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python-ldapdomaindump

open-source

Dumps Active DirectoryActive DirectoryMicrosoft's directory service for Windows networks that centralises authentication and authorisation; cyber tools target or integrate with it because it controls identity and access across most enterprise environments. users, groups, computers, and policies via LDAP and outputs results in HTML, JSON, and greppable formats.

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Summary

python-ldapdomaindump is an open-source command-line tool that connects to Active Directory over LDAP and dumps users, groups, computers, domain policy, and trust relationships into HTML, JSON, and greppable output formats. It authenticates with a username and password or an NTLMNTLMA Windows authentication protocol that proves knowledge of a password through a challenge and response. Captured authentication exchanges can be abused in relay attacks when protections are absent. hash, or it can piggyback on an existing LDAP session, which lets it plug into relaying tools like Impacket's ntlmrelayx, and it can convert its output into BloodHound-compatible CSVs. It is built for penetration testers doing internal network recon rather than for ongoing AD monitoring. It sits alongside other LDAP-based domain enumeration recon tools used in this phase of an engagement, without the project naming a specific peer. Started in 2016 with 14 contributors, it has had no commits in the past year and its last release was 504 days ago.

Dumps Active Directory users, groups, computers, and policies via LDAP and outputs results in HTML, JSON, and greppable formats.

How python-ldapdomaindump helps

Which computers are joined to this domain and what are they running?

pulls all computer accounts from LDAP and sorts them by operating system into a dedicated grouped file, so aging or unexpected systems stand out

What password and lockout rules actually apply to this domain?

reads the domain policy over LDAP and writes it out as a readable file covering requirements like password complexity and account lockout thresholds

What trust relationships could let an attacker pivot out of this domain?

enumerates incoming and outgoing domain trusts along with their properties into a dedicated output file

Can I turn a credential I only have relayed access to into a full picture of the domain?

accepts an existing authenticated LDAP session, including one handed off by a relaying tool, so the dump can proceed without ever needing a plaintext password

Can I feed what I find here straight into graph-based attack path analysis?

converts its own JSON output into CSV files formatted for BloodHound, so the same enumeration data can be explored for privilege escalation paths

all 6 features, with the evidence for each →

Languages

Not documented

Features

6 capabilities · 4 backed by code, an API document or a real run

Built from python-ldapdomaindump's releases, docs, README and our own runs. “Seen” marks the month we first spotted a release the vendor didn't date. Group names and tags are our labels.

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  • python-ldapdomaindump covers 1 of the 13 network discovery and scanning capabilities we track.

What python-ldapdomaindump offers you

  • Command-line interface3
  • CSV data export2
  • JSON data export2
  • Offline mode1
  • Python scripting support1
All capabilities 6 capabilities
LDAP domain enumeration verified Connects to an LDAP server to query domain users, groups, and policy attributes, with options to limit queried attributes and support SSL connections. 4 other sources · first seen Sep 2018

Shared capability: Attack surface inventory

Also counts toward: Command-line interface, JSON data export

docs

  • Dumps Active Directory users, groups, computers, and policies via LDAP and outputs results in HTML, JSON, and greppable formats.

command line

  • host — Hostname/ip or ldap://host:port connection string to connect to (use ldaps:// to use SSL) v0.9.0 · Oct 2018 · command-line history
  • --minimal — Only query minimal set of attributes to limit memmory v0.8.7 · Sep 2018 · command-line history

example

  • Run basic LDAP domain dump ldapdomaindump
ldapdomaindump file ingestion verified Loads previously dumped ldapdomaindump JSON files from a path or directory to analyse domain users, groups, and policy data offline. 3 other sources · first seen May 2018

Also counts toward: Command-line interface, CSV data export, Offline mode

command line

  • --directory — The ldapdomaindump directory where the json files are saved. Required files: domain_users.json, domain_groups.json and domain_policy.json v0.9.3 · May 2020 · command-line history
  • files — The ldapdomaindump json files to load. Required files: domain_users.json and domain_groups.json v0.8.6 · May 2018 · command-line history

example

  • Convert JSON dumps to BloodHound CSVs python -m ldapdomaindump.convert
Output format customization shipped Converts or exports the dumped data into various formats such as greppable text, enum4linux-like structures, and BloodHound-compatible CSVs. 3 other sources

Also counts toward: CSV data export, JSON data export

docs

  • Dumps Active Directory users, groups, computers, and policies via LDAP and outputs results in HTML, JSON, and greppable formats.

example

  • Output dumps in enum4linux-like format python -m ldapdomaindump.pretty
  • Convert JSON dumps to BloodHound CSVs python -m ldapdomaindump.convert
Debug logging verified Enables a debug-level logger to provide verbose output during tool execution. 1 other source · first seen May 2018

command line

LDAP authentication verified Authenticates to the LDAP server using domain credentials supplied by the user. 1 other source · first seen Sep 2017

Also counts toward: Command-line interface

command line

  • --user — DOMAIN\username for authentication, leave empty for v0.8.5 · Sep 2017 · command-line history
Python module importability shipped Allows the cyber tool to be invoked and executed programmatically as a Python module rather than solely as a standalone CLI application. 1 other source

Also counts toward: Python scripting support

example

  • Run as Python module python -m ldapdomaindump

Lines in monospace are the tool's own words: help text parsed from its source, or an endpoint from its API document. Under release, a line is our summary of a dated release. Under docs, it is quoted from the tool's own documentation or website. Each links back to the source it came from.

Score card

  1. Shipping velocity releases a month, last 12 months
  2. Documentation quality release-notes rubric, 0–100
  3. Breaking-change discipline how rarely a release breaks users, and notice given
  4. Maintenance risk quiet time against its own cadence, maintainers
  5. Innovation & surface new capabilities, API and MCP surface
  6. Community health
  7. Contributor trend distinct committers, this year against last
  8. Maintainer concentration bus factor now against a year ago
  9. Issue backlog trend issues closed per issue opened, 12 months
  10. Responsiveness median time to a maintainer’s first reply

Nine scores for python-ldapdomaindump, 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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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 python-ldapdomaindump 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.

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Releases

Nothing shipped since we started watching. Releases and docs updates land here as they happen.

last release Apr 4, 2025 · watching since Jun 24, 2026

Project health

Issue, contributor and maintainer activity in dirkjanm/ldapdomaindump, 2023-10 – 2026-09, as of 2026-09-25. Twelve-month figures first; the trend lines run the whole window.

  1. Contributors 0 −2 vs prior 12 months
  2. Maintainers 0 +0 joined · −0 left, last 12 months
  3. Bus factor 0 committers behind half the commits
  4. Community-filed 100% of issues opened, last 12 months
  5. Median time to close 12 days first maintainer reply 12 days
  6. Open backlog 13 of 52 issues ever filed
  1. Issues opened / month 0 · 0 communitymaintainers
  2. Open backlog 13
  3. Contributors / month 0
  4. Commits / month 0
  5. Stars 1,429
Issue themes, last 12 months
  • other 100%

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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