# DanMcInerney/net-creds

Sniffs sensitive data from interface or pcap

Repository: https://github.com/DanMcInerney/net-creds
Canonical: https://ross.abutalabs.com/products/net-creds
Language: Python
License: GPL-3.0
License Family: copyleft
Last push: 2023-11-02T10:46:03+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 4256, "days_push": 1035, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1859, forks 438 (observed 2026-08-28T04:05:45.260631+00:00)

## What it is
A Python command-line tool that sniffs passwords, hashes, and other sensitive data from a live network interface or a pcap file. It reassembles fragmented packets and identifies services without relying on port numbers.

## Use cases
- sniff passwords and hashes from network traffic
- extract credentials from a pcap capture file
- capture HTTP basic auth and form logins on a network
- detect NTLMv1/v2 and Kerberos authentication traffic
- analyze cleartext protocol logins like FTP, IMAP, POP, Telnet, SMTP

## When to choose
- you need to demonstrate cleartext credential exposure during a pentest or security audit
- you want to analyze saved pcap files for leaked credentials
- you need protocol detection independent of port numbers

## When to avoid
- you need encrypted traffic analysis (HTTPS, SSH)
- you want a maintained tool with modern Python 3 support
- you need a GUI or passive IDS-style alerting rather than a CLI dump

## Facets
- artifact type: cli-tool
- maturity: maintenance
- function: security, networking, osint
- domain: security, penetration-testing, networking
- platform: python, cli
- tags: packet-sniffing, credential-sniffing, pcap-analysis, password-harvesting, scapy, command-line, linux, macos

## Member repositories
- DanMcInerney/net-creds (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:45.260631+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:15:57.778029+00:00, confidence not recorded.
  - readme: https://github.com/DanMcInerney/net-creds (fetched 2026-08-28T04:05:45.260631+00:00, sha ea41ee7f8a20)
- Data as of 2026-08-30T08:39:29.467469+00:00.