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Module 3 of 4 · 50 min

Automated Red-Teaming & Vulnerability Scanners

Deploy automated red-teaming frameworks (PyRIT, Giskard, Promptfoo) to continuously discover vulnerabilities in AI deployments.

Core concept

By the end

You will be able to

  • Architect continuous automated red-teaming harnesses integrated into CI/CD pipelines.
  • Configure PyRIT and Promptfoo suites targeting PII leakage, prompt extraction, and tool hijacking.
  • Generate executive vulnerability reports and CVSS-style AI risk ratings.
01

Continuous Red-Teaming in CI/CD

Manual penetration testing cannot keep pace with frequent model updates, prompt revisions, and third-party tool changes. Automated red-teaming uses red-team orchestrator LLMs to dynamically generate adversarial probes and score target responses.

Promptfoo Red-Teaming Configuration
yaml
targets:
  - id: https://api.enterprise.internal/v1/agent
    config:
      apiKey: ${{ secrets.AGENT_API_KEY }}
tests:
  - plugin: prompt-injection
  - plugin: pii:direct
  - plugin: bfla
  - plugin: ssrf
assert:
  - type: guardrail
    metric: safety_violation_rate
    threshold: 0.00

Practice activity

Execute Automated Red-Teaming Suite Against an AI Endpoint

  1. Configure an automated red-teaming run with 50 adversarial attack scenarios.
  2. Execute test harness against a mock enterprise banking assistant.
  3. Generate a categorized vulnerability finding report with remediation patches.

What to produce

  • Vulnerability scorecard and attack transcript logs.

Reflect before continuing

How does continuous red-teaming in CI differ from traditional static code security analysis?

Evidence

Sources and verification

Knowledge check

Make it stick.

Pass at 80%

Choose the strongest answer for each question. Your attempts become part of your account transcript.

01What is the primary benefit of running automated LLM red-teaming suites in CI pipelines?