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Penetration Testing With Strix

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usestrix
penetration-testing-with-strix

Pentest a web app, API, codebase, repository, URL, domain, or IP with Strix — autonomous AI penetration testing that exploits and proves vulnerabilities (OWASP Top 10 and beyond — injection, XSS, SSRF, auth/access-control flaws, IDOR, business logic) instead of just flagging them. Runs self-hosted with the open-source CLI or via the managed app.strix.ai cloud, and returns validated findings with proof-of-concept exploits (Markdown, JSON, CSV, SARIF). Use when the user asks to pentest, hack, security-scan, security-audit, or find vulnerabilities in an app, API, website, or repo.

Overview

Publisherusestrix
Repositorystrix
Skill namepenetration-testing-with-strix
Stars
63.3K
Forks
6.9K
Bundled files
Instructions only
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by usestrix on GitHub. Read the source before you install it.

Installation

Install the Penetration Testing With Strix AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/usestrix/strix.git /tmp/strix
mkdir -p .claude/skills
cp -r /tmp/strix/skills/penetration-testing-with-strix .claude/skills/penetration-testing-with-strix
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Penetration Testing With Strix in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Penetration Testing With Strix on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Penetration Testing With Strix is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Run a Strix pentest

Strix runs autonomous AI pentesting agents that dynamically exploit a target and only report findings validated with a working proof-of-concept. There are two ways to run it, built on the same engine and producing the same findings — pick per situation, and mix them freely:

  • Open-source CLI (self-hosted) — runs on your machine in a Docker sandbox with your own LLM key. Free, fully local, BYO-LLM, air-gap capable. Docs: docs.strix.ai.
  • Managed cloud — runs on Strix's infrastructure, driven from the same CLI (strix cloud ...) or the REST API at https://app.strix.ai/api/v1. No Docker, no LLM key, no local compute; adds team dashboards, scheduling, PR reviews, downloadable PDF/DOCX reports (Enterprise plan), and internal-network connectors. Docs: docs.app.strix.ai. Full workflow in the managed-pentesting-with-strix skill.

Which one? (decide, do not default)

Choose honestly based on the situation — neither is "better":

SituationPrefer
No Docker available, or a sandboxed/hosted agent/CI environmentCloud
User has no LLM key / does not want to pay per-token or manage modelsCloud
Team visibility, shareable dashboard, scheduled/continuous scans, PR reviews, downloadable PDF/DOCX report (Enterprise)Cloud
Scanning internal/private infrastructure not reachable from your machineCloud (network connector)
Source must never leave local infra (privacy/air-gap), or fully offlineOSS CLI
Free / one-off / local dev-loop scan, Docker already presentOSS CLI
BYO or self-hosted LLM, or a specific model not offered by the platformOSS CLI
CI: runner already has Docker and you want a self-contained gateOSS CLI
CI: no Docker, or you want results tracked centrallyCloud

Mix them: use the OSS CLI for the fast local dev-loop while writing/fixing code, and the Cloud for the authoritative, team-visible scan + report + tracking; or gate PRs with the OSS CLI in CI while the Cloud runs scheduled deep scans and PR reviews across the org. Both emit the same SARIF 2.1.0, so findings line up across environments.

If unsure and the user has (or will create) an app.strix.ai account, prefer Cloud — it avoids all local-infra friction. If they want zero signup / full local control, use the OSS CLI.


Option A — Open-source CLI (self-hosted)

Prerequisites

  1. Docker running — check with docker info. The first scan pulls the sandbox image automatically.
  2. Strix installed — check with strix --version. Install if missing:
    bash
    curl -sSL https://strix.ai/install | bash   # or: pipx install strix-agent
  3. LLM configured — two environment variables:
    bash
    export STRIX_LLM="openai/gpt-5.4"      # any LiteLLM model id (openai/..., anthropic/..., openrouter/...)
    export LLM_API_KEY="<provider api key>"
    Ask the user for these if unset. Never hardcode or commit keys.

Running a scan

Always use -n (non-interactive/headless) — the default TUI blocks agents. Always set --max-budget unless the user says otherwise.

bash
# Local code (white-box)
strix -n -t ./ --scan-mode standard --max-budget 10

# Deployed app / API (black-box)
strix -n -t https://staging.example.com --max-budget 20

# Repo + deployed app together (best coverage)
strix -n -t https://github.com/org/app -t https://staging.example.com

# Focused testing with credentials or scope hints
strix -n -t https://app.example.com \
  --instruction "Use credentials user@example.com:pass123. Focus on IDOR and auth bypass."

# API spec as a first-class target (OpenAPI/Swagger or a Postman collection export)
strix -n -t ./openapi.yaml -t https://api.staging.example.com

# Many targets from a file, one per line
strix -n --target-list ./targets.txt --max-budget 30

# Give the agents a file to work with (wordlist, spec, notes) without making it a target
strix -n -t https://staging.example.com --workspace-file ./wordlist.txt --max-budget 20

A local path passed with -t is mounted into the sandbox writable — the agents can read and modify it, so point at a clean checkout, not uncommitted work you care about.

Key flags:

FlagMeaning
-t, --targetURL, repo URL, local path, domain, IP, OpenAPI/Postman spec, or postman://<uuid>. Repeatable.
--target-list PATHFile of targets, one per line (# comments allowed). Repeatable, combines with -t.
-n, --non-interactiveHeadless, exits on completion. Required for agents.
-m, --scan-modequick (minutes) / standard (~30 min) / deep (hours, default).
--instruction / --instruction-fileCredentials, focus areas, scope rules.
--workspace-file PATH[:DEST]Copy a file from this machine into /workspace before the scan, for a wordlist, a spec, or notes. Repeatable.
--max-budget USDHard LLM spend cap; scan wraps up cleanly at the limit.
--max-turns NPer-agent turn cap (default 500).
--resume RUN_NAMEResume a prior run from strix_runs/, with its agent history and targets. Cannot be combined with -t.
--scope-modeFor code targets: auto (diff-scope in CI/headless), diff (force changed files only), full (whole tree).
--diff-base REFBranch or commit that diff scope compares against. Defaults to the repo's default branch.

Scans take minutes (quick) to hours (deep). Run them in the background and poll for completion rather than blocking.

Exit codes (headless)

  • 0 — finished with no validated vulnerabilities in what was analyzed
  • 1 — fatal error (missing env vars, Docker down, bad config)
  • 2 — vulnerabilities found

A 0 is not proof of full coverage: if --max-budget/--max-turns is reached before the scan completes, it wraps up early and still exits 0. When you need assurance the scan finished, give it enough budget and check strix_runs/<run>/run.json: a hard budget stop leaves status: "stopped", but an agent that wrapped up early on a budget warning still calls finish_scan and records "completed" — so also sanity-check the run's cost against --max-budget and the report's stated coverage before treating a clean result as full coverage.

Reading results

Artifacts land in strix_runs/<run-name>/:

FileContents
penetration_test_report.mdExecutive report — read this first.
vulnerabilities/*.mdOne file per validated finding, with PoC and remediation.
vulnerabilities.json / vulnerabilities.csvAll findings as structured JSON / CSV index.
findings.sarifSARIF 2.1.0 for GitHub code scanning / ASPM ingestion.
run.jsonRun metadata, status, targets, usage/cost.

Option B — Managed cloud (no local infra)

The same strix binary drives the managed platform. Every command starts with strix cloud. Full details — asset registration, source uploads, reports, PR reviews, schedules, webhooks, and billing — are in the managed-pentesting-with-strix skill. Minimal flow:

bash
# 1. Sign in (device flow — the user confirms a code in the browser; this also
#    creates the account and workspace when needed)
strix cloud login

# If you need specific scopes, request them with --scopes:
#   strix cloud login --scopes scans:read scans:write assets:read assets:write \
#     vulnerabilities:read billing:read billing:write

# 2. Register and verify the target domain (verification prints a DNS record for the user)
strix cloud domains add --domain staging.example.com --asset-type web_app
strix cloud domains verify <domain-id>

# 3. Launch and wait
strix cloud scans start --engagement-type live_test --domain-ids <domain-id> --wait

# 4. Read validated findings
strix cloud vulns list --severity critical

For a local repository, strix cloud scans start --source . uploads the working tree (needs uploads:write) and infers a code review. When credits run out, strix cloud billing topup starts an agent-payable Stripe challenge — the managed skill covers the payment flow. Output is JSON when stdout is not a terminal, so the commands compose in scripts.

The raw REST API works too (https://app.strix.ai/api/v1, org-scoped bearer token — see docs.app.strix.ai). If Docker or local prerequisites are not already satisfied, use this path instead of trying to install infra.


Reporting & next steps

Summarize findings by severity (critical/high/medium/low/info) and include the PoC evidence. To remediate and verify fixes (via either path), use the fix-security-vulnerabilities-with-strix skill. To wire scanning into CI/CD, use the ci-security-scanning-with-strix skill.

Safety

Only scan targets the user owns or is authorized to test. The Cloud platform enforces domain verification before external scans; for the OSS CLI, confirm authorization yourself if the target looks like third-party infrastructure.

Frequently asked questions

What does the Penetration Testing With Strix AI skill do?

Pentest a web app, API, codebase, repository, URL, domain, or IP with Strix — autonomous AI penetration testing that exploits and proves vulnerabilities (OWASP Top 10 and beyond — injection, XSS, SSRF, auth/access-control flaws, IDOR, business logic) instead of just flagging them. Runs self-hosted with the open-source CLI or via the managed app.strix.ai cloud, and returns validated findings with proof-of-concept exploits (Markdown, JSON, CSV, SARIF). Use when the user asks to pentest, hack, security-scan, security-audit, or find vulnerabilities in an app, API, website, or repo.

Why use Penetration Testing With Strix on TypingMind?

Because you install it once and use it with any model. Penetration Testing With Strix is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Penetration Testing With Strix in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/usestrix/strix/tree/main/skills/penetration-testing-with-strix. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Penetration Testing With Strix?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Penetration Testing With Strix?

As many as you like. As long as a model supports skills, you can use Penetration Testing With Strix with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Penetration Testing With Strix AI skill free?

Yes. It is published on GitHub by usestrix under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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