// bug bounty
· from $20/moAn uncensored
bug bounty co-pilot.
Bug bounty hunting is a numbers game where leverage compounds.
A senior hunter with the right tooling can submit five quality reports in the time another hunter spends triaging one target. The math favours whoever has automated the most boilerplate. The major coding assistants will help you — until your prompt mentions exploitation, payload, sink, vulnerability class, or anything else that triggers their content policy. Then you spend half your hunting time arguing with the tool. TartarusAI accelerates the parts you can automate — JavaScript bundle triage, parameter fuzzing, sink discovery, PoC writing, disclosure-ready reports — so you spend the engagement on the parts that actually pay: the bug, the impact, the writeup. Same enterprise-grade runtime safety guards. No content-policy refusals. The agent treats bounty work the way HackerOne and Bugcrowd already treat it: as legitimate professional security research.
- ✓Zero refusals on offsec work
- ✓Custom-tuned model — not a wrapper
- ✓Runtime guards on by default
- ✓256K context, sub-2s TTFT
refund if it ever refuses · no card on file · crypto-only · cancel any time
❯ analyse this 30K-line js bundle for prototype pollution + ssrf sinks
target: hackerone program, in-scope subdomain
▎▣ static-analyzed bundle.js (32,488 lines)
▎+ found 3 candidate prototype-pollution sinks
▎+ found 1 ssrf via image-proxy endpoint
▎+ wrote poc/proto_pollute.html (47 lines)
done.// what it does
What you ship
when nothing refuses.
JavaScript bundle analysis
Static analysis of giant SPA bundles, sink discovery (DOM XSS, prototype pollution, SSRF, open redirect, postMessage abuse, JSONP abuse), source-map reconstruction, secret extraction, hidden-endpoint enumeration, lazy-loaded route discovery. The agent reads the bundle so you do not have to scroll for an hour.
Subdomain + asset enumeration
Custom recon harnesses across the program scope. Subdomain takeover candidate detection, dangling DNS scoring, S3 / GCS / Azure blob bucket triage, GitHub leak scanning, public-paste monitoring, JS endpoint extraction, API documentation scraping. Output is structured data ready for prioritisation.
PoC writing on the spot
Once you find the bug, the agent writes the H1-quality PoC — minimal repro, impact demonstration, suggested fix. Cuts your writeup time to a fraction. Particularly useful for the long-tail bug classes (CRLF injection, SSRF chain to RCE, prototype pollution to ATO) where the working PoC requires plumbing the platform team will not accept without.
Disclosure-ready reports
Title, severity scoring, CVSS, repro steps, impact statement, suggested remediation. Tailored to the program style (HackerOne strict prose, Bugcrowd structured fields, private-program custom format). The agent ghostwrites the report; you review, edit, submit. Cuts time-to-submission significantly without sacrificing report quality.
Authorization-aware fuzzing
Configure scope (in-scope hosts, out-of-scope paths, rate limits, accepted tooling), and the agent writes the harness that respects them. Useful for staying compliant with program rules during automated discovery — particularly important on programs with strict rate-limit clauses or specific tooling restrictions.
Target prioritisation
Given a recon dataset, the agent ranks targets by likely yield: subdomain age, technology stack confidence, public exposure, presence of admin / dev / staging variants, recent platform activity, comparable disclosed bugs. Cuts the worst part of bounty hunting — choosing which target to actually attack.
// workflow
A typical hunt
You pick a program, you read the scope, you decide where to focus. The agent generates the recon harness sized to the scope (subdomain enumeration, JS endpoint discovery, exposed S3 buckets, GitHub leak monitoring, paste-site watching). Output lands as structured data — JSON, CSV, or directly into the prioritisation spreadsheet you already use.
You start poking at the highest-priority targets. The agent reads the JS bundles, identifies sinks worth investigating, writes the harnesses for parameter fuzzing, drives the chain when an attack surface looks promising. For business-logic bugs (the high-payout class), the agent helps you reason about the application state machine and identify the assumption that breaks.
When you find something, the agent writes the PoC and the report. You review, edit, submit. The submission flow itself stays manual — the agent does not interact with the bounty platform on your behalf — but everything upstream of "click submit" is accelerated.
// where it fits
In your existing bounty toolchain
TartarusAI does not replace Burp, Caido, ffuf, nuclei, subfinder, amass, gowitness, httpx, or your custom recon scripts. It writes the harnesses and Burp extensions that drive them. Custom Burp extensions for engagement-specific patterns. Custom nuclei templates calibrated to your target. ffuf wordlists generated from the application surface. Custom parsers for the structured output of your recon stack.
For the parts that are bottlenecked on senior judgement (which bug class to chase, which target to focus on, when to walk away), you stay in control. For the parts that are bottlenecked on typing (writing the harness, writing the PoC, writing the report), the agent absorbs the work.
- ●Pairs with Burp Suite Pro, Caido, ffuf, nuclei, subfinder, amass, httpx, gowitness, dnsx, naabu, katana.
- ●Generates Burp extensions, custom nuclei templates, ffuf wordlists from extracted application surface.
- ●Outputs are raw scripts and structured data — no SaaS lock-in.
- ●Authorization-aware: respects program scope, rate limits, and tooling restrictions.
// economics
The economics of bounty hunting with AI
A senior bounty hunter who can spin up custom recon, custom Burp extensions, and custom nuclei templates per program is a small minority. Most hunters use the same off-the-shelf tooling, find the same off-the-shelf bug classes, and compete with hundreds of others on the same surface. The bugs that pay are the ones that require either deep expertise or custom tooling — usually both.
TartarusAI lowers the cost of custom tooling per program from "you need a senior hunter who can also code" to "you need a senior hunter who can describe what they want." The expertise stays with you. The tooling cost drops by an order of magnitude. The bugs that used to require both pay you because you are now in the small minority that has the tooling to find them.
The math: $20-150/month subscription pays for itself on a single $500 medium-severity finding. For full-time hunters, the subscription is a rounding error against monthly bounty income. For part-time hunters, it is the difference between competing on the picked-over surface and going after the bugs nobody else has tooling for.
// questions
What people actually ask.
Will it help with bug bounty work without lecturing me?+
Is it safe to paste sensitive bounty research into TartarusAI?+
Can it run authorization-aware fuzzing?+
What classes of bugs does it cover?+
Does it know modern web framework quirks?+
Will it write the report in HackerOne / Bugcrowd format?+
Can it analyse mobile apps (iOS / Android)?+
How does it compare to Caido / Burp AI assistants?+
// ready
Stop fighting refusals.
Start shipping the engagement.
One tier covers most engagements at $20/month. If the agent ever refuses, hedges, or returns neutered output on legitimate engagement work, we refund — see the refund policy.
refund if it ever refuses · no card on file · crypto-only