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https://github.com/SHOGGOTH-SECTOR/sica-fondt.git
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Place useful parts of the surrounding repos into sica-fondt by layer, per the
body model (Ada = membrane; brain/endocrine/capabilities/knowledge non-Ada):
- brain/ LLM reasoning + providers (dapr, hermes, MoMoA)
- capabilities/ REPRAG sidecars: hermes tools/skills, dapr tools, parallel
dispatch, A51 channels, and the OSINT cluster
- knowledge/ LORAG corpus: 754 cyber-skills, agency personas, secure-coding,
MITRE ATT&CK data
- reference/ defensive threat-reference (C3, shhbruh doc) + AdaYaml parser
License handling: AGPL sources (worldosint, advanced_evolution, mercury,
Reticulum) and GPL DeTTECT are SPEC-only clean-room/port descriptions — no
copyleft code copied. MIT/Apache/data parts copied as working trees.
Safety: shhbruh escape/persistence material and C3 covert-C2 kept as reference
only, not wired into the running organism. See CONSOLIDATION.md.
https://claude.ai/code/session_01UehUqEXXJJCsHoA4voCU5c
61 lines
1.9 KiB
Markdown
61 lines
1.9 KiB
Markdown
# API Reference: SQL Injection Detection Agent
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## Dependencies
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| Library | Version | Purpose |
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|---------|---------|---------|
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| requests | >=2.28 | HTTP client for injection payload delivery |
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## CLI Usage
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```bash
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python scripts/agent.py \
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--url "https://target.example.com/products" \
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--param id --method GET \
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--output sqli_report.json
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```
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## Functions
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### `detect_error_based(url, param, method, headers) -> dict`
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Injects `'` and matches response against SQL error patterns for MySQL, PostgreSQL, MSSQL, Oracle, SQLite.
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### `detect_boolean_based(url, param, method, headers) -> dict`
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Compares response lengths for `AND 1=1` (true) vs `AND 1=2` (false) against a baseline.
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### `detect_time_based(url, param, method, headers, delay) -> dict`
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Tests `SLEEP()`, `pg_sleep()`, and `WAITFOR DELAY` payloads. Measures response time against target delay.
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### `detect_union_columns(url, param, method, headers, max_cols) -> dict`
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Increments `ORDER BY N` until error to determine column count for UNION injection.
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### `fingerprint_database(url, param, method, headers) -> dict`
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Tries `@@version` and `version()` via UNION SELECT to identify the database engine.
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### `run_assessment(url, param, method) -> dict`
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Runs all detection techniques and compiles findings.
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## SQL Error Signatures
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| Database | Pattern |
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|----------|---------|
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| MySQL | `SQL syntax.*MySQL`, `Warning.*mysql_` |
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| PostgreSQL | `ERROR:\s+syntax error`, `PSQLException` |
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| MSSQL | `SQL Server.*Driver`, `SQLServerException` |
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| Oracle | `ORA-\d{5}` |
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| SQLite | `SQLite\.Exception` |
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## Output Schema
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```json
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{
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"target": "https://target.example.com/products",
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"parameter": "id",
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"injectable": true,
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"error_based": {"injectable": true, "database": "mysql"},
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"boolean_based": {"injectable": true},
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"time_based": {"injectable": false},
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"findings": ["CRITICAL: Error-based SQLi confirmed (DB: mysql)"]
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}
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```
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