arjun
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Arjun is an HTTP Parameter Discovery Suite that finds valid query parameters for URL endpoints using a default dictionary of 25,890 parameter names.
arjun -u https://api.example.com/endpoint --casing camel arjun -u https://target.example.com/search --ratelimit 5 arjun -u "https://example.com/page" --disable-redirects arjun -u https://example.com --passive arjun -u https://example.com -c 10 arjun -u https://example.com -T 30
Summary
arjun is an open-source command-line tool that discovers hidden HTTP parameters on URL endpoints by probing them against a dictionary of 25,890 names while keeping request counts low. It is free to use, installed via pipx, and run directly from a terminal against a single URL or a list imported from Burp Suite, a text file, or a raw request file. It suits penetration testers and bug bounty hunters doing web recon, fitting into the parameter-discovery and fuzzing stage of an engagement rather than replacing broader scanners. Options like --casing and --rate-limit let it match an API's naming style or stay under a WAFWAFA reverse proxy that inspects HTTP/HTTPS traffic and blocks requests matching known attack patterns such as SQL injection or XSS, sitting between clients and a web application to filter malicious input before it reaches the server.'s threshold during sensitive work. First committed in 2018 with 26 contributors and 41 tracked releases, it has had no commits in the past year, with its last release 656 days ago.
Arjun is an HTTP Parameter Discovery Suite that finds valid query parameters for URL endpoints using a default dictionary of 25,890 parameter names.
How arjun helps
Which hidden parameters does this endpoint actually accept?
probes the URL with a dictionary of 25,890 parameter names in a small number of chunked requests and reports back the names that produce a distinct response
Does this API expect its parameters in a particular naming style?
matches discovered parameter names to a chosen casing convention so results line up with how the target actually names them, cutting down false negatives from style mismatches
Can I probe this target without tripping its rate limits during a sensitive test?
caps outgoing requests to a set number per second so discovery stays under a WAF or server's threshold instead of firing all chunks at once
Where did this parameter list come from before I start probing?
pulls parameter candidates directly out of a JavaScript response or an imported request before adding them to the dictionary it tests
Languages
Command line
No command matches.
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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 arjun, 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 arjun 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
4 entries
No entries match these filters.
- November 20241
- September 20221
- April 20221
- December 20201
Project health
Issue, contributor and maintainer activity in s0md3v/Arjun, 2023-10 – 2026-09, as of 2026-09-25. Twelve-month figures first; the trend lines run the whole window.
- Contributors 0 −3 vs prior 12 months
- Maintainers 0 +0 joined · −0 left, last 12 months
- Bus factor 0 committers behind half the commits
- Community-filed 100% of issues opened, last 12 months
- Median time to close not measured issues closed, last 12 months
- Open backlog 20 of 158 issues ever filed
- Issues opened / month 0 · 0 communitymaintainers
- Open backlog 20
- Contributors / month 0
- Commits / month 0
- Stars 6,397
- 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
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