Security Review Checklist for Micro Apps Built with LLMs and Desktop AI Tools

Hook: Why desktop AI micro apps are the new approval bottleneck

In 2026, teams approve more tiny AI-powered micro apps and micro apps than ever before. These apps are fast to build, often by non-developers, and they pack powerful capabilities: local LLMs, autonomous agents with direct file system access, and hardware-accelerated inference on devices like Raspberry Pi 5 with AI HATs. That speed and capability create an approval problem: how do you safely onboard a tool that reads files, calls LLMs, or runs background agents without introducing data leakage, supply-chain risk, or runtime escalation?

Executive summary: The checklist in a sentence

Use this security review checklist to evaluate micro apps and desktop AI tools before granting approval. Focus areas: threat modeling, data handling, dependency and build review, runtime protections, and operational readiness. The checklist converts security questions into concrete verification steps, commands, and gating criteria you can automate into CI/CD and IT approval flows.

The 2026 context: What changed and why this matters now

Recent product and platform shifts accelerated the risk surface for desktop AI. In late 2025 and early 2026, several trends made reviews indispensable:

• Desktop agents with file system access became mainstream. Research previews from major AI labs now ship agents capable of reading, writing, and modifying user files—powerful but risky for org data governance.
• Edge AI hardware and low-cost HATs for devices like Raspberry Pi 5 made local LLM inference viable, increasing on-device data processing but also expanding where sensitive data can land.
• The micro app movement means more apps are created by domain experts and non-developers, driving fast innovation but lowering initial security hygiene.
• Supply chain visibility, SBOMs, and runtime attestation are now expected standards in many enterprise procurement processes.

How to use this checklist

1. Run a quick triage from the quick approval checklist on new apps.
2. If the app touches sensitive data or requires elevated permissions, run the deep-dive steps for each area.
3. Automate technical checks into your intake pipeline: dependency scans, SBOM validation, and runtime policy templates.
4. Store results in a central approval ticket and apply a gating decision matrix (approve, require mitigation, deny).

Quick approval checklist (5-minute triage)

• Does the app require network access? If yes, which endpoints and why?
• Does the app request access to file system locations beyond its installation folder?
• Is the app signed and distributed via an official store or vendor site with integrity checks?
• Does the developer provide an SBOM or dependency manifest?
• Are there privacy claims about storing or sending PII to third-party LLMs?

Deep-dive sections

1. Threat modeling: a fast, repeatable process

Threat modeling for micro apps should be lightweight but structured. Use the STRIDE categories to identify high-impact risks and produce mitigations you can validate.

1. Identify assets: user files, credentials, API keys, internal network resources, corp data. Example: a personal dining app may access contacts and location.
2. Identify trust boundaries: app sandbox vs OS, local LLM vs cloud LLM, agent vs user process, signed updater vs arbitrary download.
3. Map threats to capabilities. Example: an agent with write access plus network creates a risk of exfiltration of sensitive files.
4. Document attack scenarios with likelihood and impact, then assign controls and acceptance criteria.

Example minimal threat model item:

Threat: Data exfiltration via third-party LLMs. Control: Block outbound requests except to company-approved LLM endpoints; require prompt redaction of PII; verify TLS with pinned roots. Acceptance: Network egress policy present and enforced; automated test demonstrates blocked connection to unapproved endpoint.

Checklist: threat modeling verification

• Documented asset list and trust boundaries
• At least one concrete attack scenario and mitigation
• Approval condition threshold (e.g., require mitigations if high impact)

2. Data handling: protect data at rest, in memory, and in transit

Desktop AI tools are judged by how they treat data. The worst outcomes are accidental leakages of PII or corporate secrets into third-party LLMs or cloud telemetry.

1. Data classification. Require the app owner to declare the highest classification of data the app may access.
2. Input hygiene and prompt redaction. For LLM calls, enforce pre-send scrubbing of PII and secrets.
3. Local storage policies. Use encrypted storage for caches and models; avoid persistent plaintext storage of user content.
4. Telemetry and logging. Define what telemetry is sent and provide an opt-out for sensitive contexts; redact PII before logging.
5. Retention and deletion. Specify retention windows and proof of deletion for cached model artifacts or transcripts.

Practical checks and sample commands

Ask the dev for a data-flow diagram and run these validation steps.

• Check outbound network calls from the app with a temporary proxy or packet capture during a scripted run.

sudo tcpdump -i any host example-llm-endpoint.com or port 443

Validate local storage locations and permissions. On macOS, check the app sandbox entitlements; on Linux, check AppArmor or flatpak sandbox rules.

ls -l /path/to/app/data
strings /path/to/app/binary | grep -i token

Checklist: data handling verification

• Data classification declared and approved
• Prompt redaction implemented and tested
• Encrypted local caches or explicit exception documented
• Telemetry and retention policies documented

3. Dependency review and supply chain

Micro apps often depend on many OSS packages. A small app can inherit dozens of transitive dependencies. In 2026, SBOMs and automated dependency scanning are standard gating checks.

Required artifacts from the developer before approval:

• Lockfile or package manifest (package-lock.json, poetry.lock, requirements.txt with hashes)
• SBOM in CycloneDX or SPDX format
• Build instructions and deterministic build evidence if possible

Tools and commands to run

Common toolchain commands you can run in CI or locally:

# For Node projects
npm ci --package-lock-only
npm audit --json > node-audit.json

# For Python projects
pip install pip-audit
pip-audit -r requirements.txt --format json > py-audit.json

# Generate SBOM with syft
syft path/to/artifact -o cyclonedx-json=sbom.json

# Scan container images
trivy image --format json -o trivy-report.json myorg/myapp:latest

Checklist: dependency verification

• SBOM provided and validated
• No critical or high unmitigated CVEs in direct deps
• Lockfiles present and reproducible build demonstrated
• Third-party binaries (including native wheels) reviewed or blocked

4. Build, signing, and distribution

How the app is built, signed, and distributed is critical. Unsigned or tamperable distribution channels are a major risk.

• Require code signing for macOS and Windows builds. For macOS, verify notarization. For Windows, require Authenticode signing.
• Prefer store distribution (App Store, Microsoft Store) or vendor-hosted releases with reproducible checksums.
• For auto-updaters, require signed update payloads and use pinned public keys for verification.

Example verification commands:

# Verify a macOS code signature
codesign --verify --deep --strict /Applications/MyApp.app

# Verify checksum
shasum -a 256 MyApp.dmg

Checklist: build and distribution

• Signed binary or notarized app present
• Integrity checks and update signing enforced
• Distribution channel approved

5. Runtime protections and least privilege

Enforce principle of least privilege at runtime. Treat desktop AI tools as potential attack vectors for local escalation and lateral movement.

• Restrict file access to approved directories only. Use OS sandboxing or platform-specific entitlements.
• Limit network access. Block egress except to whitelisted, audited endpoints; if cloud LLMs are used, prefer enterprise endpoints with contractually bound data usage policies.
• Run background agents with minimal privileges and require explicit user consent for persistent services.
• Use process-level hardening: enable ASLR, DEP, stack canaries where possible.
• Integrate with endpoint security. Ensure EDR or MDM policies monitor or manage the app lifecycle.

Practical runtime checks

# On Linux: view AppArmor profile if provided
sudo aa-status

# Confirm the process runs as an unprivileged user
ps aux | grep MyApp

# Confirm app did not open unexpected listeners
ss -lntp | grep MyApp

Checklist: runtime protection verification

• Sandbox or entitlements in place
• Network egress policy enforced
• EDR/MDM integration verified
• Auto-update implements signed payloads

6. Observability, incident response, and forensics

Approving an app means you accept operational responsibility if it misbehaves. Require minimal observability and a plan for incident response.

• Logging: what the app logs, where logs are sent, and redaction controls.
• Telemetry: require opt-in for telemetry that could include content data.
• Forensics: if the app runs agents, require forensic artifacts and a crash dump policy.
• Escalation: define an incident owner and SLA for patching critical vulnerabilities.

Checklist: operational readiness

• Telemetry and logs documented and controllable
• Incident contact and SLA defined
• Rollback plan for auto-updates and emergency uninstall procedure

Decision matrix: approve, require mitigation, or deny

Use a simple scoring model to drive decisions. Assign points against the following failure modes: data exfiltration potential, unsandboxed FS access, unvetted dependencies, unsigned distribution, and no operational plan. Thresholds are example values you can tailor.

• 0-3 points: Approve with monitoring
• 4-7 points: Require mitigations before approval
• 8+ points: Deny until redesign

Example scoring: unsandboxed FS access = 3, unvetted transitive deps with high CVEs = 3, outbound to generic public LLM = 2 → 8 points → Deny

Example: approving a personal micro app (case study)

A small team built a micro app to produce meeting summaries using an LLM. It runs on macOS and uses a local sqlite cache. Applying the checklist we found:

• No SBOM provided. Developer supplied requirements.txt with no hashes.
• Network egress to a public LLM without contractual data protections.
• App was unsigned and installed outside the company MDM channel.
• Telemetry included snippets of meeting content by default.

Outcome: mitigation required. Developer produced a CycloneDX SBOM, switched to the company LLM endpoint, removed content telemetry, added prompt redaction, and produced notarized macOS builds. Post-mitigation checks passed and the app was approved with an MDM policy to enforce sandboxing.

Automation: integrating checks into intake pipelines

To scale approvals, automate parts of this checklist. Recommended pipeline steps:

1. On upload, generate SBOM with syft and run grype/trivy scans.
2. Run static analysis and secret scanning on source or packaged artifacts.
3. Execute a small sandboxed instrumentation run to capture network egress and file access patterns.
4. Produce a report artifact attached to the approval ticket; block approvals when critical fails appear.

Example GitHub Actions snippet (conceptual):

jobs:
  build-and-sbom:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - run: syft . -o cyclonedx-json=sbom.json
      - run: grype sbom:sbom.json -o json -q > grype.json

Policy templates and guardrails (copy/paste)

Minimal policy you can require from any micro app owner before approval:

The app must provide a signed binary or notarized build, a current SBOM in CycloneDX, documented data flows, an endpoint whitelist for any external APIs, and a removal clause that deletes cached data within 7 days of uninstall. Telemetry is disabled by default or must be opt-in. Sensitive corporate data must not be sent to public LLMs without a legal and contractual review.

Future predictions: what security teams should prepare for in 2026+

• More on-device LLMs will reduce cloud egress but increase risks of local exfiltration and hardware-attestation needs; expect attestation APIs to become mainstream for device-local inference.
• Auto-agents with multi-step workflows will require behavior whitelisting rather than static permission checks; runtime policy engines that understand agent intent will be a hot area.
• SBOMs will become richer, tying binary provenance to CI signatures; automated attestation of build pipelines will be a competitive differentiator for vendors.

Quick reference: checklist matrix (one page)

• Threat modeling: documented + mitigations? — Yes/No
• Data classification and redaction: present? — Yes/No
• SBOM and dependency scan: present/low risk? — Yes/No
• Signed/notarized distribution: present? — Yes/No
• Sandboxing / entitlements: in place? — Yes/No
• Telemetry opt-in & retention policy: present? — Yes/No
• Incident response owner and SLA: present? — Yes/No

Final actionable takeaways

• Integrate SBOM generation and dependency scanning into app intake flows now.
• Make least privilege and network egress whitelists non-negotiable for AI-powered desktop apps.
• Require signed builds and signed auto-update payloads to prevent tampering.
• Automate lightweight threat modeling for every app that touches internal data.
• Maintain a short denial/mitigation loop: micro apps change quickly—reviews must be fast, repeatable, and automated where possible.

Call to action

Use this checklist as a living document. Start by adding SBOM and network egress checks to your app intake pipeline this quarter. If you want a ready-to-run audit playbook and automated CI templates tailored to your environment, download our kit or contact our team for a 30-minute review session. Protect your data without slowing innovation—approve safely.

Related Reading

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• Tool Sprawl Audit: tame your micro apps and integrations (simpler.cloud)
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