mirror of
https://github.com/SHOGGOTH-SECTOR/sica-fondt.git
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24f816b6a3
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
2.0 KiB
2.0 KiB
API Reference: Implementing API Abuse Detection with Rate Limiting
Redis Token Bucket (Python)
import redis, time
r = redis.Redis()
# Lua-based atomic token bucket
lua = """
local tokens = tonumber(redis.call('HGET', KEYS[1], 'tokens') or ARGV[1])
local last = tonumber(redis.call('HGET', KEYS[1], 'last') or ARGV[3])
local elapsed = ARGV[3] - last
tokens = math.min(tonumber(ARGV[1]), tokens + elapsed * tonumber(ARGV[2]))
if tokens >= 1 then
tokens = tokens - 1
redis.call('HMSET', KEYS[1], 'tokens', tokens, 'last', ARGV[3])
return 1
end
return 0
"""
allowed = r.eval(lua, 1, f"rl:{client_ip}", max_tokens, refill_rate, time.time())
Rate Limit Response Headers
| Header | Description |
|---|---|
X-RateLimit-Limit |
Maximum requests allowed |
X-RateLimit-Remaining |
Requests remaining |
X-RateLimit-Reset |
Unix timestamp when limit resets |
Retry-After |
Seconds until client can retry |
NGINX Rate Limiting
limit_req_zone $binary_remote_addr zone=api:10m rate=10r/s;
location /api/ {
limit_req zone=api burst=20 nodelay;
limit_req_status 429;
}
Abuse Detection Thresholds
| Attack Type | Indicator | Threshold |
|---|---|---|
| Brute Force | Auth failures/IP | > 10 in 5 min |
| Credential Stuffing | Unique users/IP | > 20 |
| API Scraping | Requests/IP | > 500/hr |
| Rate Bypass | User-Agent rotation | > 10 unique UAs |
Flask-Limiter
from flask import Flask
from flask_limiter import Limiter
from flask_limiter.util import get_remote_address
app = Flask(__name__)
limiter = Limiter(get_remote_address, app=app, default_limits=["100/minute"])
@app.route("/api/login")
@limiter.limit("5/minute")
def login():
pass
References
- Redis Rate Limiting: https://redis.io/glossary/rate-limiting/
- Flask-Limiter: https://flask-limiter.readthedocs.io/
- IETF RateLimit Headers: https://datatracker.ietf.org/doc/draft-ietf-httpapi-ratelimit-headers/