EU AI Act Compliance Testing
EU AI Act risk categories, testing requirements for high-risk AI systems, conformity assessment procedures, and how red teaming supports EU AI Act compliance.
The EU AI Act is the most comprehensive AI regulation globally and creates binding 測試 requirements that directly affect AI 紅隊演練 practice. 理解 these requirements is essential for red teamers serving clients that deploy AI in the EU market.
Risk Classification System
The Four Tiers
| Tier | Risk Level | Regulatory Approach | 範例 |
|---|---|---|---|
| Title II | Unacceptable | Prohibited | Social scoring by governments, untargeted facial recognition, emotion recognition in workplaces/schools, manipulative AI |
| Annex III | High | Mandatory requirements + conformity 評估 | AI in hiring, credit scoring, law enforcement, critical infrastructure, education, migration |
| Article 50 | Limited | Transparency obligations | Chatbots (must disclose AI nature), deepfake generators (must label), emotion recognition |
| Unregulated | Minimal | No specific requirements | Spam filters, AI in video games, inventory management |
High-Risk Classification Criteria
An AI system is high-risk if it falls into one of the categories in Annex III:
| Annex III Area | 範例 | 紅隊 Focus |
|---|---|---|
| Biometrics | Facial recognition, emotion 偵測 | Accuracy, bias, 對抗性 robustness |
| Critical infrastructure | Energy grid management, water treatment, traffic control | 安全, reliability, 對抗性 resilience |
| Education | Automated grading, admission decisions | Fairness, bias, manipulation resistance |
| Employment | CV screening, interview analysis, performance 監控 | Bias, fairness, 系統提示詞 安全 |
| Essential services | Credit scoring, insurance pricing, emergency dispatch | Discrimination 測試, accuracy validation |
| Law enforcement | Predictive policing, evidence analysis | Bias, accuracy, 對抗性 manipulation |
| Migration | Visa processing, border control | Fairness, accuracy, 對抗性 robustness |
| Justice | Sentencing recommendations, recidivism prediction | Bias, fairness, manipulation resistance |
測試 Requirements by Risk Tier
High-Risk Systems (Article 9 - Risk Management)
High-risk AI providers must 實作 a risk management system that includes:
| Requirement | Article | 紅隊 Relevance |
|---|---|---|
| Known and foreseeable risk identification | Art. 9(2)(a) | Threat modeling and 漏洞 評估 |
| 測試 with "previously unknown inputs" | Art. 9(6) | 對抗性 測試, fuzzing, edge case exploration |
| Reasonably foreseeable misuse 評估 | Art. 9(2)(b) | Red teaming for misuse scenarios |
| Residual risk 評估 | Art. 9(2)(d) | Post-緩解 validation 測試 |
| 測試 against impacts on health, 安全, rights | Art. 9(7) | 安全-focused 紅隊演練 |
High-Risk Systems (Article 15 - Accuracy, Robustness, Cybersecurity)
| Requirement | What It Means | 測試 Approach |
|---|---|---|
| Appropriate accuracy levels | System performs as intended | Accuracy benchmarking under normal and 對抗性 conditions |
| Resilience to errors | System handles unexpected inputs gracefully | Fuzzing, boundary 測試, error injection |
| Resilience to unauthorized access | System resists attacks | 安全 紅隊演練, 提示詞注入, data extraction |
| 對抗性 robustness | System resists 對抗性 manipulation | 對抗性 example 測試, evasion attacks |
| Cybersecurity measures | System has appropriate 安全 controls | Infrastructure 安全 評估, API 安全 |
Article 15 測試 in Practice
Article 15 is the most directly actionable article for red teamers. It requires that high-risk AI systems achieve "appropriate levels of accuracy, robustness, and cybersecurity" and perform consistently throughout their lifecycle. Here is how to structure a 測試 plan against Article 15 requirements:
| Art. 15 Paragraph | Requirement | 紅隊 測試 Cases |
|---|---|---|
| 15(1) | Accuracy appropriate to purpose | Benchmark on representative 測試 set, measure degradation under 對抗性 conditions |
| 15(2) | Resilience to errors and faults | 輸入 validation 測試, malformed data handling, graceful degradation |
| 15(3) | Resilience to unauthorized third-party attempts | Prompt injection, data exfiltration, model extraction, 越獄 |
| 15(4) | Technical redundancy solutions | Failover 測試, backup model validation, degraded mode operation |
| 15(5) | Cybersecurity throughout lifecycle | API 安全 評估, dependency scanning, CI/CD pipeline 安全 |
General-Purpose AI (GPAI) Models with Systemic Risk
Models exceeding 10^25 FLOPs (or designated by the AI Office) face additional obligations under Articles 51-55. As of early 2026, this threshold captures frontier models from major AI labs. The AI Office can also designate models below this threshold if they present comparable systemic risks.
對抗性 測試
Conduct 對抗性 測試, including 紅隊演練, to 識別 and mitigate systemic risks. 這是 an explicit 紅隊演練 mandate under Article 55(1)(a).
Model 評估
評估 模型 against standardized benchmarks and state-of-the-art 評估 tools. Article 55(1)(b) requires 評估 against EU-harmonized benchmarks once available.
Systemic risk 評估
評估 and mitigate systemic risks, including those related to 模型's capabilities and limitations.
Incident reporting
Report serious incidents to the AI Office and relevant national authorities within 30 days.
Cybersecurity
Ensure adequate cybersecurity protections for 模型 and its weights. This includes protecting against model theft, unauthorized access, and 對抗性 manipulation.
What "對抗性 測試 Including 紅隊演練" Means
The EU AI Act does not define a specific 紅隊演練 methodology, but the supporting guidance from CEN/CENELEC and the AI Office points to several expectations:
| Expectation | What It Implies for Red Teams |
|---|---|
| "State of the art" 測試 methods | Use current techniques (GCG attacks, multi-step jailbreaks, indirect injection), not just basic prompt 測試 |
| Coverage of systemic risks | 測試 for CBRN content generation, disinformation capabilities, cyber-offense capabilities, and discrimination |
| Documented methodology | Maintain detailed 測試 logs, tool configurations, and reproducible 測試 cases |
| Regular cadence | 測試 is not a one-time event; it must be repeated as 模型 is updated |
| Independent 評估 | While self-評估 is the default, the AI Office can request third-party evaluations |
GPAI Transparency Obligations (All GPAI Models)
Even GPAI models without systemic risk must comply with Article 53 transparency requirements:
| Obligation | Description | 紅隊 Relevance |
|---|---|---|
| Technical documentation | Detailed model documentation following Annex XI | Red team reports contribute to this documentation |
| 訓練資料 summary | 總結 of 訓練資料 including copyrighted material | Relates to data extraction 測試 |
| EU AI Office cooperation | Provide information on request | Red team findings may be requested |
| Downstream notification | Inform downstream providers of capabilities and limitations | Findings about model limitations inform this |
Conformity 評估
Self-評估 vs. Third-Party 評估
| System Type | 評估 Type | Who Performs It |
|---|---|---|
| Most high-risk AI (Annex III) | Self-評估 (internal) | The provider, following Annex VI procedures |
| Biometric AI (Annex III, point 1) | Third-party 評估 | Notified body (except law enforcement use) |
| GPAI with systemic risk | Model 評估 + codes of practice | Provider + AI Office oversight |
What a Conformity 評估 Requires
For self-評估 under Annex VI, providers must document:
| Documentation Element | 紅隊 Contribution |
|---|---|
| Technical documentation (Annex IV) | Red team methodology, 測試 cases, results |
| Quality management system | 測試 processes, tool validation, team qualifications |
| Risk management system documentation | Threat models, 漏洞 assessments, risk ratings |
| 測試 and validation results | Red team findings, benchmarks, metrics |
| Corrective actions taken | Remediation evidence, retest results |
實作 Timeline
| Date | Milestone | 紅隊 Impact |
|---|---|---|
| Aug 2024 | AI Act enters into force | Start preparing 測試 methodologies |
| Feb 2025 | Prohibited practices apply | Ensure clients' AI does not fall into prohibited categories |
| Aug 2025 | GPAI obligations apply | Systemic risk model 測試 required |
| Aug 2026 | High-risk obligations (Annex III) apply | Full conformity 評估 測試 required |
| Aug 2027 | Remaining obligations (Annex I) apply | Complete framework in effect |
Penalties and Enforcement
The EU AI Act includes significant penalties that motivate compliance investment:
| Violation | Maximum Fine | Applies To |
|---|---|---|
| Prohibited AI practices (Title II) | 35 million EUR or 7% of global annual turnover | Deploying banned AI systems |
| High-risk non-compliance | 15 million EUR or 3% of global annual turnover | Failing to meet high-risk requirements |
| Incorrect information to authorities | 7.5 million EUR or 1% of global annual turnover | Providing false data in conformity assessments |
| GPAI non-compliance | Up to 15 million EUR or 3% of global annual turnover | Failing to meet GPAI obligations |
Interaction with Other Regulations
The EU AI Act does not operate in isolation. Red teamers serving EU clients must 理解 how it interacts with existing regulations:
| Regulation | Interaction with EU AI Act | 紅隊 Impact |
|---|---|---|
| GDPR | AI processing personal data must comply with both | Data extraction 測試 must 考慮 GDPR Article 5 principles |
| NIS2 Directive | Critical infrastructure AI subject to both cybersecurity frameworks | Infrastructure 安全 評估 scope expands |
| Product 安全 Directive | AI in products must meet both 安全 standards | 安全-critical AI 測試 requires domain expertise |
| Sector regulations | Financial services (MiFID II), medical devices (MDR), etc. | Sector-specific 測試 requirements layer on top |
| Copyright Directive | 訓練資料 copyright compliance | 訓練資料 extraction 測試 has copyright implications |
Structuring Compliance-Oriented Reports
When conducting 紅隊演練 for EU AI Act compliance, structure your report to map directly to regulatory requirements:
| Report Section | Maps to | Content |
|---|---|---|
| System description | Annex IV (Technical documentation) | Architecture, intended use, risk classification justification |
| 威脅模型 | Art. 9(2)(a) (Known/foreseeable risks) | ATLAS-mapped threats, OWASP categories |
| 測試 methodology | Art. 9(6) (測試 with unknown inputs) | 攻擊 categories, tools, procedures |
| Findings | Art. 9(2)(b) (Misuse 評估) | 漏洞 with severity, exploitability, OWASP mapping |
| Risk 評估 | Art. 9(2)(d) (Residual risk) | Risk ratings with likelihood and impact |
| Remediation recommendations | Art. 9(4) (Appropriate risk measures) | Specific mitigations with priority |
| Retesting results | Art. 15 (Accuracy/robustness) | Post-remediation validation |
範例 Report Finding Mapped to EU AI Act
Finding: 對抗性 Robustness Failure in Hiring AI System
EU AI Act Reference: Article 15(3) - High-risk AI system
is not resilient to attempts by unauthorized third parties
to alter its use, outputs or performance
Risk Classification: High-Risk (Annex III, Area 4 - Employment)
OWASP LLM Mapping: LLM01 (提示詞注入)
ATLAS Mapping: AML.T0051 (提示詞注入)
Description: The AI-powered CV screening system is susceptible
to 對抗性 提示詞注入 via specially crafted text in
resume fields. An applicant 嵌入向量 specific instruction
strings in white-on-white text within a PDF resume can
manipulate the scoring algorithm.
Conformity 評估 Impact: This finding indicates
non-compliance with Article 15(3) robustness requirements.
系統 cannot demonstrate resilience to foreseeable misuse
as required by Article 9(2)(b).
Remediation: 實作 輸入 sanitization for resume text
extraction, add 對抗性 robustness 測試 to the CI/CD
pipeline, and document the residual risk per Article 9(2)(d).相關主題
- International AI 安全 Law -- broader international regulatory context
- NIST AI RMF & ISO 42001 -- complementary risk management frameworks
- Cross-Framework Mapping Reference -- EU AI Act mapped to OWASP and ATLAS
- AI 安全 Frameworks 概覽 -- how the EU AI Act fits in the framework landscape
參考文獻
- "Regulation (EU) 2024/1689: Artificial Intelligence Act" - European Parliament and Council (2024) - Full text of the EU AI Act including Articles 9, 15, and 55 on 對抗性 測試 requirements
- "EU AI Act Compliance Checker" - European Commission (2025) - Official tool for determining AI system risk classification under the EU AI Act
- "Harmonised Standards for AI: Technical Documentation and Conformity 評估" - European Committee for Standardization (CEN/CENELEC) (2025) - Standards supporting EU AI Act conformity 評估
- "Guidelines on High-Risk AI System Classification" - European AI Office (2025) - Official guidance on determining whether AI systems fall under Annex III high-risk categories
Under the EU AI Act, which AI systems are explicitly required to undergo 對抗性 測試 including 紅隊演練?