# client9/libinjection

SQL / SQLI tokenizer parser analyzer

Repository: https://github.com/client9/libinjection
Canonical: https://ross.abutalabs.com/products/libinjection
Language: C
License: NOASSERTION
License Family: other
Last push: 2023-10-04T14:35:31+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 5167, "days_push": 1064, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1032, forks 282 (observed 2026-08-28T04:03:18.180048+00:00)

## What it is
A C library that tokenizes and analyzes input strings to detect SQL injection (SQLi) attacks using fingerprint matching. It has bindings for PHP, Python, and Lua, and is commonly embedded in web application firewalls.

## Use cases
- detect sql injection attacks in user input
- add sqli detection to a web application firewall
- scan request parameters for sql injection payloads
- integrate sql injection detection into a php or python app
- fingerprint malicious sql queries

## When to choose
- you need fast, lightweight SQLi detection in C/C++ or via bindings
- you are building a WAF or input filter that must flag SQL injection attempts
- you want a mature, widely deployed detection library with fingerprint output

## When to avoid
- you need general-purpose SQL parsing or query analysis rather than attack detection
- you need actively developed features or recent vulnerability updates
- you need detection for injection types beyond SQL (e.g., XSS, LDAP)

## Facets
- artifact type: library
- maturity: maintenance
- function: parser, security, nlp
- domain: security, parsers, web-development, developer-tools
- platform: c, cpp, python, php, lua, cross-platform
- tags: sql-injection-detection, sqli, tokenizer, waf, fingerprinting

## Member repositories
- client9/libinjection (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:18.180048+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 2026-08-30T07:06:44.437008+00:00, confidence not recorded.
  - readme: https://github.com/client9/libinjection (fetched 2026-08-28T04:03:18.180048+00:00, sha 3ba874eb5a37)
- Data as of 2026-08-30T08:39:29.467469+00:00.