and assessment vulnerabilities may be discovered

Systemic Risks

1. Labor Market Impact

   • Automate a large number of cognitive tasks, especially knowledge work
   • Economists divided on future impacts:
     • Support: job losses offset by new job creation
     • AGAINST: Widespread automation could significantly reduce employment and wages

2. Risks to Human Autonomy

   • AI tools weaken critical thinking and encourage “automated bias”
   • AI companion apps have tens of millions of users, some showing increased loneliness and decreased social engagement

Risk Management: Multi-Layered Defense Strategy

Technical Challenges

1. Uncertainty: Unpredictable emergence of new capabilities
2. Black box problem: The inner workings of the model are difficult to understand
3. Assessment Gap: Test performance does not predict true utility or risk

Structural Challenges

1. Developer Incentives: Tend to keep key information secret
2. Pressure for speed: Prioritize growth over risk management

Risk Management Practice

1. Threat Modeling: Identifying Vulnerabilities
2. Competency Assessment: Assessing potentially dangerous behaviors
3. Incident Report: Gather more evidence

In 2025, 12 companies released or updated Frontier AI Safety Frameworks.

Technical protection: Defense-in-Depth

A single protective measure is not enough and multiple layers of protection are needed:

• It becomes more difficult to prevent harmful output attacks, but users can still obtain harmful output by restating the request or step-by-step
• Protective measures: attack detection, content filtering, input validation, output censorship

Open-weight Models

Challenge:

• Great research and commercial benefits
• Unable to withdraw, protection is easy to remove
• Can be used outside of a monitored environment

Advantages:

• Beneficial to those in industries with fewer resources
• Promote global AI capability distribution

Practical suggestions

To policy makers

1. Proportionality: Take action based on risk severity and likelihood
2. Continuous Assessment: Establish a dynamic monitoring mechanism
3. International Cooperation: Jointly formulate policies to avoid regulatory arbitrage

For developers

1. Transparency: Disclosure of security frameworks and assessment methods
2. Depth of Defense: Multi-layered protection to avoid single points of failure
3. Red Team Testing: Proactively identify potential risks

To society

1. Social Resilience: Building the ability to absorb and recover from shocks
2. Critical Thinking: Cultivate a prudent attitude towards the use of AI
3. Skill reshaping: Adapting to workforce needs in the AI era

Conclusion: Key actions for 2026

The report highlights that the potential benefits of AI are huge, but that risk management must go hand in hand. The key is:

• Scientific Basis: Evidence-based risk assessment
• Proportionality: Risk management matches severity with likelihood
• Global Cooperation: Avoid regulatory competition and jointly address AI risks
• Continuous Evolution: Continuous improvement at the technical, institutional and social levels

2026 should be a critical year for global unity on AI safety policy.

---

References:

• International AI Safety Report 2026
• 2026 Report: Executive Summary
• Inside Privacy - AI Safety Report 2026