Ingest
Connect a repo or paste a program. Every push is pulled into the engine automatically — no config, no agents to babysit.
→ PNM AI EngineAI that hunts bugs
AI that hunts bugs
while you sleep.
Our engine pairs large security models with battle-tested fuzzing and symbolic execution — trained on 1,100+ real vulnerabilities reported by our hackers. It reads every commit, reasons about your code like an attacker, and only surfaces bugs it can actually prove.
Free · Solana programs
$ pnm-engine analyze ./programs --chain solana
0+
Real vulnerabilities learned
0
False positives shipped
0/7
Continuous analysis
0%
Avg. finding confidence
Four steps from commit to proof.
Reason
Security models, coverage-guided fuzzing and symbolic execution explore millions of paths in parallel, flagging anything that smells like an attacker's way in.
→Verify
An AI agent rebuilds each candidate as a working exploit inside a sandbox and throws away everything it can't actually reproduce. That's how we ship zero false positives.
→Report
You get a ranked, deduplicated report with a runnable proof-of-concept and an AI-drafted fix for every real bug — reviewed by a human hacker before it reaches you.
Not a chatbot. A security machine.
LLM-assisted triage
A fine-tuned security model reads diffs and findings the way a senior auditor would — ranking impact and killing noise.
Graph neural networks
We embed bytecode and call graphs so the model spots dangerous patterns even in code it has never seen before.
Coverage-guided fuzzing
Millions of mutated inputs driven toward unexplored branches to force rare, high-severity edge cases into the open.
Concolic execution
Symbolic + concrete execution solves for the exact inputs that reach a vulnerable state — no guessing.
Agentic PoC synthesis
An autonomous agent writes and runs exploits in a sandbox, proving each bug is real before you ever see it.
Trained on real bugs
Learning from 1,100+ vulnerabilities our hackers actually reported — not synthetic benchmarks.
The honest answers.
Is this just an LLM wrapper?–
No. The models are one layer of a pipeline that also runs fuzzing, symbolic execution and sandboxed exploit verification. AI decides where to look and how to explain a bug; the classic techniques prove it.
How do you avoid false positives?+
Which ecosystems are supported?+
Do humans stay in the loop?+