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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.

DeepseekModel キュレーション済みスキル 品質 優秀 · 90 v1.0.0

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https://deepseekmodel.com/api/download.php?id=usestrix-strix-skills-penetration-testing-with-strix-skill-md&format=skill
ダウンロード .skill 標準形式。system_prompt と model_config を収録し、任意の Agent で利用可能
.skill ファイルの system_prompt フィールドの実際の内容。
name penetration-testing-with-strix description 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. license Apache-2.0 metadata {"author":"usestrix","homepage":"https://docs.strix.ai"} 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": Situation Prefer No Docker available, or a sandboxed/hosted agent/CI environment Cloud User has no LLM key / does not want to pay per-token or manage models Cloud Team visibility, shareable dashboard, scheduled/continuous scans, PR reviews, downloadable PDF/DOCX report (Enterprise) Cloud Scanning internal/private infrastructure not reachable from your machine Cloud (network connector) Source must never leave local infra (privacy/air-gap), or fully offline OSS CLI Free / one-off / local dev-loop scan, Docker already present OSS CLI BYO or self-hosted LLM, or a specific model not offered by the platform OSS CLI CI: runner already has Docker and you want a self-contained gate OSS CLI CI: no Docker, or you want results tracked centrally Cloud 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 Docker running — check with docker info . The first scan pulls the sandbox image automatically. Strix installed — check with strix --version . Install if missing: curl -sSL https://strix.ai/install | bash # or: pipx install strix-agent LLM configured — two environment variables: 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. # 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: Flag Meaning -t, --target URL, repo URL, local path, domain, IP, OpenAPI/Postman spec, or postman://<uuid> . Repeatable. --target-list PATH File of targets, one per line ( # comments allowed). Repeatable, combines with -t . -n, --non-interactive Headless, exits on completion. Required for agents. -m, --scan-mode quick (minutes) / standard (~30 min) / deep (hours, default). --instruction / --instruction-file Credentials, focus areas, scope rules. --workspace-file PATH[:DEST] Place a file from this machine into /workspace read-only before the scan, for a wordlist, a spec, or notes. Repeatable. --max-budget USD Hard LLM spend cap; scan wraps up cleanly at the limit. --max-turns N Per-agent turn cap (default 500). --resume RUN_NAME Resume a prior run from strix_runs/ , with its agent history and targets. Cannot be combined with -t . --scope-mode For code targets: auto (diff-scope in CI/headless), diff (force changed files only), full (whole tree). --diff-base REF Branch 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>/ : File Contents penetration_test_report.md Executive report — read this first. vulnerabilities/*.md One file per validated finding, with PoC and remediation. vulnerabilities.json / vulnerabilities.csv All findings as structured JSON / CSV index. findings.sarif SARIF 2.1.0 for GitHub code scanning / ASPM ingestion. run.json Run 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.
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ダウンロードした .skill に含まれるフィールド。
フィールド 説明
formatフォーマット識別子(skill/v1)
skill_idスキル固有 ID
nameスキル名
versionバージョン
description説明
categoryカテゴリ(配列)
trigger_wordsトリガーワード
tagsタグ
sourceソース
source_urlソース URL(本ページ)
exported_atエクスポート日時(ダウンロード毎)
system_promptシステムプロンプト本文
model_configモデル設定:provider / model / temperature / max_tokens / top_p
examplesサンプル
install_guide各プラットフォームの導入説明(Coze / Dify / Claude / カスタム)
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.skill 標準形式。system_prompt と model_config を収録し、任意の Agent で利用可能 ダウンロード
.skillpro 拡張形式。scripts / tools / dependencies / hooks を含む ダウンロード
.json 純粋な JSON 出力。system_prompt とモデル設定のみ ダウンロード
Coze frontmatter 付き Markdown。Coze へのインポート用 ダウンロード
Dify Dify DSL。アプリ作成後にそのままインポート ダウンロード

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