ELTON builds a digital twin of your device, threat models it with MITRE EMB3D, generates a test case for every threat, and runs a proprietary toolchain against the real product. AI builds the tools. The pipeline does the testing. Your data never becomes the prompt.
Autonomous pentesting is real and it is narrow. It is strong on a reachable web surface and blind to the firmware, hardware, and protocols where a medical device actually lives.
The pipeline runs in one direction and the trace never breaks. Each link carries an identifier, so coverage is a list you can read before the first packet is sent.
Ground truth first. A model of the device's security architecture, built from the documentation your quality system already produces: components and SBOM, interfaces, trust boundaries, and documented countermeasures. It is Level 1 of verification and clears 30 to 40 percent of findings as Not Affected before anyone touches hardware.
The twin becomes threats. Component types map to device properties, properties map to the threats EMB3D says apply, and ELTON extends both where the catalog stops. Every threat is pinned to a specific component, so nothing about the next step requires guessing where to look.
A procedure, not a prompt. For each pinned threat the pipeline generates a test case with a scope, a pass criterion, a fail criterion, and an execution tier. Because it descends from a threat that descends from the twin, coverage is auditable in both directions, which is the artifact FDA reviewers ask for.
AI writes the tools, the pipeline runs them. An orchestrator dispatches deterministic tools and specialized agents across network, firmware, code, and clinical workflow, carried onto the real device by TestLink™. More than five agentic loops write new tools around the clock. No finding reaches you before a qualified tester reviews it.
Proof, not a rating. Every test runs at the deepest tier you open. Each finding lands as directly exploitable, conditionally exploitable, an unexploitable weakness, or already mitigated, with an executed test case behind it and a rating on the FDA-qualified MDDT rubric.
Your source code, firmware, documents and findings stay inside the pipeline. What a model sees is ELTON's own engineering work: what a tool needs to do, and how a documented protocol behaves.
Pointing a general model at a device has no device context, verifies nothing, and returns something different every time. The output is a wall of maybe-issues with no evidence a reviewer can follow, and getting there meant handing over your source code.
Tools are deterministic. They run the same way twice, against the real device, and produce evidence. The data path and the development path never meet, so the model improves on ELTON's engineering, never on your data.
The tiers run in your favor. More access does not surface more alarms, it retires more findings with evidence a regulator will accept.
A web-app validator re-exploits a finding to prove it is real. ELTON also manufactures the negatives: the executed test that lets you defensibly dismiss the 99 percent that are not exploitable on your device.
Autonomous web-app pentesters point a model at a target and prove what they can reach. ELTON builds the device first, threat models it, tests every threat with tools the AI writes and a human reviews, and proves both what is exploitable and what is not, in evidence a reviewer will accept.