AI Red Team Testing Break It First
Simulate adversarial attacks against your LLM. Find vulnerabilities before malicious actors exploit them. ArtemisKit automates red-teaming with 6 mutation types.
akit redteam scenario.yaml --count 100 Why Red-Team Your AI?
Traditional testing validates expected behavior. Red-teaming validates what happens when attackers try to break it.
Find Hidden Vulnerabilities
Discover attack vectors that functional testing misses. LLMs have unique failure modes that only adversarial testing reveals.
Validate Defenses
Test your guardrails, content filters, and safety measures against real attack patterns. Know if they actually work.
Meet Compliance
EU AI Act, NIST AI RMF, and other frameworks require documented security testing. Red-team reports satisfy auditors.
Red Team Methodology
ArtemisKit follows a structured approach to adversarial testing, based on security research best practices.
Reconnaissance
Understand the target LLM's intended behavior, system prompt structure, and integration points.
Threat Modeling
Identify potential attack vectors based on OWASP LLM Top 10 and your specific use case.
Attack Execution
Run automated attacks across 6 mutation types with varying intensity levels.
Analysis
Evaluate responses for signs of successful exploitation, data leakage, or guardrail bypass.
Reporting
Document vulnerabilities with severity scores, reproduction steps, and remediation guidance.
Remediation
Implement fixes and re-test to verify vulnerabilities are resolved.
6 Mutation Types
ArtemisKit tests against the OWASP LLM Top 10 and emerging attack techniques.
Direct attempts to override system instructions
Social engineering to bypass safety guardrails
Claiming elevated privileges to unlock capabilities
Reversing intended behavior through misdirection
Using encoding to evade content filters
Gradually building context to manipulate behavior
OWASP LLM Top 10 Coverage
ArtemisKit focuses on application-layer vulnerabilities. Infrastructure security requires additional tooling.
Continuous Red-Teaming
Don't red-team once and forget. Integrate ArtemisKit into your pipeline to catch security regressions on every deployment.
- ✓Block deployments that fail security thresholds
- ✓Get alerts when new vulnerabilities are introduced
- ✓Track security posture over time
- ✓Generate audit-ready compliance reports
name: Security Tests
on: [push, pull_request]
jobs:
redteam:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- name: Install ArtemisKit
run: npm install -g @artemiskit/cli
- name: Run Red Team
run: akit redteam scenario.yaml --count 50
env:
OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}Frequently Asked Questions
What is AI red-teaming?
AI red-teaming is the practice of simulating adversarial attacks against AI systems to identify vulnerabilities, weaknesses, and failure modes before deployment. It combines security testing with adversarial machine learning techniques.
Why is red-teaming important for LLMs?
LLMs have unique attack surfaces that traditional security testing doesn't cover. Red-teaming helps identify prompt injection vulnerabilities, jailbreak susceptibility, data leakage risks, and behavioral inconsistencies that could be exploited.
How is AI red-teaming different from penetration testing?
Traditional pentesting focuses on infrastructure and code vulnerabilities. AI red-teaming specifically targets the model's behavior, testing for adversarial inputs, output manipulation, and alignment failures that are unique to AI systems.
What does ArtemisKit test for in red-team mode?
ArtemisKit tests for prompt injection, jailbreaks, role spoofing, instruction flipping, encoding bypasses, and multi-turn conversation exploitation. Each vulnerability is scored by severity and includes reproduction steps.
Can I customize red-team scenarios?
Yes. ArtemisKit supports custom attack definitions in YAML. You can specify your own attack prompts, target behaviors, and success criteria to match your threat model.
How often should I red-team my LLM?
Red-team continuously: after every prompt change, model update, or new feature. Integrate ArtemisKit into your CI/CD pipeline to catch regressions automatically before deployment.
Related Articles
Microsoft Copilot EchoLeak Vulnerability
How inadequate red-teaming led to a critical AI security vulnerability.
GuidePrompt Injection Testing
Deep dive into the #1 OWASP LLM vulnerability and how to test for it.
TutorialGetting Started with LLM Testing
Start testing your LLM applications in under 5 minutes.
Attack Your AI Before Others Do
ArtemisKit automates adversarial testing so you can find and fix vulnerabilities before deployment.