Scan results

    Calorie AI - Food Scan

    iOS

    AI-powered calorie and nutrition tracker that uses computer vision to identify foods from photos and estimate their nutritional content. Features meal logging, calorie tracking, and personalized dietary insights powered by artificial intelligence.

    CITT SCORE
    86
    out of 100
    TRUSTED

    Quick Verdict

    Best for: Health-focused users comfortable with standard analytics

    What It Means For You

    Usage and behavioral data is shared with Firebase Analytics, AppsFlyer, Facebook SDK, and TikTok Business SDK for analytics and ad attribution. Subscription data flows through RevenueCat, and AI features connect to OpenAI. User credentials are stored securely on-device and not synced to iCloud.

    Method and Limitations

    Static analysis only. CITT reviewed the code of this build without running the app, so runtime behavior was not observed. The findings below describe the analyzed build and may not reflect server-side controls or later versions. CITT makes no determination about compliance with any law or platform policy; it reports what the artifacts contain and cites the published guidance so a reader can compare.

    How CITT scores an app

    Findings (5)

    Data Security

    2 total
    2 Medium

    Code Security

    1 total
    1 Low

    Third-Party Risk

    2 total
    1 Medium
    1 Info

    Third-Party Services

    AppsFlyer, Facebook SDK, TikTok Business SDK, Firebase Auth, Firebase Firestore, Firebase Realtime Database, Firebase Analytics, Firebase Crashlytics, Firebase Installations, RevenueCat, Alamofire, DotLottiePlayer, OpenAI

    Security Strengths

    • HTTPS enforced throughout — no ATS exceptions or arbitrary loads permitted
    • Keychain credentials stored with device-bound access (kSecAttrAccessibleAfterFirstUnlockThisDeviceOnly), not iCloud-syncable
    • ATT (App Tracking Transparency) correctly implemented before ad SDK data collection
    • CryptoKit used for modern cryptography (SHA-256/384/512, Curve25519, ChaCha20-Poly1305)
    • OpenAI API key not hardcoded in binary — fetched at runtime reducing static extraction risk
    What changed since the last scan

    Version diff is on the Developer plan. See developer plans.

    Context Tags

    health
    camera
    ads
    sensitive data

    Package

    app.Calorie-Ai

    Version

    1.10.10 (build 1.0)

    Analysis Date

    Apr 14, 2026

    0

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    Recommendation: Use With Caution

    Food images are transmitted to OpenAI for analysis, and the app uses multiple ad-tracking SDKs. Not recommended for privacy-conscious users or those tracking medically sensitive dietary patterns.

    Key Findings

    Data Security - 2 findings (2 medium)

    Network Security - 0 findings

    Code Safety - 1 finding (1 low)

    Privacy - 2 findings (1 medium, 1 info)

    Privacy Concerns

    What Data is Collected

    • Personal information: account details, user profile
    • Health and biometric data: meal entries, calorie goals, weight history, heart rate, steps, active energy
    • Device information: device identifier, advertising identifier
    • Usage data: app interactions, meal logging patterns, exercise frequency
    • Food photos and descriptions: transmitted to OpenAI for calorie analysis

    Third-Party Data Sharing

    The following third parties may receive your data:

    • AppsFlyer - advertising attribution and user tracking
    • TikTok Business SDK - advertising attribution and behavioral analytics
    • Facebook SDK - advertising attribution
    • Firebase (Google) - backend services including analytics and crash reporting
    • RevenueCat - subscription management and purchase attribution
    • OpenAI - AI-powered food and calorie analysis

    Understanding the Scores

    Security: 91/100
    Privacy: 84/100

    Security Breakdown

    • Data Security: 87/100 - Health and dietary data stored with standard device protection; food images transmitted to a third-party AI service
    • Network Security: 100/100 - All communication secured in transit with no exceptions
    • Code Safety: 95/100 - Sound coding practices throughout with a minor access control gap in one interaction handler

    Privacy Breakdown

    • Data Collection: 90/100 - Collects health and biometric data with appropriate system permission prompts
    • Data Sharing: 88/100 - Food photos sent to OpenAI; behavioral data shared with three ad-tech companies
    • User Control: 93/100 - App Tracking Transparency prompt gives you control over ad tracking before it begins

    Positive Security Features

    • All data sent between the app and servers is protected in transit, with no exceptions to secure communication requirements
    • Saved account details are stored with device-bound protection and cannot sync to other devices
    • You are asked for permission before any advertising tracking begins
    • Modern, industry-standard methods are used for cryptographic operations within the app
    • The AI service access code is not permanently embedded in the app binary, reducing one specific class of risk

    Areas for Improvement

    GDPR / CCPA Compliance

    The app's privacy practices could be strengthened by:

    1. Clearer Disclosure for AI Food Analysis
      Users should be explicitly informed during onboarding that food photos and meal descriptions are transmitted to OpenAI, and given a clear option to opt out of this feature.

    2. Reduced Ad-Tech Footprint
      Running three simultaneous advertising attribution services in a health-category app with access to biometric data exceeds industry norms and could be reduced to a single analytics provider.

    Security Enhancements

    1. Stronger Local Data Protection
      Meal entries, weight history, and calorie data stored on device could use the highest available protection level so the database remains protected even after the device has been unlocked once since boot.

    2. Interaction Handler Validation
      Handlers that respond to signals from other apps on the same device should verify the source before processing parameters, reducing the potential for manipulation.

    Technical Context

    App Type: Health and nutrition tracker with AI food recognition and HealthKit integration
    Classes Analyzed: iOS binary (native Swift)
    Third-Party Services: 13 third-party services identified
    Context Tags: health, camera, ads, sensitive_data


    About This Analysis

    This security analysis was conducted by CITT (Can I Trust That), an independent security analysis service. We perform static code analysis on iOS applications to help users make informed decisions about app security and privacy.

    App Details

    Developer: Not available
    Version: 1.10.10 (build 1.0)
    Analysis Date: 2026-04-14
    Package: app.Calorie-Ai

    Analysis Limitations

    • Static analysis only (code review without running the app)
    • Based on IPA version 1.10.10 analyzed on 2026-04-14
    • May not reflect server-side security controls
    • Cannot detect all runtime behaviors

    Right of Reply

    Developer not yet contacted