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Operator: *"proxy hops defaults to 1 and should be in the settings UI not in the envs, we need the security values to be in the UI."* Overrules the call I made yesterday, and rule 25 is on your side — I argued deployment-topology, but the operator has to be able to SEE what protects them, and reading a container's environment is not seeing. Six new settings in a **Security** group: trusted proxy hops (default 1), the per-account and per-address sign-in limits with their shared window, and the sign-up limit with its own. `THOUGHTSYNC_TRUSTED_PROXY_HOPS` is gone; the rate limits are no longer hardcoded constants. **The hard part was keeping the throttle cheap.** It consults these BEFORE opening a database connection — deliberately, because a refused attempt is meant to cost nothing, and the hop count is needed to know who is even asking. A query per attempt would undo both. So there is a small cache seeded from the registry defaults (the app works with no database at all, which is what the DB-free unit lane relies on), loaded at boot, and refreshed on every settings save — the same live-update contract `session_ttl_days` already had. `SlidingWindow` now takes its limit and window as SUPPLIERS rather than values, so a saved number applies to the next attempt instead of the next deploy. **Bounds are rejected, not clamped.** A hop count of 99 would trust anything a caller sent; a sign-in limit of 0 would lock every account out permanently. Both now fail validation with a message naming the range, and the number input carries min/max so the browser objects first. Silently storing a different number than the one typed is how somebody ends up believing a protection is set to something it is not. `MAX_BUCKETS` stays a constant on purpose: it protects the limiter from itself rather than the app from a caller, and there is no operator judgment to apply. Two integration tests, because the whole point is the round trip: a dangerous value refused, a legitimate one reaching the cache the throttle reads and persisting; and every Security row reaching the admin payload with bounds and a description that explains itself.
135 lines
5.5 KiB
Python
135 lines
5.5 KiB
Python
"""Throttling for the endpoints that a public deployment leaves exposed.
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Only the credential endpoints are rate-limited: login, register, and the native
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device-link exchange. Everything else already needs a session or a device token to
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reach, so an attacker has to get through one of these three first.
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## Why in-process is enough here, and where that stops being true
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State lives in module-level dicts, so it is per-process. That is correct for how
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this image actually serves — one hypercorn worker (see the Dockerfile, and the
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same assumption the trash sweeper documents in app.py). If that ever gains
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``--workers N``, each worker would keep its own counters and the effective limit
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would multiply by N; the fix then is a shared store (the Postgres connection is
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already there), not a bigger number here.
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## Two keys, on purpose
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Every attempt is counted against BOTH the account being tried and the address it
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came from, and either one can refuse it:
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- **The account** is the key that matters, and the key that cannot be forged. It
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is what stops credential stuffing against one known email, no matter how many
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addresses the attempts arrive from.
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- **The address** bounds the damage from one source spraying many accounts. It is
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read from ``X-Forwarded-For``, counting in from the RIGHT by
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the **Trusted proxy hops** setting so that only entries our own proxies wrote are
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believed — a forged header lands to the left of those and is never selected. It is
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still the weaker of the two keys, because it depends on that setting matching the
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deployment; the account key depends on nothing.
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Counting is by failure for the sign-in routes and by attempt for registration: a
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correct password should never move someone closer to being locked out, but every
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registration is a row in the users table whether it succeeds or not.
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"""
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from __future__ import annotations
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import time
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from collections import deque
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from collections.abc import Callable
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from .settings import live
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# Never let the bookkeeping become the denial of service: an attacker rotating an
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# address could otherwise mint an unbounded number of buckets. Well above any real
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# deployment's distinct-caller count, so a legitimate instance never reaches it; when
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# it is reached the oldest buckets are dropped, which at worst forgives some attempts.
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#
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# Not a setting: it protects the limiter from itself rather than the app from a
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# caller, and there is no operator judgment to apply to it.
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MAX_BUCKETS = 10_000
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class SlidingWindow:
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"""Counts events per key over a trailing window.
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Sliding rather than a fixed window because a fixed one lets twice the limit
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through across a boundary — 10 at 14:59 and 10 at 15:00 — which for a login
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limiter is the difference between the number meaning something and not.
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The limit and window are SUPPLIERS, not values, so an admin saving a new number in
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Settings takes effect on the next attempt instead of the next deploy. They are read
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per call, which is a dict lookup — the settings cache never touches the database.
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"""
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def __init__(self, limit: Callable[[], int], window_s: Callable[[], float]) -> None:
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self._limit = limit
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self._window_s = window_s
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self._hits: dict[str, deque[float]] = {}
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@property
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def limit(self) -> int:
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return self._limit()
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@property
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def window_s(self) -> float:
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return self._window_s()
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def _prune(self, key: str, now: float) -> deque[float]:
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hits = self._hits.get(key)
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if hits is None:
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hits = deque()
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# Insertion-ordered, so the first key is the least recently created.
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if len(self._hits) >= MAX_BUCKETS:
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self._hits.pop(next(iter(self._hits)), None)
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self._hits[key] = hits
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cutoff = now - self.window_s
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while hits and hits[0] <= cutoff:
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hits.popleft()
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return hits
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def retry_after(self, key: str, now: float | None = None) -> int | None:
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"""Seconds until `key` may try again, or None while it is still under the
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limit. Read-only — it does not count as an attempt."""
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now = time.monotonic() if now is None else now
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hits = self._prune(key, now)
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if len(hits) < self.limit:
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return None
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# The window frees up when its OLDEST hit falls out of it.
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return max(1, int(hits[0] + self.window_s - now) + 1)
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def record(self, key: str, now: float | None = None) -> None:
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now = time.monotonic() if now is None else now
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self._prune(key, now).append(now)
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def forget(self, key: str) -> None:
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"""Drop a key's history. Used after a successful sign-in, so someone who
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fumbled a password twice and then got it right starts clean rather than
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carrying those two for the next quarter of an hour."""
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self._hits.pop(key, None)
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def clear(self) -> None:
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self._hits.clear()
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def _minutes(key: str) -> Callable[[], float]:
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return lambda: float(live(key)) * 60.0
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sign_in_by_account = SlidingWindow(
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lambda: live("signin_limit_per_account"), _minutes("signin_window_minutes")
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)
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sign_in_by_address = SlidingWindow(
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lambda: live("signin_limit_per_address"), _minutes("signin_window_minutes")
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)
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register_by_address = SlidingWindow(
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lambda: live("register_limit_per_address"), _minutes("register_window_minutes")
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)
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def reset_all() -> None:
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"""Drop every counter. For tests — nothing in the app calls this."""
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for window in (sign_in_by_account, sign_in_by_address, register_by_address):
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window.clear()
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