mac_apt
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macOS (& ios) Artifact Parsing Tool
python mac_apt.py -i <image.dmg> -o <output_dir> CALCULATOR python mac_apt.py -i <image.dmg> -o <output_dir> AKD mac_apt.py -p KEYCHAINS --verbose mac_apt.py -p LAUNCHPAD,FACETIME --verbose mac_apt.py -p FIREFOX,CHROMIUM /path/to/image.dmg mac_apt -i /path/to/collection.zip --_input_type AXIOMZIP mac_apt -pf /path/to/password_file mac_apt.py -f /path/to/recon_image.sparseimage mac_apt.py -p SAFARI /path/to/System/Library/Safari mac_apt.py -p Imessage /path/to/iMessage.db > imessage_alias.json mac_apt ios_apt APPS -i <ios_disk_image>.img mac_apt -p macos_big_sur_plugin mac_apt -p ios14_db_plugin mac_apt -p all --recovery-key <RECOVERY_KEY> <PATH_TO_ENCRYPTED_VOLUME> mac_apt -p all --fast sqlite3 <MAC_APT_OUTPUT_DIR>/export_log.db 'SELECT * FROM logs;' mac_apt.py --plugin SCREENTIME,COOKIES -o json -f screendata.json mac_apt.py -o aff4 -f output.aff4 mac_apt -p recentitems ./mac_apt.py -f ./disk.dmg iMessage ./mac_apt.py -f ./disk.dmg SAFARI ./mac_apt.py -f ./disk.dmg RECENTITEMS
Summary
mac_apt is an open-source, Python-based command-line tool for processing macOS and iOS disk images (or live machines) to extract forensic artifacts through individual plugins, covering things like Safari history, keychains, recent items, and Apple Biome data. It's aimed at DFIRDFIRDigital forensics and incident response, investigating what happened during a security incident and containing it. The work includes preserving evidence, reconstructing activity and restoring systems. practitioners handling Mac and iOS evidence rather than general incident response, and it reads a wide range of source formats including E01E01A forensic disk image format that stores a drive's contents in segmented files with built-in checksums and metadata, widely used in digital forensics because it supports compression and chain-of-custody verification., VMDK, DD, AFF4, UAC and Velociraptor collections, mounted images, and encrypted APFSAPFSApple's proprietary filesystem introduced in 2017, replacing HFS+, designed for SSDs with native encryption, snapshots, and space sharing — giving cyber tools reliable forensic primitives on modern Apple devices. volumes, writing results out to XLSX, CSV, TSVTSVA plain-text format storing tabular data with fields separated by tab characters, making it easy for cyber tools to parse and exchange structured records without a CSV parser's quoting complexity., JSONLJSONLA text format where each line is a self-contained, valid JSON object, making it easy to stream, append, and process large datasets line by line without parsing a full JSON array into memory. or SQLite. It sits in the macOS/iOS forensics niche of the broader DFIR category rather than competing with general-purpose triage suites. First committed in 2017, it remains active, with 13 contributors, 60 commits in the past year, and a release 32 days ago.
macOS (& ios) Artifact Parsing Tool
How mac_apt helps
Which files were opened, downloaded, or recently accessed on this Mac before it was imaged?
reads the Recent Items and volume bookmark data (including SFL4 records and creation dates) and lists what was accessed, from where, and when
What did someone browse or search for on this Mac?
parses Safari history and related artifacts from the disk image or live machine and reports visited URLs with timestamps
What credentials or secrets are stored in this Mac's keychain?
parses the keychain file directly, unlocking it with a supplied password or key file, and lists the stored items
Can this image be processed even though the disk was encrypted or the volume is sealed?
opens encrypted APFS volumes using a supplied password or recovery key, and handles Big Sur sealed system volumes natively
Does this collection have to come from a full raw image, or can it come from the response tooling already in use?
reads E01, VMDK, AFF4, DD, sparseimage, UAC collections, Velociraptor targeted collections, and Axiom collection zips directly, so the same plugins run regardless of how the evidence was gathered
Languages
Command line
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| option | found in | since | description |
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Values are placeholders taken from each option’s declared default. Nothing is executed here — the output shown is a recording of a run that already happened.
Score card
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Shipping velocity releases a month, last 12 months
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Documentation quality release-notes rubric, 0–100
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Breaking-change discipline how rarely a release breaks users, and notice given
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Maintenance risk quiet time against its own cadence, maintainers
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Innovation & surface new capabilities, API and MCP surface
- Community health
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Contributor trend distinct committers, this year against last
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Maintainer concentration bus factor now against a year ago
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Issue backlog trend issues closed per issue opened, 12 months
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Responsiveness median time to a maintainer’s first reply
Nine scores for mac_apt, each built from the releases, notes and API history this site tracks — and, on Researcher and Business, where it stands among every measured tool in its category and how that has moved.
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Blank means never measured; nothing here is a filled-in zero.
Peer standings are included with Researcher and Business. See how this tool ranks against its competitors, using data we don't publish anywhere else.
See plans →Each score is 0–100 on a published rubric, derived from data this site already shows. A dimension we cannot measure says so — it is never scored 0 and never enters a rank.
How this is scored
- Shipping velocity — releases a month over the last 12 months, on a log scale where 16 a month scores 100. Needs 3 releases in the window.
- Documentation quality — the release-notes rubric published on the analytics page, unchanged.
- Breaking-change discipline — the share of releases carrying a breaking change (none scores 100, half or more scores 0). Only projects that label breaking changes are measured: a clean record cannot be told from an unlabelled one.
- Maintenance risk — quiet time measured against the tool's own release cadence (quiet longer than 90 days and three typical gaps scores 0), plus the number of active maintainers and whether contributors are growing, where known. Higher is lower risk.
- Innovation & surface — new capabilities in the feature ledger this year, API and MCP operations added, and the share of commits that are features: the mean of whichever we hold.
- Community health (open-source tools only) — each is a direction, not a size: contributors this year against last, the bus factor now against a year ago, and issues closed per issue opened, where steady scores 50, doubled 100 and halved 0; plus the median time to a maintainer's first reply (an hour scores about 100, a month or more 0). A commercial card has no repository and carries none of the four.
- Documented delivery (commercial tools) — when a vendor's documented new capabilities have slowed against its own baseline, that counts against the overall standing as heavily as two low dimensions. Steady delivery, or too little history to judge, leaves the standing alone; it is never a sixth score.
- Open-source tools are scored on repository signals; commercial tools on their published changelog. Ranks compare tools within one category and one profile and need at least five measured peers; the overall standing needs three ranked dimensions. Best-in-breed or consolidator is a shape, not a quality, and is never scored.
Release-note detail over time
See how much detail mac_apt puts in its release notes, quarter by quarter, and compare it with tracked tools in the same category. This history uses up to ten retained, reviewed notes per tool; sparse or blank older quarters may reflect that limit.
Checking release-note history…
Releases
21 entries
No entries match these filters.
- October 20261
- July 20261
- February 20261
- December 20251
- July 20251
- May 20251
- May 20241
- June 20231
- June 20221
- July 20211
- May 20212
- March 20211
- February 20211
- December 20202
- September 20201
- May 20201
- April 20201
- August 20191
- June 20181
Project health
Issue, contributor and maintainer activity in ydkhatri/mac_apt, 2023-12 – 2026-11, as of 2026-11-03. Twelve-month figures first; the trend lines run the whole window.
- Contributors 5 +3 vs prior 12 months
- Maintainers 1 +0 joined · −0 left, last 12 months
- Bus factor 1 committers behind half the commits
- Community-filed 74% of issues opened, last 12 months
- Median time to close 2.6 days first maintainer reply 23 hours
- Open backlog 8 of 111 issues ever filed
- Issues opened / month 4 · 0 communitymaintainers
- Open backlog 8
- Contributors / month 0
- Commits / month 0
- Stars 1,087
- bugs 42%
- features 5%
- other 53%
Counts from the project's public repository and git history; no individuals are identified. Maintainers are the committers covering 80% of the last year's commits. how we measure this
Libraries its release ships
What its latest release locks, checked against known advisories.None of its release's images build from a lockfile we can read.
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