~20%
of bug bounty submissions in 2025 were AI-generated
5%
of submissions turned out to be genuine vulnerabilities
30-90 min
of engineer time per report — real or fake
30-90 min saved per report
Reports that used to eat an engineer's morning now resolve on their own. Structured proof, no manual reproduction.
Dynamic reproduction, not static checks
We spin up the app, run the exploit, and capture what happens. Real execution against real code, not a static scan.
Full evidence trail
HTTP logs, command output, failure explanations. Attached to every verdict so your team can verify the result.
Multi-agent orchestration
Six AI agents in sequence: validate, plan, provision, deploy, exploit, report. Each step logged, each decision traceable.
Proof in a sandbox, not your production
Each report gets its own disposable infrastructure. The vulnerable app is deployed at the exact reported version. An attacker instance runs exploits from a separate machine. The environment tears down after every run.
HTTP logs, command output, attack timeline
The reproduction gap
Platforms are getting good at filtering noise. The runtime reproduction step is still manual. Konvu automates it.
~60-80%
Noise filtering
Platforms handle this. AI exists (HackerOne Hai, Bugcrowd AI).
~15-30%
Lightweight verification
Burp Suite, curl, code review. Single HTTP request checks.
~2-5%
Full reproduction
30-90 min engineer time. Complex auth, config-dependent.