Imported from jeremylongshore/tons-of-skills-marketplace (
plugins/security/penetration-tester/skills/detecting-sql-injection-patterns/SKILL.md). Install upstream withnpx skills add jeremylongshore/tons-of-skills-marketplace --skill detecting-sql-injection-patterns. Copyright stays with the author (MIT).
Detecting SQL Injection Patterns
Overview
SQL injection (CWE-89, OWASP A03:2021) remains one of the highest- impact and most-easily-introduced vulnerability classes. The fix is near-universal: use parameterized queries. The cause when introduced: an engineer concatenates user input into a SQL string because the ORM's parameterization mechanism wasn't obvious, or because they "just need to add a quick condition."
The scanner reads source files and grades each apparent SQL-string construction against the threshold table.
When the skill produces findings
| Finding | Severity | Threshold | Affected control |
|---|---|---|---|
| f-string with SQL keywords + user input | CRITICAL | f"SELECT * FROM users WHERE id = {user_id}" |
CWE-89 |
| String concat into SQL keyword string | CRITICAL | "SELECT ... " + var + " ..." |
CWE-89 |
| %-format SQL string | HIGH | "SELECT * FROM %s" % table_name |
CWE-89 |
.format() into SQL string |
HIGH | "SELECT {} FROM users".format(col) |
CWE-89 |
cursor.execute(f"...") |
CRITICAL | f-string passed directly to cursor.execute | CWE-89 |
sequelize.query with template literal |
HIGH | sequelize.query(\SELECT * FROM ${table}`)` |
CWE-89 |
| Knex / sequelize raw() with interpolation | HIGH | knex.raw('SELECT * FROM ' + table) |
CWE-89 |
Django .extra() with raw SQL |
MEDIUM | Model.objects.extra(where=['col = ' + val]) |
CWE-89 |
cursor.executemany with string-built query |
CRITICAL | Same risk as execute | CWE-89 |
JDBC Statement.execute with concat |
HIGH | Java pattern: not PreparedStatement | CWE-89 |
Rails where() with string interpolation |
HIGH | User.where("name = '#{name}'") |
CWE-89 |
Go db.Query with fmt.Sprintf |
HIGH | db.Query(fmt.Sprintf("...", arg)) |
CWE-89 |
Prerequisites
- Python 3.9+
- Target source tree on local filesystem
Instructions
Step 1 — Run the scanner
python3 ${CLAUDE_PLUGIN_ROOT}/skills/detecting-sql-injection-patterns/scripts/scan_sqli.py /path/to/repoOptions:
Usage: scan_sqli.py PATH [OPTIONS]
Options:
--output FILE Write findings to FILE
--format FMT json | jsonl | markdown (default: markdown)
--min-severity SEV (default: info)
--include-tests Include test directories (default: excluded)
--languages LIST Comma-separated: python,javascript,typescript,java,
ruby,go,php,csharp (default: all)
Step 2 — Interpret findings
CRITICAL = direct user-input → query string construction. Fix the specific query AND audit nearby code for the same pattern.
HIGH = pattern suggests interpolation but might be a fixed identifier (table/column name). Verify by reading the code.
MEDIUM = framework-specific pattern that's safe ONLY with strict
input validation (Django .extra(), Rails string where()).
Step 3 — Remediation
For each finding, the fix is the same shape per language: use the
language/library's parameterized-query API. See
references/PLAYBOOK.md for per-language snippets.
Step 4 — Cross-skill chaining
Consider running scanning-for-hardcoded-secrets (#10) on the same
target — same audit, different class of finding.
Examples
Example 1 — Pre-merge code review
python3 ${CLAUDE_PLUGIN_ROOT}/skills/detecting-sql-injection-patterns/scripts/scan_sqli.py \
--min-severity high $(git diff --name-only main...HEAD | tr '\n' ' ')Scans only files changed in the current branch — fast feedback for PR review.
Example 2 — Legacy codebase audit
python3 ${CLAUDE_PLUGIN_ROOT}/skills/detecting-sql-injection-patterns/scripts/scan_sqli.py \
/path/to/legacy-app --format markdown > sqli-audit.mdExpect dozens to hundreds of findings on a pre-ORM Java/PHP codebase. Prioritize by reachability: the queries reached from public endpoints first.
Output
JSON / JSONL / Markdown. Exit codes: 0 clean, 1 high/critical, 2 error.
Error Handling
- False positives on fixed-identifier interpolation (e.g.,
f"SELECT * FROM {tablename}"wheretablenameis hardcoded) → verify manually. The scanner can't reason about variable provenance without a full AST + control-flow pass. - String-built dynamic-table queries are sometimes legitimate (multi-tenant routing). Flag and review; the fix is usually allow-list validation + identifier quoting.
Resources
references/THEORY.md— Per-language interpolation patterns, ORM-specific safe vs unsafe APIs, why prepared statements workreferences/PLAYBOOK.md— Per-language parameterization snippets (Python sqlite3 + psycopg + SQLAlchemy, Node mysql2 + pg + knex- sequelize, Ruby ActiveRecord, Go database/sql, Java JDBC PreparedStatement, PHP PDO)