AI for Cybersecurity & Bug Bounty Hunting

Think creatively and prompt AI to uncover hidden attack surfaces during reconnaissance. Enhance efficiency through shell scripting, practice regularly, and include all vulnerabilities in your methodology.

Learn techniques to uncover vulnerabilities and report them clearly and respectfully, detailing impact, scope, permission to proceed with triage, and knowing limits.

Learn how to go from recon to reporting using AI to map the attack surface, identify assets and hidden targets, and produce automated, evidence-rich bug bounty reports.

Search startup.jobs for bug bounty postings to uncover private or hidden programs not yet on major platforms. For example, Blue Apron began using HackerOne and rewarded reported bugs.

Explore identifying bug bounty programs across platforms like Monster.com and bug bounty portals, recognize vulnerability disclosures, and learn reconnaissance and attack surface mapping techniques to report findings.

Identify bug bounty programs through active reconnaissance and attack surface mapping using available JSON data. Parse program details from GitHub and domains.txt to identify in scope targets and subdomains.

Demonstrates passive subdomain enumeration with sub finder, detailing data sources and API keys, and shows how combining with a word-list script can uncover assets missed by passive methods.

Explore using a LM agent with Claude.ai and Ollama, after setting up a test bed. Compare cloud tools to offline Ollama and run local models like llama 3.

Set up open web UI to provide a chat-like interface for a local llama 3.1 model, enabling offline usage and a docker-based deployment.

Set up the Claude LLM using the MCP protocol in Claude AI, connect to an external attacker MCP server, and run subdomain enumeration with sub finder for cybersecurity reconnaissance.

Learn to run port scans with nmap.org, read outputs on open ports and web server details, and switch to Olama with an ncp protocol server after free plan limits.

Fine-tune models to build a vector knowledge base for subdomain enumeration and cms tech detection, using prompts that enhance context and outputs for future exports.

Fine-tune a model to build a vector database for API testing using Postman collections and Swagger documentation. Identify endpoints, parameters, and tokens to test OWASP top ten vulnerabilities.

Use prompts to identify vulnerabilities and generate API test cases, then confirm manually; analyze JavaScript files to extract endpoints and secrets to guide fuzzing and subdomain discovery.

Discover how MCP, the model context protocol, an open standard from Anthropic AI, expands large language models with external actions—email, database access, file operations—by linking to apps and devices.

Expose a local web AI app to the internet with ngrok, forward port 3000, and access a private, offline LM via a public web UI.

Connect the MCP server to the Open Web UI using a valid Open API structure, configure a Flask app and run sub finder, and perform subdomain enumeration and tech detection.

Explore subdomain enumeration with subfinder and implement automated technology and http detection using an open web UI and an MCP server, enhancing a Flask app with new routes.

Configure and run an MCP server via the open web UI, enabling Sub Finder. Analyze JavaScript for content discovery and bug bounty reconnaissance using Wayback URLs.

Prepare JavaScript files for analysis with GPT for all by adding JS to allowed extensions and saving them to a collection, then build embeddings with llama 3.1 for vector-based querying.

Analyze javascript files from bank targets to reveal target-specific variable names, endpoints, and subdomains; generate bank-focused wordlists for fuzzing and perform subdomain enumeration to discover assets.

Identify active assets and subdomains from a cleaned word list, deduplicate duplicates, and use permutation techniques with WFirst, Fuf, directory Buster, and Olama to expand subdomain enumeration.

Discover how retrieval augmented generation enhances a llama 3.18 billion instruction model by integrating external textual knowledge via a document store and vector database for pen testing and bug bounties.

Train the model with external knowledge resources to refine output, using the open web UI to create knowledge bases and upload RFCs and RFC content for security insights.

Showcases an authentication bypass in Apple ID signup, where manipulating an RFC 204 response bypasses email OTP and moves to phone verification, enabling account takeover.

Train a specialized Ollama model on a four-page somatosensory knowledge base, derived from the anatomy of the somatosensory system, skin, and temperature responses.

Train Ollama for API testing using custom API documentation and swagger insights; identify endpoints, generate test cases, and explore bug bounty programs like Bentley's with a structured knowledge base.

Identify RESTful API endpoints and their parameters, then craft http requests and leverage swagger docs to guide API pentesting. Build and feed knowledge bases to models for bug bounty testing.