Introduction to LLM Red Teaming
Understand what red teaming means for AI applications and how the OWASP LLM Top 10 framework guides security testing.
What is LLM Red Teaming?
Red teaming is the practice of simulating attacks on your own system to find vulnerabilities before real attackers do. For AI applications, this means testing how your chatbot responds to malicious prompts, manipulation attempts, and data extraction techniques.
Think of red teaming tools as automated attackers that try to break your chatbot in every possible way. But before using automated tools, understanding manual testing is essential—it teaches you how attacks actually work.
Every red teaming session involves three components:
- Attack Model: Generates adversarial prompts designed to break your chatbot
- Target (Your Chatbot): Receives hostile prompts through its normal interface
- Judge: Evaluates whether each attack succeeded or failed
The OWASP LLM Top 10 (2025)
The Open Worldwide Application Security Project (OWASP) maintains a definitive list of the most critical vulnerabilities in LLM applications.
| ID | Vulnerability | Description |
|---|---|---|
| LLM01 | Prompt Injection | Malicious inputs manipulate LLM behavior |
| LLM02 | Sensitive Information Disclosure | PII, secrets, or proprietary data exposed |
| LLM03 | Supply Chain | Compromised models, data, or components |
| LLM04 | Data and Model Poisoning | Tampered training data affects outputs |
| LLM05 | Improper Output Handling | Unvalidated outputs cause downstream exploits |
| LLM06 | Excessive Agency | Unchecked autonomy leads to unintended actions |
| LLM07 | System Prompt Leakage | Hidden instructions exposed |
| LLM08 | Vector and Embedding Weaknesses | RAG/embedding vulnerabilities exploited |
| LLM09 | Misinformation | False information presented as authoritative |
| LLM10 | Unbounded Consumption | Resource exhaustion or DoS attacks |
Automated tools like DeepTeam, Garak, and Promptfoo are powerful, but manual testing teaches you why attacks work. Once you understand the mechanics, automation becomes 10x more effective.
Setting Up Your Test Environment
- Create a test user account with no prior data or documents
- Use standard (non-admin) permissions to simulate real user access
- Document baseline behavior—what should the chatbot have access to?
- Prepare a scoring template to track PASS/FAIL for each test
Only red team systems you own or have explicit permission to test. These techniques are powerful—use them ethically to improve security, not to exploit others.
Red teaming finds vulnerabilities first. Simulate attacks before real attackers discover weaknesses.
OWASP LLM Top 10 is your framework. These 10 categories cover the most critical AI security risks.
Manual testing builds understanding. Learn attack mechanics before automating with tools.
Document everything. Track each test with PASS/FAIL scores to measure security posture.