mirror of
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
24 lines
1.1 KiB
Markdown
24 lines
1.1 KiB
Markdown
# Workflows — NoSQL Injection Exploitation
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## Detection Workflow
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1. Identify application technology stack (check for MongoDB, CouchDB indicators)
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2. Map all input points accepting JSON data or query parameters
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3. Submit operator payloads ($ne, $gt, $regex) in each parameter
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4. Monitor responses for authentication bypass or data leakage
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5. Test for JavaScript injection via $where operator
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6. Document all vulnerable endpoints with proof-of-concept payloads
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## Blind Extraction Workflow
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1. Confirm boolean-based injection by comparing true/false responses
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2. Determine password/field length using $regex with length patterns
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3. Extract characters one at a time using $regex "^<known_chars><test>"
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4. Automate extraction with Python script using binary search
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5. Validate extracted data by attempting authentication
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## Automated Scanning Workflow
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1. Configure proxy (Burp Suite) to intercept target traffic
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2. Run NoSQLMap against identified endpoints
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3. Use nuclei with NoSQL injection templates for broad coverage
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4. Manually verify automated findings with crafted payloads
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5. Escalate confirmed findings to data extraction or RCE attempts
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