pentest-agents

作者 H-mmer已验证

Bug bounty agent framework for Claude Code, Codex, Gemini, Cursor, Windsurf, Copilot, and OpenClaw — 48 agents, 26 commands, 19 CLI tools, 2 MCP servers, autonomous hunt loops, exploit chain builder.

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Python
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2026/8/23
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⚠️ 第三方软件声明

本 Skill 为第三方开源软件,独立托管于 GitHub。SkillTip 仅为信息目录,不控制或维护底层仓库。所显示的安全检查为自动化且范围有限,安装前请自行审查源码。

阅读服务条款

安装

添加到你的 Claude Code skills 目录:

# Add to your Claude Code skills
git clone https://github.com/H-mmer/pentest-agents

快速入门

使用 pentest-agents 等 Skills 的指南。

安全报告

已验证

上次扫描:—

{
  "status": "PASSED",
  "issues": []
}

README.md

Pentest Agent Suite

Pentest Agent Suite for Claude Code

Autonomous bug-bounty framework for Claude Code and 6 other AI coding tools — 50 agents, 26 commands, 19 CLI tools, 11 skills, 2 MCP servers.

Python 3.10+ Claude Code MCP servers 50 agents Payloads 7 IDE targets


~760 files · ~118k lines · 50 agents · 26 commands · 19 CLI tools · 11 skills · 2 MCP servers (16 bug-bounty platforms + BYO writeup search) · 2,500 payload lines

A complete bug bounty framework. Battle-tested hunting methodology with concrete payloads, 7-Question Gate validation, autonomous hunt loops, A→B exploit chain building, persistent brain with endpoint tracking, optional semantic writeup search (bring your own index), automatic cost tracking via CC hooks, live platform integration, and a cross-IDE installer that emits the native format for Claude Code, Codex, Gemini, Cursor, Windsurf, VS Code Copilot, and OpenClaw.

Quick Start

# MCP servers are launched via `uv run --with mcp` — no global pip install required.
export HACKERONE_USERNAME=you HACKERONE_TOKEN=your_token
uv run python3 tools/scaffold.py hackerone tesla
cd ~/bounties/hackerone-tesla && claude
/model opus             # Opus 4.7 [1M] — subagents inherit via model: "inherit"
/sync hackerone tesla
/brain init && /status
/hunt tesla.com

scaffold.py provisions the workspace for every supported project-scoped client, not only Claude Code: CLAUDE.md, AGENTS.md, .codex/, .agents/skills/, .gemini/, .cursor/, .windsurf/, .github/, and .vscode/mcp.json are generated from the copied workspace assets so paths resolve inside the bounty workspace.

Install (Claude Code + 6 other AI coding tools)

The framework ships pre-rendered for every supported tool. There are two ways to use it:

1. Use the bundles directly (no install step)

git clone https://github.com/H-mmer/pentest-agents-suite
cd pentest-agents-suite/pentest-agents/providers/codex
codex                       # or: cd ../gemini && gemini, etc.

The providers/<id>/ tree contains a fully-translated, ready-to-use bundle for each non-Claude target. Path references inside use .. to reach the repo's tools/, rules/, and mcp-*-server/ — so the bundle works as long as it stays inside the cloned repo.

2. Run the installer (writes into your own project or ~/.codex/ etc.)

python3 -m tools.installer install --targets all --scope project
python3 -m tools.installer install --targets codex --scope global

Install mode rewrites paths to absolute references back into the cloned pentest-agents repo, so the install works no matter where the user's own project lives.

TargetAgentsSlash commandsRulesMCPScopes
Claude Codenative .claude/agents/*.md.claude/skills/<name>/SKILL.mdCLAUDE.md.mcp.json / ~/.claude.jsonglobal + project
OpenAI Codexnative .codex/agents/*.toml.agents/skills/<name>/SKILL.mdAGENTS.md (≤32 KiB)[mcp_servers.*] in config.tomlglobal + project
Google Gemininative .gemini/agents/*.mdTOML in .gemini/commands/GEMINI.mdmcpServers in settings.jsonglobal + project
Cursor→ skills .cursor/skills/agent-*/SKILL.md (no native subagents)→ skills .cursor/skills/cmd-*/SKILL.md.cursor/rules/*.mdc + AGENTS.md.cursor/mcp.jsonglobal + project
Windsurf→ skillsWorkflows.windsurf/rules/*.md (≤12 KiB / file)~/.codeium/windsurf/mcp_config.jsonglobal + project
VS Code Copilot.github/agents/*.agent.md (≤30 KiB / agent).github/prompts/*.prompt.md.github/copilot-instructions.md + .github/instructions/*.vscode/mcp.jsonproject + global-MCP
OpenClaw→ skills→ skills~/.openclaw/workspace/AGENTS.md or <proj>/AGENTS.mdmcp.servers in ~/.openclaw/openclaw.jsonglobal + project (MCP is user-level)

Cursor, Windsurf, and OpenClaw have no native subagent concept; Claude-format agents render as skills/rules. Codex commands are emitted as AgentSkills under .agents/skills/; the deprecated .codex/prompts/ path is not used.

providers/ directory (in the cloned repo):

providers/
├── codex/    AGENTS.md + .codex/{agents,config.toml} + .agents/skills
├── gemini/   GEMINI.md + .gemini/{agents,commands} + settings.json
├── cursor/   AGENTS.md + .cursor/{rules,skills,mcp.json}
├── windsurf/ AGENTS.md + .windsurf/{rules,workflows,skills} + mcp_config.json
├── copilot/  .github/{copilot-instructions.md,instructions,prompts,agents} + .vscode/mcp.json
└── openclaw/ AGENTS.md + .agents/skills/ + openclaw.json

providers/ is generated, not edited by hand. Re-render after editing .claude/, rules/, or skills/ source:

python3 -m tools.installer render --targets all
python3 -m tools.installer render --check        # exits 1 if drift

The test_committed_providers_match_render pytest case enforces drift detection locally — there is no GitHub Actions CI by project policy.

What gets translated

When .claude/ content is rendered for non-Claude targets, the translator:

  • Drops the model: field — each target uses its own default model.
  • Strips Claude-specific prose — "Claude Code" → "the AI coding tool", "the Agent tool" → "the subagent dispatch tool", model: "inherit" is removed entirely.
  • Rewrites $CLAUDE_PROJECT_DIR — to .. in providers/ (relative to the cloned repo), or to absolute paths into the cloned source repo when installing into a user's project.
  • Maps effort: frontmatter to model_reasoning_effort in Codex TOML.
  • Caps body length — Copilot agents are truncated at 30,000 chars (Copilot's hard limit). Windsurf rules are chunked at 12,000 chars (workspace) / 6,000 chars (global).
  • Adds Copilot subagent links — orchestrator agents (chain-builder, correlator, recon-ranker) get an agents: list of siblings so Copilot wires the dispatch graph.

Installer management

pentest-agents list                      # detect which targets are installed
pentest-agents install --targets claude_code,codex --scope global
pentest-agents install --dry-run         # preview every file + JSON merge
pentest-agents verify                    # check manifest vs. disk (drift)
pentest-agents uninstall                 # reverse, restore .pa-backup files
pentest-agents render --targets all      # regenerate providers/<id>/
pentest-agents render --check            # drift gate (exit 1 if dirty)

Every install records a manifest (.pentest-agents/manifest.json for project scope, ~/.config/pentest-agents/manifest.json for global). Uninstall only removes files we wrote and surgically strips only the MCP/JSON keys we merged — your other settings are never touched. Conflicting writes back up the original as <path>.pa-backup and are restored on uninstall.

Workflow

New program:   /new → /sync → /brain init → /analyze → /surface → /hunt
Returning:     /resume <target> → /hunt or /autopilot
After finding: /validate → /chain → /report → /dupcheck → /submit → /learn
Batch triage:  /triage (7-Question Gate on all findings)

MCP Servers (2)

bounty-platforms (16 platforms)

HackerOne (full API), Bugcrowd, Intigriti, Immunefi (public), YesWeHack + 11 stubs. 7 MCP tools: list_platforms, get_program_scope, get_program_policy, search_hacktivity, sync_program, draft_report, submit_report.

writeup-search (BYO index)

Searchable knowledge base agents query during hunting and validation. 4 MCP tools:

  • search_writeups — semantic search (FAISS) or keyword search for prior art
  • get_writeup — full writeup content by ID
  • search_techniques — exploitation techniques by vuln class
  • search_payloads — curated payloads from rules/payloads.md

The writeup index is not bundled. Bulk-redistributing scraped hacktivity violates most platform ToS, so this repo ships the server only. The search_payloads + search_techniques fallback works out of the box; the semantic/keyword layers activate once you point the server at your own index.

Three search modes (auto-detected, graceful fallback):

ModeRequiresSearches
FAISS (semantic)faiss-cpu, sentence-transformers, your metadata.db + index.faissYour writeup corpus via vector embeddings
SQLite (keyword)Your metadata.db onlyYour writeup corpus via LIKE over the text column
Local (default)Nothing — zero depsrules/payloads.md + skills/ shipped in this repo

Point the server at your index by dropping metadata.db (+ optionally index.faiss) into ~/.local/share/pentest-writeups/, or set WRITEUP_DB_DIR=/path/to/dir.

Expected schema (metadata.db): a SQLite file with at least one table containing columns id, title, url, and one text column (content / text / body / writeup). Row order in the table must match vector order in index.faiss when using semantic mode.

Build your own index — rag-builder/

The repo now ships a local RAG/FAISS builder under rag-builder/ that turns a list of GitHub / GitLab repositories into a metadata.db + index.faiss pair the writeup-search MCP server consumes. Destructive operations (clone, embed, write) are always gated behind --execute — running the CLI without it prints the plan and changes nothing, so you can never wipe an existing index by accident.

cd rag-builder

# 1. Inspect the plan — no network, no writes.
python3 build.py status
python3 build.py ingest                    # dry-run (the default)

# 2. Opt-in pre-flight: probe every URL with `git ls-remote` (network).
python3 build.py ingest --check-remotes    # ~5s for 141 repos at 16 workers

# 3. Actually clone + index every repo from repos.yaml into ./data/.
python3 build.py ingest --execute
python3 build.py ingest --execute --check-remotes   # skip unreachable first

# 4. Point the MCP server at the output.
export WRITEUP_DB_DIR="$PWD/data"
python3 ../mcp-writeup-server/server.py --test

rag-builder/repos.yaml ships with a 146-entry seed covering CTF archives, bug-bounty reports, payload collections, and research aggregators — edit freely. repos-skipped.yaml is loaded automatically as an exclusion list (override with --skip-list or --no-skip-list). config.yaml controls the embedding model (all-MiniLM-L6-v2 by default), host allowlist, clone size cap, and file-size ceiling. See rag-builder/README.md for the full reference.

CC Hooks (automatic cost tracking)

Configured in settings.json, fires automatically:

  • SubagentStopcost_hook.py logs agent name + session to cost-tracking.json
  • Stop → logs session end
  • SessionStart → welcome message

Statusline shows live cost from session token data: $0.57

Commands (26)

Hunting & Analysis

CommandDescription
/hunt <target> [--vuln-class]Active hunting — searches writeup DB for techniques first, then tests with concrete payloads
/autopilot <target>Autonomous loop with --paranoid/--normal/--yolo checkpoints
/surface <target>P1/P2/Kill ranked attack surface
/chainBuild A→B→C exploit chains via chain-builder agent (9 capability rows + 4 documented deep chains in rules/chain-table.md)
/analyze <target>AI analysis: crown jewels, attack paths, blind spots
/mindmap <target>Attack surface tree with brain status
/sast <repo>Source-code vulnerability hunting (entry → flow → gap → exploit pipeline)

Validation & Reporting

CommandDescription
/validate <finding>7-Question Gate → PASS/KILL/DOWNGRADE/CHAIN REQUIRED
/triageBatch-validate ALL findings, kill weak ones
/quality <draft>Score report 1-10 (blocks below 7)
/report [format]Reports (hard gate: requires /validate PASS)
/dupcheck <desc>Hacktivity + writeup DB for duplicates
/submit <finding>Submit (hard gate: /validate PASS + /quality ≥ 7)

Session & Memory

CommandDescription
/resume <target>Resume — untested endpoints + suggestions
/rememberLog finding/pattern for cross-target learning
/learn <id> <status>Record response — auto-boosts paid techniques
/braininit, brief, status, endpoint, endpoints, record, exhausted

Infrastructure

CommandDescription
/new, /sync, /statusSetup + dashboard
/pipeline, /quickscan, /fullscanScanning pipelines
/correlateChain discovery across findings
/cost, /monitorCost tracking, target change detection

Agents (50)

H1 Weakness Specialists (19)

xss-hunter (#60/#61/#62), sqli-hunter (#67), csrf-hunter (#57), ssrf-hunter (#75), ssti-hunter (#74), idor-hunter (#55), auth-tester (#27), info-disclosure (#18), open-redirect (#38), rce-hunter (#70), xxe-hunter (#63), file-upload (#39), cors-hunter (#58), subdomain-takeover (#145), business-logic (#28), race-condition (#29), privilege-escalation (#26), oauth-hunter (#1/#22/#106/#137), llm-ai-hunter (chains under #18/#55/#61/#70/#106)

Hunting & Analysis (3)

  • validator — 7-Question Gate + never-submit list (PASS/KILL/DOWNGRADE/CHAIN)
  • chain-builder — A→B chain walk against the capability table, searches writeup DB for proven chains
  • recon-ranker — P1/P2/Kill surface ranking

Infrastructure / Recon (10)

recon, vuln-scanner, config-auditor, cloud-recon, js-analyzer, waf-profiler, graphql-audit, nuclei-writer, browser-agent (Burp MCP), browser-stealth-agent (Camoufox)

Meta / Validation (9)

brain, correlator, quality-check, monitor, poc-builder, report-writer, scope-check, browser-verifier (client-side PoC proof), dast-devils-advocate (adversarial downgrade)

SAST Pipeline (8)

sast-file-ranker, sast-entry-mapper, sast-danger-mapper, sast-flow-tracer, sast-gap-analyzer, sast-devils-advocate, sast-hunter, sast-exploit-builder

Specialized (1)

web3-auditor — Solidity grep arsenal, Foundry PoC, DeFi patterns

Hunting Skills (5 deep methodology skills + 6 reference skills = 11)

The hunt-* skills are vuln-class-specific methodology files distilled from public bug-bounty reports. Each has a verified 2024-2026 CVE catalog and sub-techniques. The matching specialist agent reads its skill via Read $CLAUDE_PROJECT_DIR/skills/hunt-<class>/SKILL.md before testing.

SkillLinesPairs WithHighlights
skills/hunt-rce/SKILL.md1,135rce-hunter1,218-report distillation. RSC CVE-2025-55182, runc Leaky Vessels, BentoML pickle, LangChain REPL, Tekton/OpenProject git arg injection, ingress-nginx, container/runtime, ML serving, agentic LLM tool-use, OSS supply chain
skills/hunt-idor/SKILL.md969idor-hunter1,117-report distillation. Sam Curry automotive chain, OneUptime CVE-2026-30956, Zitadel V2Beta/Mgmt API, Inforcer tenant enum, Apache Answer UUIDv1 prediction, Indico BOLA, GraphQL field-level pivots, agentic AI cross-tenant
skills/hunt-xss/SKILL.md968xss-hunterDOMPurify mXSS family, Auth0 nextjs-auth0 returnTo, RSC DoS family, markdown-to-jsx, listmonk admin-ATO, Trix rich-text editor (H1 #2819573 / #2521419), Jupyter notebook XSS (GHSA-rch3-82jr-f9w9), n8n MCP OAuth XSS (GHSA-537j-gqpc-p7fq), LinkedIn-class iframe-in-article (H1 #2212950), 10 sub-techniques (A-J), Semgrep / ast-grep / ripgrep / CodeQL patterns
skills/hunt-oauth/SKILL.md770oauth-hunter365-report distillation. ruby-saml parser differentials, Authentik regex redirect_uri, workers-oauth-provider PKCE downgrade, Entra ID actor token, Hono JWT alg confusion, nOAuth, Tekton token exfil, Argo CD project token, tinyauth
skills/hunt-llm-ai/SKILL.md930llm-ai-hunterOWASP LLM Top 10 v2025 + Agentic AI Top 10. Microsoft 365 Copilot ASCII Smuggling, LangChain GmailToolkit indirect injection (CVE-2025-46059), LangChain PythonREPLTool semantic RCE (CVE-2025-68613), BentoML pickle, Ollama RCE family, Open WebUI SSE injection, MLflow path traversal

Reference skills (read by methodology-aware agents): hunting-methodology, recon-methodology, report-writing, sast-methodology, triage-validation, vuln-classes.

CLI Tools (19)

ToolPurpose
brain.pyBrain with endpoint tracking + circuit breaker
intel_engine.pyHacktivity patterns + tech→vuln mapping
journal.pyJSONL session journal for /resume
target_selector.pyProgram ROI ranking
cost_hook.pyCC hook: auto-logs agent completions via SubagentStop
statusline.pyDashboard (--compact/--watch/--json)
scope_check.pyScope validation with --list
scope_hook.pyPreToolUse hook: blocks out-of-scope Bash commands (exact + wildcard)
cvss_version_guard.pyEnforces H1 = CVSS 3.1, other platforms = CVSS 4.0
file_path_guard.pyBlocks hallucinated file paths in reports
file_safety.pyShared safety checks for agent-written files
dedup_findings.pyDedup + hacktivity cross-reference
global_brain.pyCross-engagement knowledge (incremental hash-based sync)
response_tracker.pyResponse learning + auto-boost paid techniques
scaffold.pyWorkspace scaffolding with update mode
capture.pyScreenshots + video (WSL2)
cost.pyToken cost tracking + ROI
camofox_ctl.shCamoufox (stealth Firefox) lifecycle — Cloudflare/Akamai bypass
pentest-statusline.shCC statusline: findings, brain, context, cost

Rules Library (rules/)

Single source of truth for every agent — all hunters, validators, and report-writers read the relevant files at session start.

FileLinesPurpose
hunting.md36031 hunting rules (Rule 0 harm check, Rule 8 sibling check, Rule 9 A→B signal, Rule 19 never-submit, Rule 24 mutation matrix, Rule 28 detection-token rotation, Rule 30 no cross-region inference, Rule 31 unauth state-change battery)
payloads.md2,605XSS (incl. Detection Mechanism Rotation Ladder) / SSRF / SQLi / IDOR / OAuth / upload / race / SSTI / deser / JWT / LFI / prototype pollution / NoSQLi / DeFi
techniques.md389Proven attack techniques extracted from real paid engagements
waf-bypass-protocol.md166WAF bypass iteration ladder for Akamai/Cloudflare/Imperva
vendor-status.md127Patched vendor vectors, framework fingerprints, cooldown tables
chain-table.md192Capability→next-bug chain table for /chain (9 capability rows + 4 documented deep chains)
never-submit.md42Never-submit list + conditionally-valid-with-chain table
mistakes.md665Top 10 most common mistakes — every agent reads this at session start

Key Features

  • Writeup search MCP: Agents query prior art during hunting — bring your own FAISS/SQLite writeup index, or fall back to the shipped payload/technique library
  • CC hooks: SubagentStop/Stop auto-log costs, statusline shows live $X.XX from token data
  • PreToolUse scope hook: Bash commands are matched (exact + wildcard) against scope.yaml; out-of-scope targets are blocked before the tool call fires
  • 7-Question Gate: Every finding validated — first NO = KILL
  • Depth Engine: /autopilot enforces an anti-shallow protocol — no claim of "exhausted" until the exhaustion matrix is complete
  • Stacked-encoding mandate: /hunt and /autopilot require multi-layer encoding in every payload attempt before declaring a surface clean
  • CVSS policy guard: HackerOne findings use CVSS 3.1; every other platform uses CVSS 4.0 — enforced by cvss_version_guard.py
  • Circuit breaker: 5× consecutive 403/429 → auto-backoff 60s
  • Endpoint tracking: Brain records every endpoint tested per target
  • Hard validation gates: /report and /submit refuse without /validate PASS
  • Never-submit filter: Pipeline auto-kills informational findings
  • Incremental sync: Global brain hash-based, skips unchanged files
  • Feedback loop: /learn auto-boosts paid techniques globally
  • Session journal: JSONL log for /resume continuity

Requirements

  • Python 3.10+, uv (MCP servers launch via uv run --with mcp)
  • Optional: uv pip install faiss-cpu sentence-transformers (for writeup semantic search)
  • Security tools: nmap, httpx, subfinder, nuclei, ffuf, katana, sqlmap
  • GraphQL hunter tools: graphql-path-enumcargo install --git https://gitlab.com/dee-see/graphql-path-enum (auto-installed by setup-mcp.sh if cargo is present)
  • Evidence: grim/scrot, wf-recorder/ffmpeg
  • jq (for statusline)

License

For authorized security testing only. Follow responsible disclosure.

常见问题

What is pentest-agents?

pentest-agents is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by H-mmer. Bug bounty agent framework for Claude Code, Codex, Gemini, Cursor, Windsurf, Copilot, and OpenClaw — 48 agents, 26 commands, 19 CLI tools, 2 MCP servers, autonomous hunt loops, exploit chain builder. It has 806 GitHub stars.

Is pentest-agents safe to use?

Yes. pentest-agents passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.

How do I install pentest-agents?

Clone the repository with "git clone https://github.com/H-mmer/pentest-agents" and add it to your Claude Code skills directory (see the Installation section above).

What programming language is pentest-agents written in?

pentest-agents is primarily written in Python. It is open-source under H-mmer on GitHub, so you can review or fork the full source.

Are there alternatives to pentest-agents?

Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh pentest-agents against similar tools.

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