LLM Security Toolkit Review: Offline AI Security App for Android
If your team ships chatbots, retrieval assistants or AI agents to production, two questions keep coming up: how secure is the system right now, and what do we do first when something goes wrong? This review looks at LLM Security Toolkit, an LLM security app for Android by ChatStick Co., Ltd. that puts assessments, checklists, runbooks and a security guide on one device, with no network connection required.
What LLM Security Toolkit is
According to its Google Play listing, the app is an offline toolkit for builders, security teams and students who need to assess and harden production LLM applications. It runs on Android phones and tablets, sits in the Education category, and the developer states it is designed for educational and defensive use.
Privacy is a strong point. The Data safety section on Google Play says no data is collected and none is shared with third parties, and the description adds that there is no account, no ads and no tracking, with all progress and assessment data kept on the device. That matters when your notes describe internal architecture you would rather not upload anywhere.
Seven interactive assessment tools
The core of the app is a set of tools that turn a vague sense of being secure enough into scores you can track over time.
OWASP GenAI Top-10 Scorecard: rate exposure across ten risk categories and get a weighted residual-risk score.
AI Security Maturity Assessment: answer per-domain questions to land on a maturity level from 1 to 5.
Prompt Injection Defense Coverage: toggle the controls you already have and see your weighted coverage.
Incident Severity Calculator: score an AI incident from Low to Critical in a CVSS-style model.
Payload Library: test payloads with what each one tests, its mitigation and severity, copyable to the clipboard, for authorized testing only.
Incident Response Runbooks: phased Detect, Contain, Eradicate, Recover and Review steps with a live timer.
Security Posture Report: combine results into one posture score, follow the trend and export as text or PDF.
Checklist, built-in guide and learning mode
Beyond scoring, the app helps a whole team build a shared understanding of the threat landscape.
Hardening checklist: phased steps mapped to real attack categories.
Built-in practical guide: chapters and diagrams on prompt injection, indirect prompt injection, data exfiltration, model extraction, insecure output handling, supply chain risk, incident response and agentic AI governance, with adjustable text size, bookmarks and resume.
Learn area: chapter quizzes with instant feedback, a timed exam mode, flashcards, a glossary, achievements and a diagram gallery.
Progress dashboard: overall progress, daily tips, streaks and achievements.
A quick workflow to get started
Run the OWASP GenAI Top-10 Scorecard to see which categories carry the most risk.
Open Prompt Injection Defense Coverage and toggle the controls you have to expose the gaps.
Work through the hardening checklist phase by phase, then re-assess.
Rehearse with the incident response runbooks and rate scenarios with the severity calculator.
Wrap up with a Security Posture Report and export a PDF for your team.
App details
Name: LLM Security Toolkit by CS
Platform: Android, via Google Play
Price: paid app; Google Play shows the price in your local currency (US$2.99 on the US store)
Ads / in-app purchases: the store page does not list ads or in-app purchases, and the description states there are no ads
Usage: offline, no account needed
Developer: ChatStick Co., Ltd.
FAQ
Does it need an internet connection?
No. The Google Play description says the toolkit works offline and keeps assessment data on your device.
Are there ads or in-app purchases?
The store page does not list ads or in-app purchases, and the developer states the app has no ads and no tracking.
Can I use the Payload Library against any system?
No. The developer states the app is for educational and defensive use, and the Payload Library is for authorized testing only.
Who is it for?
Developers building LLM applications, security teams, and students who want a structured way to learn generative AI risks.
Conclusion
If you look after an LLM-powered system and want one place to assess risk, plan hardening work and rehearse incidents without sending data off the device, LLM Security Toolkit covers that ground with scorecards, checklists, runbooks and a learning mode.
A practical way to use it is to record a baseline scorecard first, then re-assess after each round of fixes. The Security Posture Report makes the trend easy to show to stakeholders.
It also works well as a study companion. Students and engineers new to AI security can read a guide chapter, take the matching quiz, and then try the related assessment tool, which connects concepts such as indirect prompt injection or insecure output handling to concrete controls you can check off.
Browse more Android apps from ChatStick on the ChatStick Co., Ltd. developer page on Google Play.
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