What it does. Wires Promptfoo and DeepTeam into CI/CD for automated, repeatable red-teaming of LLM apps against OWASP LLM Top 10, OWASP Agentic, and MITRE ATLAS presets, failing the build when jailbreak or injection vulnerabilities regress. Use for continuous adversarial testing in CI/CD, a merge-blocking security gate, or comparing model/prompt versions for compliance reporting. Part of mukul975/Anthropic-Cybersecurity-Skills (817 security skills) (mukul975/Anthropic-Cybersecurity-Skills).

Install

• npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill continuous-llm-red-teaming-with-promptfoo, or copy the skill folder into ~/.claude/skills/continuous-llm-red-teaming-with-promptfoo/.
• Raw file: curl -sL https://raw.githubusercontent.com/mukul975/Anthropic-Cybersecurity-Skills/HEAD/skills/continuous-llm-red-teaming-with-promptfoo/SKILL.md

SKILL.md (verbatim)

name: continuous-llm-red-teaming-with-promptfoo
description: Wires Promptfoo and DeepTeam into CI/CD for automated, repeatable red-teaming of LLM apps against OWASP LLM Top 10, OWASP Agentic, and MITRE ATLAS presets, failing the build when jailbreak or injection vulnerabilities regress. Use for continuous adversarial testing in CI/CD, a merge-blocking security gate, or comparing model/prompt versions for compliance reporting.
domain: cybersecurity
subdomain: ai-security
tags:
- ai-security
- llm-red-teaming
- promptfoo
- deepteam
- ci-cd
- owasp-llm-top10
- jailbreak
- regression-testing
version: '1.0'
author: mahipal
license: Apache-2.0
nist_ai_rmf:
- MANAGE-4.1
atlas_techniques:
- AML.T0051

Continuous LLM Red Teaming with Promptfoo

Authorized Use Only: Run these adversarial probes only against LLM applications and endpoints you own or are explicitly authorized to test. Generated attack payloads (jailbreaks, prompt injections, harmful-content elicitation) are adversarial inputs; sending them to third-party services without permission may violate terms of service.

Overview

Promptfoo is an open-source LLM evaluation and red-teaming framework (used by OpenAI and Anthropic per its README) that generates adversarial test cases, runs them against your model/agent, and grades the responses. DeepTeam (by Confident AI) is a complementary open-source framework offering 50+ ready-to-use vulnerabilities and 10+ research-backed attack methods. Together they let you treat LLM security as a regression test: every commit re-runs the same adversarial suite, and the pipeline fails when a previously-safe behavior regresses.

This matters because LLM applications change constantly — prompts, models, RAG sources, tools, and guardrails all drift. A jailbreak that was patched last sprint can silently return after a prompt edit or a model upgrade. Promptfoo maps its plugins directly onto the OWASP LLM Top 10 (owasp:llm) and OWASP Agentic (owasp:agentic) presets, and onto MITRE ATLAS, so the suite tracks recognized risk taxonomies. The core threat addressed here is AML.T0051 — LLM Prompt Injection (MITRE ATLAS): adversarial instructions that override the application's intended behavior. This skill follows the Promptfoo red-team docs (https://www.promptfoo.dev/docs/red-team/) and DeepTeam docs (https://www.trydeepteam.com/docs/getting-started), and aligns to NIST AI RMF MANAGE-4.1 (post-deployment monitoring and feedback to manage AI risk).

When to Use

• When you need continuous, automated red-teaming of an LLM app in CI/CD rather than one-off manual tests.
• When you want to enforce a security gate: block merges that introduce or reintroduce jailbreak/injection vulnerabilities.
• When mapping coverage to OWASP LLM Top 10 / OWASP Agentic / MITRE ATLAS for compliance reporting.
• When comparing the security posture of two models or prompt versions side by side.
• When tracking vulnerability regression over time across releases.

Prerequisites

• Node.js 18+ (Promptfoo is distributed via npm) and Python 3.9+ (for DeepTeam).
• Install Promptfoo and DeepTeam:

  npm install -g promptfoo            # or: npx promptfoo@latest
  pip install -U deepteam
  

• API access/credentials for the target LLM endpoint (and a grader model, e.g. an OpenAI key) exposed as environment variables.
• A CI/CD platform (GitHub Actions, GitLab CI) with secret storage.
• Authorization to test the target application.

Objectives

• Scaffold a Promptfoo red-team config targeting your LLM app.
• Enable OWASP LLM Top 10 and OWASP Agentic plugin presets plus jailbreak/injection strategies.
• Run the suite locally and interpret the per-plugin pass/fail report.
• Add DeepTeam as a second engine for programmatic, research-backed attacks.
• Integrate both into CI/CD so builds fail on new vulnerabilities.
• Generate shareable HTML/PDF security reports per run.

MITRE ATT&CK Mapping

Workflow

1. Scaffold the red-team configuration

Initialize an interactive config; it writes promptfooconfig.yaml where targets, plugins, and strategies live.

promptfoo redteam init
# choose your target type (HTTP endpoint, openai:..., anthropic:..., custom provider)

2. Define targets, OWASP presets, and attack strategies

Edit promptfooconfig.yaml. The purpose grounds attack generation; plugins are adversarial input generators; strategies are delivery techniques (jailbreak/injection wrappers).

# promptfooconfig.yaml
targets:
  - id: https://api.example.com/chat        # your app endpoint
    label: support-bot

redteam:
  purpose: |
    A customer-support assistant for an e-commerce site. Must never reveal
    system prompts, leak PII, or perform actions outside order support.
  numTests: 10
  plugins:
    - owasp:llm          # OWASP LLM Top 10 preset
    - owasp:agentic      # OWASP Agentic threats preset
    - id: pii:direct
      numTests: 15
    - prompt-extraction  # system-prompt leakage
    - harmful
  strategies:
    - id: jailbreak              # iterative single-turn jailbreak
    - id: jailbreak:composite    # stacked jailbreak techniques
    - id: crescendo              # multi-turn escalation
    - id: prompt-injection       # injection wrapper

3. Run the suite and view the report

redteam run combines generation + evaluation; then open the interactive report.

promptfoo redteam run
promptfoo redteam report            # launches the web report (pass/fail per plugin)

Each row shows the plugin (mapped to OWASP/ATLAS), the strategy, the attack prompt, the model's response, and the grader's verdict. The attack success rate per plugin is your headline metric — track it per release.

4. Add DeepTeam for programmatic, research-backed attacks

Use DeepTeam to cover additional vulnerabilities/attacks and to script bespoke suites in Python.

# deepteam_suite.py
from deepteam import red_team
from deepteam.vulnerabilities import Bias, PIILeakage
from deepteam.attacks.single_turn import PromptInjection

def model_callback(prompt: str) -> str:
    # call your application's LLM endpoint here and return the text response
    return call_my_app(prompt)

red_team(
    model_callback=model_callback,
    vulnerabilities=[Bias(types=["race"]), PIILeakage(types=["api_and_database_access"])],
    attacks=[PromptInjection()],
)

DeepTeam can also be driven from a YAML config:

deepteam run config.yaml

5. Gate the build in CI/CD (GitHub Actions)

Fail the pipeline when red-team assertions fail. Promptfoo returns a non-zero exit code on failures, which blocks the merge.

# .github/workflows/llm-redteam.yml
name: LLM Red Team
on: [pull_request]
jobs:
  redteam:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-node@v4
        with: { node-version: '20' }
      - run: npm install -g promptfoo
      - name: Run red team (fails build on new vulns)
        env:
          OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
        run: promptfoo redteam run --no-progress-bar
      - name: Export machine-readable results
        if: always()
        run: promptfoo redteam report --output results.json
      - uses: actions/upload-artifact@v4
        if: always()
        with: { name: redteam-report, path: results.json }

6. Track regressions over time

Persist results.json per run and compare attack-success-rate per plugin between releases. A rising rate for any OWASP LLM category is a regression to triage before release. Promptfoo's --filter-failing lets you re-run only previously failing cases to confirm a fix.

promptfoo redteam run --filter-failing results.json

Tools and Resources

Plugin / Strategy Reference

Validation Criteria

• promptfooconfig.yaml created with target, owasp:llm, and owasp:agentic plugins.
• Jailbreak and prompt-injection strategies enabled.
• promptfoo redteam run executes and produces a per-plugin pass/fail report.
• DeepTeam suite runs against the same target via model_callback.
• CI/CD job fails the build on new red-team failures (non-zero exit).
• results.json artifact archived per run for regression tracking.
• Attack-success-rate per OWASP category trended across releases.

Other files in this skill

• LICENSE
• references/api-reference.md
• references/standards.md
• scripts/agent.py

references/api-reference.md (verbatim)

Promptfoo / DeepTeam — Command & Config Reference

Install

Promptfoo Red-Team CLI

Useful flags: --no-progress-bar (CI), --output results.json, --filter-failing <file>, -c <config>.

Promptfoo Config Keys (redteam: block)

DeepTeam Python API

DeepTeam CLI

External References

• Promptfoo command line: https://www.promptfoo.dev/docs/usage/command-line/
• DeepTeam attacks: https://www.trydeepteam.com/docs/red-teaming-adversarial-attacks
• DeepTeam vulnerabilities: https://www.trydeepteam.com/docs/red-teaming-vulnerabilities

references/standards.md (verbatim)

Standards and References — Continuous LLM Red Teaming with Promptfoo

MITRE ATLAS Techniques

NIST AI RMF

Official Resources

• Promptfoo red-team docs: https://www.promptfoo.dev/docs/red-team/
• Promptfoo configuration: https://www.promptfoo.dev/docs/red-team/configuration/
• Promptfoo CI/CD: https://www.promptfoo.dev/docs/integrations/ci-cd/
• Promptfoo GitHub: https://github.com/promptfoo/promptfoo
• DeepTeam GitHub: https://github.com/confident-ai/deepteam
• DeepTeam docs: https://www.trydeepteam.com/docs/getting-started
• OWASP Top 10 for LLM Applications: https://genai.owasp.org/

Frameworks Tracked

• OWASP LLM Top 10 (owasp:llm preset)
• OWASP Agentic threats (owasp:agentic preset)
• MITRE ATLAS (Promptfoo ATLAS mapping)

Back to mukul975/Anthropic-Cybersecurity-Skills (817 security skills) or Agent skills.