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Continuous LLM Red Teaming

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Automate security testing for LLM applications in CI/CD.

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Free · Opens the source repo

What Continuous LLM Red Teaming does

Continuous LLM Red Teaming with Promptfoo integrates adversarial testing into your CI/CD pipeline, ensuring that your LLM applications remain secure against known vulnerabilities. By utilizing the capabilities of Promptfoo and DeepTeam, this skill allows for automated, repeatable testing against the OWASP LLM Top 10 and other recognized security frameworks. The skill enables developers to run adversarial probes that can identify regressions in security, such as jailbreaks and prompt injections, that may arise from changes in model prompts or updates.

The integration with CI/CD means that security is treated as a continuous process rather than a one-off task. Each commit triggers the red-teaming suite, which generates adversarial test cases and evaluates the model's responses. If any vulnerabilities are detected, the build fails, preventing potentially insecure code from being merged. This approach not only enhances security but also provides compliance reporting against established standards, making it easier for organizations to demonstrate their commitment to secure AI practices.

This skill is particularly valuable for teams developing LLM applications that require ongoing security assurance. By automating the testing process, developers can focus on building features while maintaining a robust security posture. With the ability to compare different model versions and track vulnerability regressions over time, teams can ensure that their applications remain resilient against emerging threats.

In essence, Continuous LLM Red Teaming with Promptfoo is a critical tool for developers and security professionals aiming to safeguard their LLM applications against adversarial attacks, ensuring that security is an integral part of the development lifecycle.

When to use it

Use this skill when you need to implement continuous security testing for LLM applications within a CI/CD workflow.

When not to use it

This skill may not be suitable for one-off manual testing scenarios or for applications lacking a CI/CD pipeline.

What you can build with it

Automated CI/CD Security Testing

Integrate continuous red-teaming into your CI/CD pipeline to ensure LLM applications are secure against vulnerabilities.

Compliance Reporting

Generate reports that map your LLM application's security posture to OWASP LLM Top 10 and other standards.

Vulnerability Regression Tracking

Monitor and track the security posture of your LLM applications over time, identifying any regressions that occur with updates.

How to install Continuous LLM Red Teaming

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1. Install with the skills CLI

npx skills add mukul975/anthropic-cybersecurity-skills/continuous-llm-red-teaming-with-promptfoo --agent claude-code

2. Or install it manually

Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.

Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs

Inside SKILL.md

Written by mukul975

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

IDName (MITRE ATLAS)Tactic
AML.T0051LLM Prompt InjectionInitial Access / Persistence (LLM)
AML.T0051.000Direct (Prompt Injection)LLM Attack
AML.T0051.001Indirect (Prompt Injection)LLM Attack
AML.T0054LLM JailbreakPrivilege Escalation / Defense Evasion (LLM)

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

ResourceLink
Promptfoo red-team docshttps://www.promptfoo.dev/docs/red-team/
Promptfoo red-team configurationhttps://www.promptfoo.dev/docs/red-team/configuration/
Promptfoo CI/CD integrationhttps://www.promptfoo.dev/docs/integrations/ci-cd/
Promptfoo MITRE ATLAS mappinghttps://www.promptfoo.dev/docs/red-team/mitre-atlas/
DeepTeam (Confident AI)https://github.com/confident-ai/deepteam
DeepTeam docshttps://www.trydeepteam.com/docs/getting-started
OWASP Top 10 for LLM Applicationshttps://genai.owasp.org/

Plugin / Strategy Reference

Promptfoo itemTypeMaps to
owasp:llmpresetOWASP LLM Top 10 suite
owasp:agenticpresetOWASP Agentic threats
prompt-extractionpluginLLM07 system-prompt leakage
pii:directpluginLLM06 sensitive-info disclosure
harmfulpluginharmful content generation
jailbreak / jailbreak:compositestrategyAML.T0054 LLM jailbreak
crescendostrategymulti-turn jailbreak
prompt-injectionstrategyAML.T0051 prompt injection

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.

Frequently asked questions about Continuous LLM Red Teaming

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