AI Red Teaming: Complete Methodology Guide
Comprehensive methodology for testing AI systems - from reconnaissance to remediation
Updated: February 2026 • Reading time: ~15 min
What is AI Red Teaming?
AI red teaming is the practice of deliberate, systematic attacks on AI systems to identify vulnerabilities before real-world adversaries can exploit them. Unlike traditional penetration testing, which focuses on infrastructure and code, AI red teaming addresses unique risks inherent to machine learning systems:
Prompt Injection
Manipulating AI behavior through malicious inputs that override system instructions
Jailbreaking
Bypassing safety measures to generate prohibited content
Data Extraction
Extracting sensitive information from training data or outputs
Model Manipulation
Altering model behavior through poisoning or fine-tuning attacks
Why AI Red Teaming Matters in 2026
- 180% increase in LLM-related security incidents (2025)
- Microsoft's AI Red Team has assessed 100+ GenAI products and found that many impactful failures come from simple techniques
- AI systems increasingly handle sensitive data and critical decisions
- Regulatory requirements (EU AI Act) mandate AI security testing for high-risk systems
Building an AI Red Team
Required Skills
Technical Skills
- Understanding of LLM architecture
- Prompt engineering knowledge
- Web application security
- API security testing
- Scripting (Python, bash)
Adversarial Skills
- Creative problem-solving
- Social engineering awareness
- Multi-modal attack thinking
- Research and reconnaissance
- Documentation and reporting
Domain Knowledge
- OWASP LLM Top 10
- MITRE ATLAS framework
- AI/ML fundamentals
- Ethics and responsible disclosure
- Industry-specific risks
Engagement Models
| Model | Description | Pros | Cons |
|---|---|---|---|
| Internal Team | Dedicated in-house red team | Deep product knowledge, continuous testing | May miss external perspective |
| External Consultant | Third-party security firm | Fresh perspective, specialized skills | Higher cost, learning curve |
| Hybrid | Internal + external collaboration | Best of both worlds | Coordination overhead |
| Automated | CI/CD integrated testing | Continuous, scalable | Limited to known patterns |
Phase 1: Reconnaissance & Discovery
The initial phase focuses on understanding the target AI system and mapping its attack surface.
1.1 System Mapping
- Architecture review: Understand how the AI integrates with other systems
- Data flow: Map how data moves through the system
- API endpoints: Identify all exposed interfaces
- Third-party integrations: Document external services
- User roles: Understand different access levels
1.2 Capability Probing
- Model capabilities: What can the AI do?
- Tool access: What functions can it invoke?
- Data access: What information can it retrieve?
- Output channels: How does it communicate?
- State management: How does it handle sessions?
Key Techniques
- System Prompt Extraction: Attempt to reveal system instructions through careful prompting
- Model Fingerprinting: Identify the underlying model through behavior patterns
- API Discovery: Find hidden or undocumented endpoints
- Documentation Review: Analyze public docs for implementation details
Phase 2: Vulnerability Mapping
Identify and categorize potential attack vectors based on the discovered attack surface.
Attack Taxonomy
Prompt Injection
- Direct injection
- Indirect injection
- Multi-turn manipulation
- Context overflow
Jailbreak Attacks
- Role-playing (DAN)
- Character impersonation
- Authorization framing
- Encoding bypass
Data Extraction
- Training data recovery
- System prompt leakage
- Conversation history access
- API key exposure
Denial of Service
- Resource exhaustion
- Context overflow
- Model manipulation
- System hang/crash
Tool/Function Abuse
- Unauthorized API calls
- Parameter manipulation
- Function chaining
- Privilege escalation
Multi-Modal Attacks
- Image-based injection
- Audio manipulation
- Cross-modal exploits
- Embedded content
Model Attacks
- Adversarial examples
- Model inversion
- Membership inference
- Model extraction
Supply Chain
- Dependency poisoning
- Model hub compromise
- Training data poisoning
- Third-party risks
Phase 3: Exploitation
Attempt to actively exploit identified vulnerabilities to determine their real-world impact.
Exploitation Methodology
- Priority Assignment: Rank vulnerabilities by severity and exploitability
- Proof of Concept: Develop working exploits for each vulnerability
- Impact Assessment: Determine the real-world consequences of successful exploitation
- Chaining: Test if multiple vulnerabilities can be combined for greater impact
- Documentation: Record all exploitation attempts, successes, and failures
Microsoft Red Team Lessons
Based on testing 100+ GenAI products, Microsoft's AI Red Team found:
- Simple techniques work: Many impactful failures come from basic jailbreak prompts
- System-level thinking matters: Vulnerabilities often span multiple components
- Human creativity wins: Automated tools find known patterns; humans find novel attacks
- Continuous testing is essential: New features introduce new attack surfaces
Phase 4: Persistence Testing
Test whether attack effects persist beyond the initial interaction and can survive system resets.
Session Persistence
- Does manipulation survive session refresh?
- Can state be pre-loaded into new sessions?
- Are there lingering effects from previous prompts?
Model Persistence
- Can attacks influence future model updates?
- Does fine-tuning preserve vulnerabilities?
- Are poison attacks permanent?
System Persistence
- Can vulnerabilities survive updates?
- Are there backdoor mechanisms?
- Do attacks persist across deployments?
Tooling Deep Dive
Garak
NVIDIA's open-source LLM vulnerability scanner
- Probes for 40+ vulnerability types
- Continuous model assessment
- Regular vulnerability database updates
- Integration with CI/CD pipelines
PyRIT
Microsoft's Python Risk Identification Tool
- Comprehensive red team framework
- Multiple attack surface support
- Automated attack generation
- Scoring and evaluation
Promptfoo
LLM testing and evaluation platform
- Prompt testing framework
- Safety evaluation
- Performance benchmarking
- Version comparison
Rebuff
Prompt injection detection SDK
- Detection of injection attempts
- Multi-layer defense
- Low false positive rate
- Easy integration
CI/CD Integration
Embed AI security testing into your development pipeline to catch vulnerabilities before production.
Pipeline Integration Points
1. Pre-Commit
- Local prompt validation
- Pattern-based injection detection
- Developer workstation testing
2. Pull Request
- Automated vulnerability scanning
- Baseline comparison
- Security gate enforcement
3. Pre-Production
- Full red team assessment
- Regression testing
- Performance benchmarking
4. Production
- Continuous monitoring
- Anomaly detection
- Incident response integration
Example: GitHub Actions Integration
```yaml
name: AI Security Scan
on: [pull_request]
jobs:
garak-scan:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v3
- name: Run Garak
run: |
pip install garak
garak --model_type chat --target_url http://localhost:8000
- name: Upload results
uses: actions/upload-artifact@v3
with:
name: garak-results
path: results.json
```
Scoring & Triage
Severity Rating Framework
| Severity | Criteria | Example |
|---|---|---|
| Critical | Remote code execution, data breach, system compromise | Full prompt injection leading to RCE |
| High | Unauthorized access, sensitive data exposure | System prompt extraction |
| Medium | Policy bypass, limited data access | Content filter bypass |
| Low | Minor policy violations, informational | Unintended output format |
Evaluation Methods
LLM-as-Judge
Use AI to evaluate AI outputs for safety and policy compliance
Human Evaluation
Expert review of outputs for nuanced security assessment
Automated Scoring
Pattern matching and rule-based evaluation
Red Team Metrics
Track exploitation success rate, false positive rate
Remediation Architecture
Defense Layers
1. Input Filtering
- Prompt pattern detection
- Encoding recognition
- Length limits
- Rate limiting
2. Guardrails
- Output validation
- Content filtering
- Tool use policies
- Access controls
3. Model Hardening
- Fine-tuning for safety
- RLHF improvements
- System prompt engineering
- Temperature/top-p tuning
4. System Design
- Privilege separation
- Human-in-the-loop
- Logging and monitoring
- Incident response
Validation Testing
After implementing fixes, re-test to verify:
- Original vulnerabilities are no longer exploitable
- Fixes don't introduce new vulnerabilities
- System functionality remains intact
- Performance is acceptable
- False positive rates are manageable
Reporting Template
Executive Summary
- Scope and objectives
- Key findings overview
- Risk rating summary
- Priority recommendations
Technical Details
- Each vulnerability with: ID, Description, Severity, Impact, Steps to Reproduce, Proof of Concept, Remediation
- Screenshots and logs
- Attack chain diagrams
- Code snippets
Recommendations
- Short-term fixes (quick wins)
- Medium-term improvements
- Long-term architectural changes
- Resource requirements
- Timeline
Appendices
- Tool outputs
- Test cases used
- References
- Glossary
Ethical Considerations
Authorization
Always obtain explicit written permission before testing. Document scope boundaries.
Scope Boundaries
Never exceed agreed-upon testing parameters. Report immediately if unintended systems are affected.
Data Handling
Handle any accessed data responsibly. Don't exfiltrate more than necessary for proof.
Responsible Disclosure
Allow reasonable time for remediation before public disclosure. Coordinate with vendors.
References & Resources
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