A security review of everything that runs through your Jamf Pro: scripts, extension attributes, policy commands and script parameters, local accounts in policies, configuration profiles and package scripts. Lynceus reads them live with read-only API requests or offline from Gimle backups. A local AI reviews each object first, then 39 deterministic rules confirm, add and check what the model claims. You get an executive summary, a security report and a fix list for engineers.
Scripts, script-type extension attributes, policy Files & Processes commands, script parameters $4–$11 and local accounts, computer and mobile device configuration profiles, and pre/postinstall scripts from packages in Gimle backups.
The bundled Gemma 4 model reads each object like a reviewer. Then 39 rules (30 security, 6 hygiene, 3 deprecation) run. A finding both agree on is confirmed. An AI finding that quotes code which doesn't exist is marked unverified and doesn't count. The AI alone never rates a finding critical.
Live scans only send GET requests, through Gimle's backup engine, so a read-only API role is enough. Or skip the API entirely and scan a Gimle backup, encrypted ones included.
Passwords, tokens and keys found in your content are masked everywhere Lynceus stores or shows them: findings, history, logs and every report. The tests check the reports for leaks.
An executive summary (risk grade, top risks, trend), a security report (every finding with evidence, coverage, accepted risks, the rule set used) and a fix list for engineers, as PDFs, plus CSV and Markdown.
Every scan is kept, so each run shows what's new and what's fixed. Unchanged objects aren't sent to the AI again. Accept a risk or mark a false positive with a reason; accepted risks reopen after 180 days by default.
Per object: is it in use, which policies use it, and how many devices it reaches. First seen and last changed come from the Gimle backup history. Filter to what's in use and fix that first.
Group findings by object or by finding, select several with ⌘-click, ⇧-click or ⌘A, and accept, dismiss or reopen them in one step.
Skip an object or check it with rules only, each with a reason. The scope applies to every later scan, and the reports list what was left out. A skipped object never counts as fixed.
Turn built-in rules off or change their severity, write custom rules as regular expressions with a live test, and share them as JSON. MDM can deploy one rule set and lock editing.
Watch a scan: progress, time left, the object under review and its stage, and the findings so far. If the AI fails, the scan pauses and retries every minute, and a stopped scan resumes where it left off.
The AI runs on the Mac by default. If a larger model is worth it, an admin can point Lynceus at the company's own OpenAI-compatible server through MDM. Secrets are masked before anything is sent, and AI can be turned off for rules-only scans. No telemetry.
All screenshots show a made-up demo tenant, "Demo Corp". No real organization data.
The read-only API role, scanning Gimle backups, managed preferences, the AI options, how findings are verified and scored, and where Lynceus keeps its data.
📖 Read the DocumentationmacOS 27 or later, Apple Silicon only.
The bundled model needs about 2.7 GB of free memory; on an 8 GB Mac, close other apps before a scan.
For live scans: a Jamf Pro API client with a read-only role.
Version 0.2.0. Signed with a Developer ID certificate and notarized by Apple. The 2.7 GB package includes the Gemma 4 E2B model (Apache 2.0).
54 unit tests pass in 17 suites (Swift 6, strict concurrency).
Tested in a demo environment only, not against a production Jamf Pro. The AI review was checked against the real bundled model on demo scripts. Pausing and retrying when the AI fails was tested end to end with a local fake server.
Years of scripts pile up in every Jamf Pro: passwords in clear text, downloads run as root, security controls switched off. Lynceus finds them and shows which ones still reach devices.
Concept app · Free · Signed & Notarized · macOS 27+ · Apple Silicon · 2.7 GB
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