# Bounty Hunting

> Bug bounty white-box hunting methodology. Load when the target is an open-source project with a security advisory program, bug bounty, or responsible disclosure policy.

- **Type:** Skill
- **Install:** `agentstack add skill-mingyiseclab-mingyi-atlas-bounty-hunting`
- **Verified:** Pending review
- **Seller:** [MingyiSecLab](https://agentstack.voostack.com/s/mingyiseclab)
- **Installs:** 0
- **Category:** [Security](https://agentstack.voostack.com/c/security)
- **Latest version:** 0.1.0
- **License:** Apache-2.0
- **Upstream author:** [MingyiSecLab](https://github.com/MingyiSecLab)
- **Source:** https://github.com/MingyiSecLab/Mingyi-Atlas/tree/main/src/skills/standard/analyst/bounty-hunting

## Install

```sh
agentstack add skill-mingyiseclab-mingyi-atlas-bounty-hunting
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# Bug Bounty Hunting Methodology

You are not scanning. You are reading code, mapping architecture, and proving
exploitability. Volume is the enemy — signal is the metric. Every report must
survive triage by an experienced security engineer.

## Target Assessment

Before committing iteration budget, evaluate the target:

1. **Impact surface**: downloads/week, GitHub stars, dependency depth. A vuln in
   lodash or React Router has 10-100x the impact of a vuln in a 200-star project.
2. **Trust boundary complexity**: Does the app load config from untrusted sources?
   Handle plugins? Parse user-controlled serialized data? Multi-tenant auth?
   Complex trust boundaries = more attack surface.
3. **Security advisory history**: Check `github.com/advisories?query=`.
   Projects that accept and credit researchers will work with you. Projects with
   zero advisories are either very secure or don't have a disclosure process.
4. **Reward program**: HackerOne, Bugcrowd, Immunefi, GitHub Security Advisories,
   Google VRP. Check scope, excluded vuln classes, and reward tiers.

Record the assessment as a node:
```
kg_add_node("repo", "", props={"stars": N, "downloads_weekly": N,
  "has_security_policy": true, "advisory_count": N, "bounty_program": "hackerone"})
```

## White-Box Methodology

This is the core loop. Fork. Read. Trace. Prove.

### Step 1 — Map the project
```bash
find /workspace/target -name 'package.json' -o -name 'pyproject.toml' \
  -o -name 'go.mod' -o -name 'Cargo.toml' -o -name 'composer.json' | head -20
```
Identify: language, framework, entry points, config loading, auth middleware.

### Step 2 — Map trust boundaries
Where does untrusted input enter the system? Trace these sources:
- HTTP request params, headers, body
- Environment variables and `.env` files
- Config files from current directory (`.gemini/settings.json`, `.vscode/settings.json`)
- Plugin/extension loading paths
- Deserialization of user-controlled data (pickle, YAML, JSON with class hints)
- IPC channels, WebSocket messages, MCP tool inputs

For each source, add an ENTRYPOINT node:
```
kg_add_node("entrypoint", "POST /api/upload body", props={"type": "http",
  "file": "routes/upload.py", "line": 42})
```

### Step 3 — Identify crown jewels
What is the most valuable thing an attacker could reach?
- Admin access / account takeover
- RCE / command execution
- Database contents / PII
- Secret keys / credentials
- File system read/write

Add CROWN_JEWEL nodes for each.

### Step 4 — Trace source → sink
For each entrypoint, trace the data flow to dangerous sinks:
- `exec()`, `eval()`, `spawn()`, `subprocess.run()`, `os.system()`
- `db.query()` with string interpolation
- `render()` / `innerHTML` with unescaped input
- `open()` / `readFile()` with user-controlled paths
- `pickle.loads()`, `yaml.unsafe_load()`, `JSON.parse()` with reviver
- `redirect()` with unvalidated URLs

Use `semgrep`, `grep`, or `bash` to find sinks, then manually trace backwards
to see if untrusted input reaches them without sanitization.

### Step 5 — Prove or discard
Every finding MUST go through `validate_finding` with:
- A working `poc_command`
- `success_patterns` that uniquely match the exploit signal
- A `negative_command` (same request without payload)
- `negative_patterns` matching the baseline response
- Full `cvss_vector` string

If you cannot reproduce it, it is NOT a finding. Record the failure and move on.

## High-Value Vulnerability Classes

Prioritize by typical bounty payout and acceptance rate:

1. **Remote Code Execution** — deserialization, template injection, command injection,
   unsafe `eval`/`exec`, `shell: true` with user input. Highest payouts.
2. **Access Control Bypass** — IDOR, BOLA, missing ownership validation, broken
   function-level authorization. Most common accepted finding class.
3. **Authentication Bypass** — token leakage, session fixation, OAuth state confusion,
   JWT `alg=none`, password reset flaws.
4. **Path Traversal / File Write** — directory escape in upload/download handlers,
   session file storage with crafted IDs, zip slip.
5. **Server-Side Request Forgery** — redirect bypass, DNS rebinding, cloud metadata
   access via internal URL parameters.
6. **Injection** — SQLi, XSS (stored > reflected), LDAP injection, GraphQL injection.
7. **Information Disclosure** — API key leakage, user enumeration via error codes,
   schema/debug endpoint exposure, directory listing.

## Report Title Convention

Every report title MUST follow: `[Impact] via [Mechanism] in [Component]`

Good:
- "Path traversal in plugin file upload enables arbitrary directory deletion and file write"
- "Cloud function validator bypass via prototype chain traversal"
- "Unauthenticated SSRF via HTTP redirect bypass in LiveLinks proxy"

Bad:
- "XSS vulnerability found" (no mechanism, no component)
- "Possible SQL injection" (speculative)
- "Security issue in API" (says nothing)

## What NOT to Do

- Do NOT submit without a validated PoC
- Do NOT inflate CVSS beyond demonstrated impact
- Do NOT submit theoretical findings ("an attacker could potentially...")
- Do NOT spam multiple low-quality reports — one rejected report damages signal
- Do NOT submit to out-of-scope targets
- Do NOT submit duplicate findings without checking `kg_query(kind="finding")`

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [MingyiSecLab](https://github.com/MingyiSecLab)
- **Source:** [MingyiSecLab/Mingyi-Atlas](https://github.com/MingyiSecLab/Mingyi-Atlas)
- **License:** Apache-2.0

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** yes
- **Filesystem access:** no
- **Shell / process execution:** yes
- **Environment & secrets:** yes
- **Dynamic code execution:** yes

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: flagged — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-mingyiseclab-mingyi-atlas-bounty-hunting
- Seller: https://agentstack.voostack.com/s/mingyiseclab
- Browse the marketplace: https://agentstack.voostack.com/browse

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