llm-public-opinion-analytics-assistant
llm public opinion analytics assistant
pentest-agents-bug-bounty-framework
Autonomous bug bounty agent framework with 50 agents, hunt loops, exploit chains, MCP servers for platform integration and writeup search
Full skill instructions
Skill by ara.so — Security Skills collection.
Autonomous bug-bounty framework for Claude Code, Codex, Gemini, Cursor, Windsurf, Copilot, and OpenClaw. Ships 50 agents, 26 commands, 19 CLI tools, 11 skills, and 2 MCP servers (bounty platforms + writeup search). Includes 2,500 lines of concrete payloads, 7-Question Gate validation, autonomous hunt loops, A→B exploit chain building, persistent brain with endpoint tracking, and cross-IDE installer.
# Clone the repository
git clone https://github.com/H-mmer/pentest-agents-suite
cd pentest-agents-suite/pentest-agents
# Quick start with MCP servers (no global pip install)
export HACKERONE_USERNAME=your_username
export HACKERONE_TOKEN=your_api_token
# Scaffold a new bounty workspace
uv run python3 tools/scaffold.py hackerone tesla
cd ~/bounties/hackerone-tesla
# Launch Claude Code
claude
# Use pre-rendered bundles directly
cd pentest-agents-suite/pentest-agents/providers/codex # or gemini, cursor, etc.
codex # or: gemini, cursor, windsurf
# OR install into your project
python3 -m tools.installer install --targets all --scope project
python3 -m tools.installer install --targets codex --scope global
# List installed targets
pentest-agents list
# Install to specific targets
pentest-agents install --targets claude_code,codex --scope global
pentest-agents install --dry-run # Preview before installing
# Verify installation
pentest-agents verify
# Uninstall (safe rollback)
pentest-agents uninstall
# Regenerate provider bundles
pentest-agents render --targets all
pentest-agents render --check # Check for drift
HackerOne (full API), Bugcrowd, Intigriti, Immunefi, YesWeHack + 11 stubs.
Configuration:
# HackerOne (full API support)
export HACKERONE_USERNAME=your_username
export HACKERONE_TOKEN=your_api_token
# Bugcrowd
export BUGCROWD_EMAIL=your_email
export BUGCROWD_TOKEN=your_token
# Intigriti
export INTIGRITI_TOKEN=your_token
# YesWeHack
export YWH_API_KEY=your_api_key
7 MCP Tools:
list_platforms - List all configured platformsget_program_scope - Fetch in/out-of-scope assetsget_program_policy - Get submission rulessearch_hacktivity - Find similar reportssync_program - Download scope to local braindraft_report - Prepare submissionsubmit_report - Submit to platformThree search modes (auto-detected, graceful fallback):
| Mode | Requires | Searches |
|---|---|---|
| FAISS (semantic) | faiss-cpu, sentence-transformers, your metadata.db + index.faiss | Your writeup corpus via vector embeddings |
| SQLite (keyword) | Your metadata.db only | Your writeup corpus via LIKE over text |
| Local (default) | Nothing | rules/payloads.md + shipped skills |
Configuration:
# Point to your index directory
export WRITEUP_DB_DIR="$HOME/.local/share/pentest-writeups"
# OR place files in default location:
# ~/.local/share/pentest-writeups/metadata.db
# ~/.local/share/pentest-writeups/index.faiss (optional)
Build Your Own Index:
cd rag-builder
# 1. Inspect the plan (dry-run, no writes)
python3 build.py status
python3 build.py ingest
# 2. Pre-flight check (probe URLs with git ls-remote)
python3 build.py ingest --check-remotes
# 3. Clone + index repos from repos.yaml
python3 build.py ingest --execute
# 4. Point MCP server at the output
export WRITEUP_DB_DIR="$PWD/data"
python3 ../mcp-writeup-server/server.py --test
Edit rag-builder/repos.yaml to customize the 146-entry seed list of CTF archives, bug-bounty reports, and payload collections.
4 MCP Tools:
search_writeups - Semantic/keyword search for prior artget_writeup - Full writeup content by IDsearch_techniques - Exploitation techniques by vuln classsearch_payloads - Curated payloads from rules/payloads.md# 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
# Set model and sync program
/model opus
/sync hackerone tesla
# Initialize brain and check status
/brain init
/status
# Hunt for vulnerabilities
/hunt tesla.com
/hunt tesla.com --vuln-class sqli
/autopilot tesla.com # Autonomous loop
# Validate findings
/validate # 7-Question Gate
/chain # Build exploit chain
/triage # Batch validate all findings
# Report submission
/report
/dupcheck
/submit
/learn # Update brain with learnings
# Brain management
/brain show endpoints
/brain add endpoint https://api.tesla.com/v1/users
/brain note "Found rate limit bypass in auth flow"
/brain search "jwt"
# Cost tracking
/cost # Show session costs
# Create new bounty workspace
import subprocess
# Scaffold for HackerOne program
subprocess.run([
"uv", "run", "python3", "tools/scaffold.py",
"hackerone", "tesla"
])
# Scaffold for Bugcrowd program
subprocess.run([
"uv", "run", "python3", "tools/scaffold.py",
"bugcrowd", "acme-corp"
])
This generates:
~/bounties/<platform>-<program>/ directoryCLAUDE.md, AGENTS.md, .codex/, .gemini/, .cursor/ configs.mcp.json with platform + writeup server config.agents/skills/ with all framework skillsKey orchestrator agents:
chain-builder - Links findings into exploit chains (A→B)correlator - Cross-references findings with brainrecon-ranker - Prioritizes attack surfacehunt-orchestrator - Coordinates active huntingvalidator - 7-Question Gate complianceSpecialized hunters:
sqli-hunter, xss-hunter, ssrf-hunterauthz-hunter, jwt-hunter, idor-hunterapi-hunter, graphql-hunter, websocket-hunterAgents inherit model via model: "inherit" frontmatter. Orchestrators dispatch to specialized agents automatically.
{
"mcpServers": {
"bounty-platforms": {
"command": "uv",
"args": [
"run",
"--with", "mcp",
"python3",
"mcp-bounty-server/server.py"
],
"env": {
"HACKERONE_USERNAME": "your_username",
"HACKERONE_TOKEN": "your_token"
}
},
"writeup-search": {
"command": "uv",
"args": [
"run",
"--with", "mcp",
"--with", "faiss-cpu",
"--with", "sentence-transformers",
"python3",
"mcp-writeup-server/server.py"
],
"env": {
"WRITEUP_DB_DIR": "/home/user/.local/share/pentest-writeups"
}
}
}
}
Add to Claude Code settings.json:
{
"hooks": {
"SubagentStop": "python3 /path/to/pentest-agents/hooks/cost_hook.py",
"Stop": "python3 /path/to/pentest-agents/hooks/cost_hook.py",
"SessionStart": "python3 /path/to/pentest-agents/hooks/welcome.py"
}
}
Logs to cost-tracking.json:
{
"sessions": [
{
"timestamp": "2026-05-17T10:30:00Z",
"agent": "sqli-hunter",
"input_tokens": 15000,
"output_tokens": 2500,
"cost_usd": 0.12
}
]
}
# Initialize brain for target
/brain init
# Add discoveries
/brain add endpoint https://api.example.com/v1/users
/brain add finding "JWT lacks signature verification in /auth"
/brain add technique "SSRF via PDF renderer"
# Query brain
/brain search "jwt"
/brain show endpoints
/brain show findings
/brain stats
# Export for reporting
/brain export findings.json
Python API:
from tools.brain import Brain
brain = Brain("tesla.com")
brain.init()
# Track endpoints
brain.add_endpoint("https://api.tesla.com/v1/users", {
"method": "GET",
"auth": "Bearer token",
"params": ["user_id", "include_deleted"]
})
# Store findings
brain.add_finding({
"vuln_class": "IDOR",
"severity": "high",
"endpoint": "/v1/users/{id}",
"description": "Lack of authz check allows cross-account access",
"poc": "curl -H 'Authorization: Bearer USER_A' https://api.tesla.com/v1/users/USER_B_ID"
})
# Query
jwt_findings = brain.search("jwt")
all_endpoints = brain.get_endpoints()
stats = brain.stats()
Framework ships rules/payloads.md with 2,500 lines of categorized payloads:
# Query via MCP
# In Claude Code session:
# Agent calls search_payloads("sqli mysql")
# Returns context-aware payloads from rules/payloads.md
Payload categories:
Add to workspace payloads/<vuln-class>.md:
# Custom SQLi Payloads for Tesla
## Time-based blind (WAF bypass)
' AND (SELECT * FROM (SELECT(SLEEP(5)))a)-- -
' AND SLEEP(5) AND '1'='1
Agents will query both shipped and custom payloads.
Every finding must pass before submission:
# Triggered via /validate command
questions = [
"What is the exact attack vector?",
"What is the business impact?",
"Can you reproduce it 3 times?",
"Is it in scope per program policy?",
"Have you checked for duplicates?",
"Is there a clear fix recommendation?",
"Does the PoC include only test data?"
]
# Agent validates each finding against all 7
# Blocks submission if any answer is unclear
# After finding multiple related issues
/chain
# Agent analyzes:
# 1. Finding A: SSRF in PDF renderer
# 2. Finding B: Admin panel on internal IP
# 3. Finding C: CSRF on admin delete user
# Builds chain:
# A (SSRF) → B (access admin) → C (delete users)
# Calculates combined severity: CRITICAL
# Generates unified PoC
Python API:
from tools.chain_builder import ChainBuilder
builder = ChainBuilder()
builder.add_finding("ssrf", {
"endpoint": "/render-pdf",
"impact": "Access internal network"
})
builder.add_finding("csrf", {
"endpoint": "/admin/delete-user",
"impact": "Delete arbitrary users",
"requires": "Admin session"
})
chain = builder.build()
# Returns: dependency graph, combined severity, unified PoC
# Via MCP tools in agent session
# List programs
programs = await mcp.call_tool("list_platforms", {})
# Get Tesla scope
scope = await mcp.call_tool("get_program_scope", {
"platform": "hackerone",
"program": "tesla"
})
# Search for similar reports
similar = await mcp.call_tool("search_hacktivity", {
"platform": "hackerone",
"query": "IDOR users endpoint",
"limit": 10
})
# Submit report
report = await mcp.call_tool("submit_report", {
"platform": "hackerone",
"program": "tesla",
"title": "IDOR in /v1/users allows cross-account access",
"severity": "high",
"description": "...",
"poc": "...",
"impact": "..."
})
# Sync program to local brain
await mcp.call_tool("sync_program", {
"platform": "bugcrowd",
"program": "acme-corp"
})
# Get submission policy
policy = await mcp.call_tool("get_program_policy", {
"platform": "bugcrowd",
"program": "acme-corp"
})
# Start autopilot mode
/autopilot tesla.com
# Agent loop:
# 1. Query writeup DB for techniques
# 2. Test endpoints from brain
# 3. Execute payloads from rules/
# 4. Validate findings (7-Question Gate)
# 5. Build exploit chains
# 6. Log to brain
# 7. Repeat with new techniques
Modes:
--paranoid - Extra validation, slower--normal - Balanced (default)--aggressive - Fast, more false positives# Test manually
cd mcp-bounty-server
uv run --with mcp python3 server.py --test
cd mcp-writeup-server
uv run --with mcp --with faiss-cpu --with sentence-transformers python3 server.py --test
# Check env vars
echo $HACKERONE_TOKEN
echo $WRITEUP_DB_DIR
# Verify .mcp.json paths are absolute
cat .mcp.json | grep command
# Check if metadata.db exists
ls -lh ~/.local/share/pentest-writeups/metadata.db
# Verify schema
sqlite3 ~/.local/share/pentest-writeups/metadata.db "PRAGMA table_info(writeups);"
# Expected columns: id, title, url, content/text/body/writeup
# Test FAISS dependencies
python3 -c "import faiss; import sentence_transformers; print('OK')"
# Check brain directory
ls -la ~/.pentest-agents/brains/
# Manually initialize
python3 -c "from tools.brain import Brain; b = Brain('tesla.com'); b.init(); print(b.stats())"
# Verify permissions
chmod -R u+w ~/.pentest-agents/
# Verify hook is registered
cat ~/.claude/settings.json | grep hooks
# Check hook output
python3 hooks/cost_hook.py # Should emit JSON
# View tracking log
cat cost-tracking.json | python3 -m json.tool
# Show what would be installed
pentest-agents install --dry-run --targets all
# Check for drift
pentest-agents verify
# Safe rollback
pentest-agents uninstall # Restores .pa-backup files
# Check drift
python3 -m tools.installer render --check
# Regenerate all providers
python3 -m tools.installer render --targets all
# Regenerate specific target
python3 -m tools.installer render --targets codex
| Feature | Claude Code | Codex | Gemini | Cursor | Windsurf | Copilot | OpenClaw |
|---|---|---|---|---|---|---|---|
| Native agents | ✅ | ✅ | ✅ | Skills only | Skills only | ✅ (30KB limit) | Skills only |
| Slash commands | ✅ | ✅ | ✅ | Skills | Workflows | Prompts | Skills |
| Rules files | ✅ | ✅ (32KB) | ✅ | ✅ | ✅ (12KB/file) | ✅ | ✅ |
| MCP servers | ✅ | ✅ | ✅ | ✅ | ✅ (user-level) | ✅ | ✅ (user-level) |
| Model inheritance | ✅ | Via model_reasoning_effort | N/A | N/A | N/A | N/A | N/A |
All targets get the same 50 agents, 26 commands, 2 MCP servers — only the file format differs.
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