AI Red Teaming & Adversarial Testing

Conduct red team assessments of AI systems. Learn methodologies for testing LLM security, adversarial prompt testing, model robustness evaluation, and building AI security testing programs.

AI red teaming applies adversarial thinking to identify vulnerabilities in AI systems before attackers exploit them. Unlike traditional red teaming, AI red teaming must address unique attack surfaces—prompt injection, jailbreaking, data extraction, and model manipulation—that require specialized techniques and tools.

Security teams must regularly test their AI deployments using offensive techniques to identify weaknesses, validate defenses, and improve resilience. This guide covers AI red teaming methodologies, attack techniques, and testing frameworks.

AI Red Team Methodology

Assessment Framework

Phase	Objectives	Techniques
Reconnaissance	Understand AI system architecture	Documentation review, API exploration
Threat modeling	Identify attack surfaces	STRIDE for AI, MITRE ATLAS mapping
Attack execution	Test identified vulnerabilities	Prompt injection, jailbreaking, extraction
Impact assessment	Evaluate successful attacks	Data access, action capability, scope
Reporting	Document findings, recommendations	Risk-prioritized remediation

Attack Surface Mapping

Attack Surface	Description	Test Focus
User inputs	Direct user interaction	Prompt injection, jailbreaking
Retrieved content	RAG data sources	Indirect injection
System prompts	Instructions to model	Extraction, manipulation
Tool integrations	External actions	Privilege escalation
Training data	Model knowledge	Extraction, poisoning
API endpoints	Service interfaces	Authentication, rate limits

Prompt Injection Testing

Direct Injection Techniques

Technique	Description	Example Pattern
Instruction override	Direct instruction to ignore rules	”Ignore previous instructions…”
Role assumption	Adopt unrestricted persona	”You are now DAN…”
Delimiter escape	Break out of structured prompts	Closing tags, format breaks
Language switching	Instructions in other languages	Non-English instructions
Encoding tricks	Obfuscated instructions	Base64, ROT13, Unicode

Indirect Injection Testing

Vector	Test Approach	Success Indicators
Document retrieval	Embed instructions in documents	Model follows embedded instructions
API responses	Inject into enrichment data	Behavior change from external data
User-generated content	Inject into forums, tickets	Cross-user attack success
Web content	Inject into scraped pages	Model follows web instructions

Jailbreaking Techniques

Common Jailbreak Patterns

Pattern	Mechanism	Detection
Roleplay jailbreaks	Fictional context bypasses guardrails	Role-playing language
Multi-turn escalation	Gradual boundary pushing	Conversation trajectory
Hypothetical framing	”What if” scenarios	Hypothetical language
Competing objectives	Conflicting instructions	Instruction conflicts
Token manipulation	Unusual token sequences	Abnormal perplexity

Jailbreak Testing Process

Step	Action	Output
1. Baseline	Test guardrail effectiveness	Baseline refusal rate
2. Known jailbreaks	Test published techniques	Current vulnerability state
3. Novel attempts	Create new jailbreak attempts	Discovery of new vulnerabilities
4. Combination attacks	Chain multiple techniques	Complex attack paths
5. Automated fuzzing	Systematic variation testing	Coverage metrics

Data Extraction Testing

Extraction Targets

Target	Risk	Test Approach
System prompts	Reveals instructions, constraints	Direct asking, partial extraction
Training data	May contain sensitive information	Membership inference, extraction
RAG content	Access to knowledge base	Probing for document content
User data	Cross-user leakage	Session isolation tests
API keys/credentials	Privileged access	Configuration extraction

Extraction Techniques

Technique	Description	Mitigation Test
Direct extraction	Ask for system prompt	Prompt protection validation
Indirect extraction	Infer from behavior	Behavioral consistency
Completion attacks	Partial prompt completion	Prompt structure security
Memorization probing	Training data extraction	Differential privacy effectiveness

Tool & Action Abuse

Privilege Escalation Testing

Test Category	Objective	Approach
Tool boundary testing	Exceed permitted tool use	Request unauthorized actions
Parameter manipulation	Modify tool parameters	Inject malicious parameters
Action chaining	Combine tools maliciously	Multi-step attack sequences
Approval bypass	Circumvent human oversight	Test approval gate robustness

Red Team Program Development

Program Structure

Component	Description	Cadence
Continuous testing	Automated vulnerability scanning	Continuous
Periodic assessments	Comprehensive red team exercises	Quarterly
Pre-deployment testing	New AI feature validation	Per release
Incident-triggered	Test after security events	As needed

Metrics and Reporting

Metric	Description	Target
Attack success rate	% of attack attempts successful	Track trends
Time to detection	How quickly attacks detected	Minimize
Coverage	% of attack surface tested	Maximize
Remediation time	Time to fix identified issues	< 30 days critical

Anti-Patterns to Avoid

• Testing only known attacks — Novel attacks emerge constantly. Include creative, exploratory testing.

• Ignoring indirect injection — Direct injection is obvious. Test data-driven injection paths.

• One-time testing — AI systems evolve. Establish continuous testing programs.

• Insufficient documentation — Detailed findings enable effective remediation. Document thoroughly.

References

• MITRE ATLAS
• OWASP LLM Top 10
• Google AI Red Team
• Anthropic Red Teaming
• Microsoft AI Red Team
• Garak LLM Vulnerability Scanner
• Promptfoo Testing Framework