Security and privacy decision
Audn.AI
A capable engineer can build a useful autonomous adversarial tester for voice/agent surfaces, but Audn’s proprietary offensive model roster, retraining pipeline, and enterprise features make a full replacement impractical for a solo builder.
Visit website↗$99/mo
$1,188/yr
Read off the official pricing page.
$100one-off160 h to build
$300/mo6 h/mo upkeep
On cash alone, building overtakes the subscription at 4 seats.
The code exists. It is not what you are paying for.
These 2 projects are real, published, and do the core job — and this page still says keep paying. What the subscription buys is proprietary models and proprietary models, and none of that ships in a repository. Fork one anyway if you want to. Go in knowing what it does not carry. What stays hard ↓ · All Audn.AI alternatives, with the arithmetic →
What a replacement has to do
- Run reconnaissance, generate adversarial payloads via an LLM, execute attacks against an isolated sandbox (HTTP/voice), capture evidence and reproducible exploit chains, produce a scored report and retest after fixes.
What it still won’t have
- Their pretrained proprietary offensive-model roster (Pingu/Necromicon) and continual fine-tuning
- Opt-in federated/retraining pipeline and per-tenant model weights
- Enterprise compliance reports, SLA, and on-premise deployment options
- KYC-gated CLI/packaged offensive tooling and curated attack corpus
What remains hard
- Proprietary models
Pingu Unchained is a blackbox external penetration-testing LLM, trained from real pentester usage.
- Proprietary models
Investment $40K in training over the last six months.
First-year cost
Keep paying
Paying is—cheaper in year one.
On cash alone, building overtakes the subscription at 4 seats.
Money you would actually spend
Time you would spend
—
What you would spend
What we assumed
The verdict above measures whether you could build it. This one is only about money.
Runnable build prompt
Build a minimal autonomous adversarial validation service in Node.js + Express, React front end, Postgres DB, and Docker deployment. Scope: (1) LLM integration module (OpenAI-compatible) to generate recon and exploit payloads, (2) recon worker that maps endpoints and agent tool edges, (3) sandboxed executor that runs HTTP/voice inputs against an isolated preview target and records full request/response logs, (4) evidence store in Postgres and simple HTML/PDF report generator with CVSS-like scoring, (5) basic web UI to launch tests, view findings, and trigger retests, plus a CLI to run scheduled scans. Out of scope: training proprietary models, federated retraining, enterprise SLA, KYC flows, on-prem binary installers. Include input validation, sandboxing/error handling, authentication for the UI, unit tests for core modules, and end-to-end tests for one full recon→exploit→report cycle.
How we checked
How the score was reached
- Pay verdict base20
- An open-source build was found+5
- 4 cited sources+3
- Price verified on pricing page+3
- 3/3 assessment runs agreed+4
- Hard moats found in the evidence-3
- Evidence score32
The base comes from the verdict. Everything under it is a check that either happened or did not, and each one is a fact frozen in this record rather than a judgement made at render time - so the same evidence always produces the same number.
How scoring works →Cited sources · 4
Every page the run actually retrieved.
- official productAudn.AI home
- official pricingAudn.AI pricing
- open sourceTencent/AI-Infra-Guard
- open sourceelder-plinius/T3MP3ST
Integrity checks
What held up, and what did not.
