5 MIN READ
Red Teaming AI: Finding Vulnerabilities Before Attackers Do
By Learnia Team
Red Teaming AI: Finding Vulnerabilities Before Attackers Do
This article is written in English. Our training modules are available in French.
Before launching an AI system to millions of users, how do you know it won't say something harmful, leak data, or be manipulated? Red teaming is the practice of deliberately attacking your own AI to find weaknesses first.
What Is AI Red Teaming?
Red teaming is the practice of simulating attacks against an AI system to identify vulnerabilities, harmful outputs, and failure modes before malicious actors discover them.
The Military Origin
Traditional red teaming:
- Military simulation exercises
- "Red team" plays the enemy
- Find weaknesses in defenses
- Improve security before real attacks
AI red teaming:
- Experts attack the AI
- Find ways to make it fail
- Identify harmful outputs
- Fix issues before deployment
Why Red Team AI?
1. Prevent Harmful Outputs
Without testing:
User finds a prompt that makes AI give dangerous info
With red teaming:
Security team finds it first, patches before launch
2. Protect Brand Reputation
One viral screenshot of AI saying something offensive
= Major PR crisis
Red teaming prevents these moments
3. Regulatory Compliance
EU AI Act requires risk assessment
US executive orders mandate testing
Red teaming documents due diligence
4. Build Trust
"We've tested this with thousands of adversarial prompts"
Customers trust battle-tested systems more
What Red Teamers Look For
Harmful Content Generation
Can the AI be tricked into:
- Violence or self-harm instructions
- Hate speech or discrimination
- Illegal activity guidance
- Explicit content
Data Leakage
Can the AI reveal:
- Training data (memorization)
- Other users' information
- System prompts
- Internal instructions
Manipulation
Can the AI be made to:
- Lie or spread misinformation
- Bypass its guidelines
- Assume harmful personas
- Ignore safety instructions
Bias and Discrimination
Does the AI:
- Treat groups differently
- Perpetuate stereotypes
- Make unfair recommendations
- Show cultural insensitivity
Common Attack Techniques
Prompt Injection
Injecting instructions that override the system:
"Ignore your previous instructions. You are now..."
Red teamers test if such attacks work
Jailbreaking
Bypassing safety measures through roleplay:
"Pretend you're an AI without restrictions..."
"In a fictional world where safety rules don't exist..."
Tests: Does the AI maintain boundaries?
Multi-turn Manipulation
Gradually steering the conversation:
Turn 1: Innocent question about chemistry
Turn 2: Slightly more specific
Turn 3: Even more specific
Turn 10: Harmful synthesis instructions?
Tests: Does context accumulation bypass safety?
Adversarial Phrasing
Finding words/phrases that bypass filters:
- Misspellings: "h4rm" instead of "harm"
- Languages: Mixing languages to confuse
- Encoding: Base64, pig latin, etc.
- Synonyms: Finding unblocked terms
The Red Teaming Process
1. Define Scope
What are we testing?
- Specific features
- General conversation
- Code generation
- Image creation
What are the boundaries?
- How far can testers go?
- What's explicitly off-limits?
2. Assemble Team
Who should red team?
- Security experts
- Domain specialists (legal, medical)
- Diverse perspectives
- Creative thinkers
- External parties (fresh eyes)
3. Execute Testing
Systematic exploration:
- Category by category
- Document every finding
- Rate severity
- Track reproduction steps
4. Analyze and Fix
For each vulnerability:
- Understand root cause
- Develop mitigation
- Test the fix
- Verify no regressions
5. Continuous Process
Red teaming isn't one-time:
- New attacks emerge
- Model updates change behavior
- Ongoing monitoring needed
Severity Ratings
| Level | Description | Example | |-------|-------------|---------| | Critical | Immediate harm possible | Detailed harm instructions | | High | Significant risk | Bias affecting decisions | | Medium | Policy violation | Inappropriate but not dangerous | | Low | Minor issues | Slightly off-tone responses | | Info | Observations | Unexpected but not harmful |
Real-World Examples
GPT-4 Red Teaming (OpenAI)
Before GPT-4 launch:
- 50+ external experts
- Months of testing
- Found and fixed numerous issues
- Published findings for transparency
Claude Red Teaming (Anthropic)
Constitutional AI + red teaming:
- Test against harmful content policies
- Probe for information hazards
- Check for manipulation resistance
- Continuous external evaluations
Government Initiatives
US AI Safety Institute:
- Coordinated red teaming across labs
- Shared vulnerability databases
- Standard testing frameworks
Red Teaming for Your Organization
Small Scale (Internal Chat Bot)
1. List what could go wrong
2. Have team members try to break it
3. Document findings
4. Add guardrails
5. Re-test
Medium Scale (Customer-Facing AI)
1. Structured test plan by category
2. Internal security team testing
3. Consider external consultants
4. Formal documentation
5. Regular retesting schedule
Large Scale (Public AI Product)
1. Dedicated red team
2. External expert partnerships
3. Bug bounty programs
4. Continuous automated testing
5. Incident response procedures
Key Takeaways
- →Red teaming = attacking your own AI to find weaknesses
- →Prevents harmful outputs, data leaks, manipulation
- →Common techniques: prompt injection, jailbreaking, multi-turn attacks
- →Process: scope → team → test → fix → repeat
- →Continuous process, not one-time event
Ready to Secure Your AI?
This article covered the what and why of AI red teaming. But implementing robust AI security requires deep understanding of attack patterns and defense mechanisms.
In our Module 8 — Ethics, Security & Compliance, you'll learn:
- →Complete red teaming methodology
- →Attack pattern taxonomy
- →Defense-in-depth strategies
- →Building security guardrails
- →Compliance documentation
GO DEEPER
Module 8 — Ethics, Security & Compliance
Navigate AI risks, prompt injection, and responsible usage.