
Continuous LLM Red Teaming
FreeAutomate security testing for LLM applications in CI/CD.
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
View source1. Install with the skills CLI
npx skills add mukul975/anthropic-cybersecurity-skills/continuous-llm-red-teaming-with-promptfoo --agent claude-code2. 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 mukul975Continuous 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
| ID | Name (MITRE ATLAS) | Tactic |
|---|---|---|
| AML.T0051 | LLM Prompt Injection | Initial Access / Persistence (LLM) |
| AML.T0051.000 | Direct (Prompt Injection) | LLM Attack |
| AML.T0051.001 | Indirect (Prompt Injection) | LLM Attack |
| AML.T0054 | LLM Jailbreak | Privilege 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
| Resource | Link |
|---|---|
| Promptfoo red-team docs | https://www.promptfoo.dev/docs/red-team/ |
| Promptfoo red-team configuration | https://www.promptfoo.dev/docs/red-team/configuration/ |
| Promptfoo CI/CD integration | https://www.promptfoo.dev/docs/integrations/ci-cd/ |
| Promptfoo MITRE ATLAS mapping | https://www.promptfoo.dev/docs/red-team/mitre-atlas/ |
| DeepTeam (Confident AI) | 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/ |
Plugin / Strategy Reference
| Promptfoo item | Type | Maps to |
|---|---|---|
owasp:llm | preset | OWASP LLM Top 10 suite |
owasp:agentic | preset | OWASP Agentic threats |
prompt-extraction | plugin | LLM07 system-prompt leakage |
pii:direct | plugin | LLM06 sensitive-info disclosure |
harmful | plugin | harmful content generation |
jailbreak / jailbreak:composite | strategy | AML.T0054 LLM jailbreak |
crescendo | strategy | multi-turn jailbreak |
prompt-injection | strategy | AML.T0051 prompt injection |
Validation Criteria
-
promptfooconfig.yamlcreated with target,owasp:llm, andowasp:agenticplugins. - Jailbreak and prompt-injection strategies enabled.
-
promptfoo redteam runexecutes 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.jsonartifact 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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