01THE THESIS

The AI pentest
engineer. It proves everything.

BountyForge hunts like your best analyst — it reads the terrain, spends its budget where evidence says it pays, learns across hunts, and ships only findings that replay. Verified, or nothing.

See a real scan

“Patterns mislead. Deviations reveal.”

THE ANALYST'S CREED — EVERY REQUEST IS MATCHED AGAINST WHAT IS NORMAL.

02THE METHOD
01

It reads the terrain before it strikes.

A sensing layer scores size, complexity and defenses from live signal — then the planner allocates budget where evidence says it pays. No fixed pipeline. No same-playbook-every-target swarm.

sense → score → allocate → strike
02

Twenty-two specialists. One mind.

A supervisor dispatches a per-target agent roster — counter-intel when a WAF appears, a rogue hunter when a target proves barren — and agents can request new specialists mid-hunt.

agent_pool.spawn(terrain, yield_memory)
03

It remembers what paid off.

Yield memory persists per target type across hunts. Steps proven barren are pruned; domains proven fertile get budget first. Every scan starts smarter than the last.

memory.decay() · memory.evolve()
04

Verified, or it ships nothing.

Nine layered gates replay every claim against the live target before it is called confirmed. Unverified findings are demoted, never shipped as fact. Scanner noise is not a deliverable.

claim → replay → hash → confirm
03THE PROMISE

“The AI said so.” is not a report.

Here is the replay. Here is the hash. Run it yourself.

●●●finding-042 · replay
$ bf verify 042 --replay
→ GET /api/v1/admin/users  (session A → tenant B)
← 200  4,113 bytes  [{"id":…}]        ✓ matches evidence hash
✓ CONFIRMED · IDOR · HIGH · cvss 8.1  sha256:9f4c…a2e1
Compliance teamsInsurersEnterprise procurementWeb2 + Web3

A buying-committee unlock — not a feature.

04THE RECORD
22
Hunting agents
90
Security tools
1,000+
Vulnerabilities spotted
9
Validation gates

Across live hunts on public programs. Every number above is a claim the engine will replay for you.

05THE PLANS
Pro
Continuous hunts, PAYG extras.
Enterprise
Private deployment, org scopes.
06QUESTIONS
What is AI penetration testing?

AI penetration testing uses autonomous agents to attack a target the way a human security researcher would — reconnaissance, fingerprinting, exploitation, chaining — but continuously, in parallel, and at a fraction of the cost. BountyForge is an AI pentest engineer: it plans its own tests, executes them in an isolated sandbox, and writes the report.

Can an AI really find real vulnerabilities?

Yes — with a verification rail. BountyForge never ships a guess: every confirmed finding must survive nine layered verification gates, including replaying the proof-of-concept against the live target. A finding that does not re-execute is demoted, not reported.

How is BountyForge different from a vulnerability scanner?

A scanner runs fixed templates and returns noise. BountyForge senses each target’s terrain, spends its budget where evidence says findings live, learns what worked on similar targets across hunts, and chains individual weaknesses into exploit paths — the judgment layer scanners do not have.

Does it work for bug bounty hunting?

Bug bounty programs are a primary surface. The engine honors scope manifests (in-scope, out-of-scope, disallowed test types), rate limits, and authorization records on every request, and formats verified findings for HackerOne, Bugcrowd, Intigriti, and Immunefi submission.

Can it audit smart contracts?

Yes — Solidity, Solana, and Move targets are audited with static analysis, invariant checks, and fork-based PoC execution, so a reported exploit is proven against a pinned live block, not asserted.

Is autonomous pentesting safe to run on production systems?

It is authorization-gated by design: scans require a valid scope record declaring authorized targets before a single packet is sent, run inside isolated sandboxes, and stop at out-of-scope boundaries enforced at the tool level.

07THE UNLOCK

Run it yourself.

or press S