Ibex – a cross-platform iOS backup decryption tool

Hacker News

ibex is a cross-platform tool designed for decrypting and extracting iOS backups. It provides forensic investigators, security researchers, and power users with the ability to access and analyze encrypted iOS backup data. It can be built and used on macOS, Linux, and Windows and is permitted to be used only with the explicit and informed consent of the backup data owner. Ibex was written in Go for straightforward compilation and to circumvent dependency issues and with the goal of enabling researchers and defenders assisting civil society victims of spyware and stalkerware Key Features - Decrypt encrypted iOS backups - Support for latest iOS versions - Cross-platform compatibility (macOS, Windows, Linux) - Automatic backup detection - Single file extraction based on filename match - Structured output organization - Detailed manifest parsing and extraction Basic Usage Examples # Run with automatic backup detection and interactive mode ibex # Specify just the backup path ibex -b /path/to/backup # Specify backup path and password ibex -b /path/to/backup -p "backup_password" # Specify custom output directory ibex -b /path/to/backup -p "backup_password" -o /path/to/output # Specify a single file for decryption and extraction ibex -b /path/to/backup -o /path/to/output --file sms.db # Specify relative path preserved output ibex -b /path/to/backup -o /path/to/output -r

Visit product View on Hacker NewsApr 19, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, macos, user · Missing: agents, agent, cursor

74%74% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Strong signals: ios · Missing: supports, reddit linkedin, podcasting

71%71% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: platform, users · Missing: plus, intuitive, reviews

51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: ios, users · Missing: mobile apps, personal, entrepreneurs

50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

36%36% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue

14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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