MCPInstall

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.

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Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/usestrix/strix --skill penetration-testing-with-strix
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads penetration-testing-with-strix automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

Security audits

Independent scans of the skill's instructions and bundled files.

Gen Agent Trust Hub FailHIGH ·The skill facilitates autonomous penetration testing using the Strix tool. It includes installation via a shell script from the vendor's official domain and utilizes a CLI to scan external targets. While the remote execution originates from a vendor-owned source, the processing of untrusted external data (URLs, repositories, and API specs) creates a potential surface for indirect prompt injection.
Socket Warn1 alert: gptSecurity
Snyk WarnMEDIUM ·Risk: MEDIUM · 1 issue

SKILL.md

The instructions your agent reads when the skill is triggered.

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:
    curl -sSL https://strix.ai/install | bash   # or: pipx install strix-agent
    
  3. LLM configured — two environment variables:
    export STRIX_LLM="openrouter/z-ai/glm-5.3"      # 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.

# 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).
--fail-on SEVERITYHeadless only: exit 2 only for findings at or above critical/high/medium/low/info. Default: any finding.
-r, --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 (with --fail-on, none at or above the threshold; lower ones are still in the artifacts)
  • 1 — fatal error (missing env vars, Docker down, bad config)
  • 2 — vulnerabilities found (with --fail-on, at least one at or above that severity)

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:

# 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.

FAQ

What does the penetration-testing-with-strix 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.

How do I install penetration-testing-with-strix?

Run npx skills add https://github.com/usestrix/strix --skill penetration-testing-with-strix in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/penetration-testing-with-strix/ (personal) or .claude/skills/penetration-testing-with-strix/ (project).

Which AI agents can use penetration-testing-with-strix?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is penetration-testing-with-strix safe to install?

Security partners have reviewed it: Gen Agent Trust Hub flagged risks; Socket flagged items to review; Snyk flagged items to review. As with any skill, read the SKILL.md and bundled scripts before installing.

Who maintains penetration-testing-with-strix?

It is published by usestrix in usestrix/strix. It has 16,910 recorded installs.

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