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Author SHA1 Message Date
claude-botandClaude Opus 5 05cd7f3674 feat(crowdsec): arm the bouncer on proxy-2
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First host goes from dry-run to enforcing. proxy-2 deliberately: it is the
dual-stack member, while proxy-1 is IPv6-only and the one where every
network-shaped problem has surfaced first — the dhcpcd/resolv.conf breakage,
and a GitHub download that could not work at all. host_vars keeps the change
to one host; proxy-1 stays on the role default.

Verified before committing: ansible-inventory resolves nftables for proxy-2
and nothing for proxy-1.

Adds netbird.renner.uno to capi_whitelists first, and that is not decoration.
The bouncer's chain sits on the INPUT hook, the proxy hosts run no other
firewall, and there is therefore no conntrack rule accepting established flows
ahead of it. A ban on the management address would drop the return traffic of
the proxies' own outbound control connection and take them off the mesh, with
no inbound attack involved at all. The same reasoning already covers the proxy
addresses, which were listed from the start.

Checked at the time of writing: none of these addresses appear in the current
decision set — searched all ~15k entries. That is the state today, not a
guarantee; they are rented addresses on a hoster whose ranges do end up on
community blocklists, which is the whole reason for listing them.

The delivery path was tested end to end beforehand with a decision on
192.0.2.1 (TEST-NET-1, so no real traffic could be affected). Both bouncers
received the ban within one poll cycle — a 206-byte delta against the usual
26 — and the revocation the same way, 203 bytes, ~5s after deletion. What that
did not test is whether a packet is actually dropped, which dry-run cannot
show. This commit is what makes that testable.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
2026-09-02 16:13:11 +02:00
k.renner d655d65ad9 Merge pull request 'feat(llama-server): 32k context per slot for the Assist agent' (#765) from llama-ctx-assist into master
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Reviewed-on: k.renner/homelab#765
2026-09-02 14:09:07 +00:00
claude-botandClaude Opus 5 60a3da9447 feat(llama-server): 32k context per slot for the Assist agent
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The Home Assistant conversation agent is configured and refuses every
request:

    API error: request (18910 tokens) exceeds the available
    context size (16384 tokens), try increasing it

18910 breaks down as ~12k for the exposed-entity overview and ~7k for
the base instructions and tool schemas, whose parameters enumerate every
exposed name, area and floor. Trimming the exposed set from 845 entities
to 666 already took the overview from 16045 tokens to 12048; the
remainder is not the sort of thing curation reaches.

--ctx-size is per slot rather than the total, which is what makes this
affordable: /props reports n_ctx_slot equal to the flag while running
four slots, where a total would have divided down to 4096. Doubling the
flag alone would double the KV cache, so the slot count halves in step
and the allocation lands where it already is -- 24979 MiB against the
32Gi limit, most of it the 19.5 GiB of weights.

Two slots matches the actual callers: one holds Home Assistant's prompt,
one holds Paperless's, and prompt caching keeps each warm. A cold prefill
of this prompt costs about two minutes, so a third caller queueing is a
better outcome than it evicting one of them.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
2026-09-02 16:07:06 +02:00
k.renner 47086a5fe9 Merge pull request 'feat(monitoring): scrape CrowdSec and give it a dashboard' (#764) from crowdsec-metrics-dashboard into master
ci/woodpecker/cron/alert-monitor Pipeline was successful
Reviewed-on: k.renner/homelab#764
2026-09-02 13:59:21 +00:00
k.renner 412acf7cb4 Merge pull request 'feat(network-policies): let Home Assistant reach the chat model' (#759) from ha-llm-agent into master
Reviewed-on: k.renner/homelab#759
2026-09-02 13:59:00 +00:00
k.renner 75ecf020cc Merge pull request 'fix(llama-embed): size the batch for bge-m3 tokenizer, not Paperless' (#762) from embed-batch-fix into master
Reviewed-on: k.renner/homelab#762
2026-09-02 13:58:42 +00:00
claude-botandClaude Opus 5 6a65a5982a feat(monitoring): scrape CrowdSec and give it a dashboard
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A dashboard first needed something to draw. Nothing scraped CrowdSec:
`up{namespace="crowdsec"}` returned zero series and Prometheus held no cs_*
metric at all, so the detection half of the ingress path was invisible —
including whether the agents were reading logs and whether any bouncer was
pulling decisions.

Both chart services already expose a `metrics` port on 6060 and the
crowdsec-ns CiliumNetworkPolicy already admits the monitoring namespace
without a port restriction. Only the ServiceMonitor was missing. One object
covers both services via matchExpressions, since they differ only in `app`.

Every panel query was written against label sets read off the live endpoints
rather than from the documentation:

  cs_active_decisions{action,origin,reason}
  cs_lapi_bouncer_requests_total{bouncer,method,route}
  cs_bucket_overflowed_total{name}
  cs_filesource_hits_total{acquis_type,datasource_type,source}
  cs_node_wl_hits_ok_total{...,reason,...}
  cs_parser_hits_ok_total / _ko_total

The bouncer panel is the one worth having: the LAPI counts requests per
bouncer, so a bouncer that stops pulling shows up as a line going to zero
rather than as nothing at all. It also makes the `<name>@<ip>` behaviour
visible — the LAPI registers a new entry per observed client address, and what
it observes is the netbird-ingress pod IP, so one logical bouncer appears
several times and gains new names as those pods roll.

The last panel is a text panel naming what the dashboard cannot show. The
bouncers' own metrics — fw_bouncer_active_decisions, dropped packets, their
view of the LAPI — sit on 127.0.0.1:60601 on public hosts with no metrics path
of any kind. So "Bouncer-Abholrate" proves a bouncer is asking, not that it
applied the answer. Better to say that on the dashboard than to let someone
read enforcement into a panel that does not measure it.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
2026-09-02 15:58:17 +02:00
k.renner 154d0328ce Merge pull request 'fix(crowdsec): move the LAPI backend annotations onto the Service' (#760) from crowdsec-lapi-service-annotations into master
Reviewed-on: k.renner/homelab#760
2026-09-02 13:48:51 +00:00
claude-botandClaude Opus 5 0a101eb8ee fix(llama-embed): size the batch for bge-m3's tokenizer, not Paperless's
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The LLM index rebuild ran for 79 minutes and then died:

    input (1069 tokens) is too large to process.
    increase the physical batch size (current batch size: 1024)

Setting the batch to Paperless's chunk size looked like an exact match
and was not. Paperless counts its 1024 tokens with llama_index's
tokenizer; this server measures the batch in bge-m3's, which is
XLM-RoBERTa and splits German into more pieces. A chunk Paperless
considered full arrived here 4.4% over the limit, and the request failed
rather than truncating.

1536 absorbs a tokenizer ratio rather than a rounding error. 2048 stays
out of reach: that is where the chat model hit ErrorDeviceLost on this
Vulkan stack, under a 20 GB model's memory pressure that a 600 MB one may
well not reproduce — but there is no reason to go looking.

The index is left partial by the failed run and has to be rebuilt with
`document_llmindex rebuild` once this is live.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
2026-09-02 15:13:54 +02:00
claude-botandClaude Opus 5 7903c81c9a feat(network-policies): let Home Assistant reach the chat model
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HA 2026.8.3 ships a native `llama_cpp` integration, so an Assist
conversation agent backed by the cluster's own model needs no custom
component — only a path from the HA pod to llama-server, which two
default-deny policies currently block in both directions. Verified from
inside the pod: the call to llama-server.ai.svc.cluster.local:8080 times
out today.

Both grants are scoped to the single pod and port, mirroring the
paperless grant next to them, so neither widens if something else is
placed in the other namespace. llama-embed is deliberately excluded: the
conversation agent uses chat completions only.

Tool calling was verified against the running model before adding this —
the Assist API requires it, and llama.cpp only exposes it for some
builds. b10731 returns finish_reason=tool_calls and parses German
commands into the right arguments without --jinja.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
2026-09-02 14:18:30 +02:00
10 changed files with 299 additions and 12 deletions
@@ -0,0 +1,19 @@
---
# Arms the CrowdSec firewall bouncer on THIS host only.
#
# The role defaults to dry-run for the whole netbird_proxy group, so the two
# members can be armed one at a time. proxy-2 goes first deliberately: it is
# the dual-stack member, while proxy-1 is IPv6-only and the one where every
# network-shaped problem has surfaced first — the dhcpcd/resolv.conf breakage,
# and the GitHub download that could not work at all.
#
# What flipping this does: the bouncer stops logging what it would block and
# creates two nftables tables (ip crowdsec, ip6 crowdsec6) with the decisions
# it holds. It touches no existing chain and sets no chain policy.
#
# Rollback is `systemctl stop crowdsec-firewall-bouncer` — a clean stop deletes
# both tables itself. Setting this back to dry-run and re-running the play does
# the same through the handler.
#
# Remove this file once proxy-1 follows and the role default moves to nftables.
crowdsec_bouncer_mode: nftables
+20 -4
View File
@@ -75,11 +75,27 @@ spec:
# many small per-expert matmuls that sets the pace, not bandwidth.
- --threads
- "4"
# 16k rather than 8k because the measured prefill falloff is mild
# and this model's KV cache is unusually cheap — only 10 of its 40
# layers use full attention.
# 32k because Home Assistant's Assist agent measured 18910 tokens
# for a single request: ~12k of exposed-entity overview plus ~7k of
# base instructions and tool schemas, whose parameters enumerate
# every exposed name, area and floor. At 16384 the request was
# refused outright.
#
# --ctx-size is per slot, not the total: /props reports
# n_ctx_slot equal to this value while running four slots, where a
# total would have divided down to 4096. Doubling it alone would
# therefore double the KV cache, so the slot count halves in step
# and the allocation stays where it is today — 24979 MiB of the
# 32Gi limit, most of it the 19.5 GiB of weights.
#
# Two slots is what is actually in use: one holds Home Assistant's
# prompt, one holds Paperless's. A third caller would queue rather
# than evict either, which is the better failure mode given a cold
# prefill of this prompt costs around two minutes.
- --ctx-size
- "16384"
- "32768"
- --parallel
- "2"
# A micro-batch of 2048 or more makes this Vulkan/Mesa stack fail
# with ErrorDeviceLost.
- --ubatch-size
+19 -8
View File
@@ -64,16 +64,27 @@ spec:
- "99"
- --threads
- "2"
# Paperless chunks documents at 1024 tokens. The defaults are
# batch 2048 / ubatch 512, and with --embeddings llama-server
# clamps both down to 512 rather than run them mismatched — below
# the chunk size. Matching both to 1024 fits a whole chunk in one
# pass; 2048 is deliberately avoided, since that is where this
# Vulkan stack fails with ErrorDeviceLost.
# Paperless chunks at 1024 tokens, but counts them with its own
# tokenizer while this server measures the batch in bge-m3's.
# Those disagree: XLM-RoBERTa splits German into more pieces than
# llama_index's counter does, so a chunk Paperless considers full
# at 1024 arrived here as 1069 and the request failed outright —
# "input (1069 tokens) is too large to process".
#
# The headroom therefore has to absorb a tokenizer ratio, not just
# a rounding error, and 1.5x covers far more than the 1.044 that
# broke it. The defaults are batch 2048 / ubatch 512, and with
# --embeddings llama-server clamps both to the smaller rather than
# run them mismatched, so both are set.
#
# 2048 stays avoided: that is where the chat model met
# ErrorDeviceLost on this Vulkan stack. That failure came under a
# 20 GB model's memory pressure and may not bind a 600 MB one, but
# this does not need to find out.
- --batch-size
- "1024"
- "1536"
- --ubatch-size
- "1024"
- "1536"
# Room for a full chunk plus overhead rather than exactly one.
- --ctx-size
- "4096"
@@ -0,0 +1,36 @@
# Scrapes CrowdSec. Until this landed, nothing did: `up{namespace="crowdsec"}`
# returned zero series and Prometheus held no cs_* metric at all, so the
# detection side of the ingress path was invisible — including whether the
# agents were reading logs and whether any bouncer was pulling decisions.
#
# One monitor for both services. They carry the same shape (a `metrics` port on
# 6060) and differ only in `app`, so a matchExpressions selector keeps this to
# a single object rather than two near-identical ones:
#
# crowdsec-service the LAPI — decisions, bouncer + machine requests
# crowdsec-agent-service the agents — acquisition, parsers, scenarios
#
# The crowdsec-ns CiliumNetworkPolicy already admits the monitoring namespace
# without a port restriction, so no policy change is needed.
#
# `release: kube-prometheus-stack` is what the chart's Prometheus selects on.
apiVersion: monitoring.coreos.com/v1
kind: ServiceMonitor
metadata:
name: crowdsec
namespace: crowdsec
labels:
app.kubernetes.io/name: crowdsec
release: kube-prometheus-stack
spec:
selector:
matchExpressions:
- key: app
operator: In
values:
- crowdsec-service
- crowdsec-agent-service
endpoints:
- port: metrics
path: /metrics
interval: 30s
@@ -23,6 +23,7 @@ resources:
- prometheusrule-cnpg-backup.yaml
- alloy-servicemonitor.yaml
- cnpg-podmonitors.yaml
- crowdsec-servicemonitor.yaml
- home-assistant-servicemonitor.yaml
- immich-servicemonitor.yaml
- loki-servicemonitor.yaml
@@ -0,0 +1,163 @@
{
"annotations": {"list": []},
"description": "CrowdSec: Erkennung im Cluster, Durchsetzung am NetBird-Edge",
"editable": true,
"fiscalYearStartMonth": 0,
"graphTooltip": 1,
"id": null,
"links": [],
"panels": [
{
"datasource": {"type": "prometheus", "uid": "prometheus"},
"description": "Aktive Ban-Entscheidungen in der LAPI, ganz überwiegend aus den CAPI-Community-Blocklisten. Das ist die Menge, die ein scharfgeschalteter Bouncer durchsetzt.",
"fieldConfig": {
"defaults": {"color": {"mode": "thresholds"}, "mappings": [], "thresholds": {"mode": "absolute", "steps": [{"color": "text", "value": null}]}, "unit": "short"},
"overrides": []
},
"gridPos": {"h": 4, "w": 6, "x": 0, "y": 0},
"id": 1,
"options": {"colorMode": "value", "graphMode": "area", "justifyMode": "auto", "orientation": "auto", "reduceOptions": {"calcs": ["lastNotNull"], "fields": "", "values": false}, "textMode": "auto"},
"targets": [{"datasource": {"type": "prometheus", "uid": "prometheus"}, "expr": "sum(cs_active_decisions)", "refId": "A"}],
"title": "Aktive Decisions",
"type": "stat"
},
{
"datasource": {"type": "prometheus", "uid": "prometheus"},
"description": "Bouncer, die in den letzten 10 Minuten Decisions abgeholt haben. Fällt das unter die erwartete Zahl, holt ein Bouncer nichts mehr — und setzt damit einen veralteten Stand durch, ohne dass irgendetwas ausfällt.",
"fieldConfig": {
"defaults": {"color": {"mode": "thresholds"}, "mappings": [], "thresholds": {"mode": "absolute", "steps": [{"color": "red", "value": null}, {"color": "green", "value": 1}]}, "unit": "short"},
"overrides": []
},
"gridPos": {"h": 4, "w": 6, "x": 6, "y": 0},
"id": 2,
"options": {"colorMode": "value", "graphMode": "none", "justifyMode": "auto", "orientation": "auto", "reduceOptions": {"calcs": ["lastNotNull"], "fields": "", "values": false}, "textMode": "auto"},
"targets": [{"datasource": {"type": "prometheus", "uid": "prometheus"}, "expr": "count(sum by (bouncer) (rate(cs_lapi_bouncer_requests_total[10m])) > 0)", "refId": "A"}],
"title": "Bouncer, die abholen",
"type": "stat"
},
{
"datasource": {"type": "prometheus", "uid": "prometheus"},
"description": "Vom Agent gelesene Logzeilen pro Sekunde. Steht das auf 0, detektiert CrowdSec nichts mehr — der häufigste stille Ausfall, weil alles andere weiterläuft.",
"fieldConfig": {
"defaults": {"color": {"mode": "thresholds"}, "mappings": [], "thresholds": {"mode": "absolute", "steps": [{"color": "red", "value": null}, {"color": "green", "value": 0.01}]}, "unit": "reqps"},
"overrides": []
},
"gridPos": {"h": 4, "w": 6, "x": 12, "y": 0},
"id": 3,
"options": {"colorMode": "value", "graphMode": "area", "justifyMode": "auto", "orientation": "auto", "reduceOptions": {"calcs": ["lastNotNull"], "fields": "", "values": false}, "textMode": "auto"},
"targets": [{"datasource": {"type": "prometheus", "uid": "prometheus"}, "expr": "sum(rate(cs_filesource_hits_total[5m]))", "refId": "A"}],
"title": "Gelesene Logzeilen/s",
"type": "stat"
},
{
"datasource": {"type": "prometheus", "uid": "prometheus"},
"description": "Szenario-Overflows der letzten 24 h — also lokal erkannte Angriffe, im Gegensatz zu den von CAPI übernommenen.",
"fieldConfig": {
"defaults": {"color": {"mode": "thresholds"}, "mappings": [], "thresholds": {"mode": "absolute", "steps": [{"color": "text", "value": null}]}, "unit": "short"},
"overrides": []
},
"gridPos": {"h": 4, "w": 6, "x": 18, "y": 0},
"id": 4,
"options": {"colorMode": "value", "graphMode": "area", "justifyMode": "auto", "orientation": "auto", "reduceOptions": {"calcs": ["lastNotNull"], "fields": "", "values": false}, "textMode": "auto"},
"targets": [{"datasource": {"type": "prometheus", "uid": "prometheus"}, "expr": "sum(increase(cs_bucket_overflowed_total[24h]))", "refId": "A"}],
"title": "Lokale Overflows (24 h)",
"type": "stat"
},
{
"datasource": {"type": "prometheus", "uid": "prometheus"},
"description": "Abholrate je Bouncer. Der Firewall-Bouncer auf den Proxies fragt alle 10 s. Die LAPI legt bei jeder neuen beobachteten Client-Adresse einen Eintrag `<name>@<ip>` an, und beobachtet wird die Pod-IP des netbird-ingress-Peers — deshalb erscheint ein logischer Bouncer hier mehrfach und nach Pod-Rollen unter neuen Namen.",
"fieldConfig": {
"defaults": {"color": {"mode": "palette-classic"}, "custom": {"axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 10, "gradientMode": "none", "hideFrom": {"legend": false, "tooltip": false, "viz": false}, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": {"type": "linear"}, "showPoints": "never", "spanNulls": false, "stacking": {"group": "A", "mode": "none"}, "thresholdsStyle": {"mode": "off"}}, "mappings": [], "thresholds": {"mode": "absolute", "steps": [{"color": "green", "value": null}]}, "unit": "reqps"},
"overrides": []
},
"gridPos": {"h": 8, "w": 12, "x": 0, "y": 4},
"id": 5,
"options": {"legend": {"calcs": ["lastNotNull"], "displayMode": "table", "placement": "right", "showLegend": true}, "tooltip": {"mode": "multi", "sort": "desc"}},
"targets": [{"datasource": {"type": "prometheus", "uid": "prometheus"}, "expr": "sum by (bouncer) (rate(cs_lapi_bouncer_requests_total[5m]))", "legendFormat": "{{bouncer}}", "refId": "A"}],
"title": "Bouncer-Abholrate",
"type": "timeseries"
},
{
"datasource": {"type": "prometheus", "uid": "prometheus"},
"description": "Aktive Decisions nach Grund. `http:scan` dominiert und stammt aus den CAPI-Blocklisten.",
"fieldConfig": {
"defaults": {"color": {"mode": "palette-classic"}, "custom": {"axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 20, "gradientMode": "none", "hideFrom": {"legend": false, "tooltip": false, "viz": false}, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": {"type": "linear"}, "showPoints": "never", "spanNulls": false, "stacking": {"group": "A", "mode": "normal"}, "thresholdsStyle": {"mode": "off"}}, "mappings": [], "thresholds": {"mode": "absolute", "steps": [{"color": "green", "value": null}]}, "unit": "short"},
"overrides": []
},
"gridPos": {"h": 8, "w": 12, "x": 12, "y": 4},
"id": 6,
"options": {"legend": {"calcs": ["lastNotNull"], "displayMode": "table", "placement": "right", "showLegend": true}, "tooltip": {"mode": "multi", "sort": "desc"}},
"targets": [{"datasource": {"type": "prometheus", "uid": "prometheus"}, "expr": "sum by (reason) (cs_active_decisions)", "legendFormat": "{{reason}}", "refId": "A"}],
"title": "Aktive Decisions nach Grund",
"type": "timeseries"
},
{
"datasource": {"type": "prometheus", "uid": "prometheus"},
"description": "Welche Szenarien lokal auslösen. Das ist das, was CrowdSec selbst aus den Traefik-Logs erkennt.",
"fieldConfig": {
"defaults": {"color": {"mode": "palette-classic"}, "custom": {"axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "bars", "fillOpacity": 70, "gradientMode": "none", "hideFrom": {"legend": false, "tooltip": false, "viz": false}, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": {"type": "linear"}, "showPoints": "never", "spanNulls": false, "stacking": {"group": "A", "mode": "normal"}, "thresholdsStyle": {"mode": "off"}}, "mappings": [], "thresholds": {"mode": "absolute", "steps": [{"color": "green", "value": null}]}, "unit": "short"},
"overrides": []
},
"gridPos": {"h": 8, "w": 12, "x": 0, "y": 12},
"id": 7,
"options": {"legend": {"calcs": ["sum"], "displayMode": "table", "placement": "right", "showLegend": true}, "tooltip": {"mode": "multi", "sort": "desc"}},
"targets": [{"datasource": {"type": "prometheus", "uid": "prometheus"}, "expr": "sum by (name) (increase(cs_bucket_overflowed_total[1h]))", "legendFormat": "{{name}}", "refId": "A"}],
"title": "Szenario-Overflows (je Stunde)",
"type": "timeseries"
},
{
"datasource": {"type": "prometheus", "uid": "prometheus"},
"description": "Geparste gegen nicht geparste Zeilen. Ein Anstieg bei `ko` heißt meist, dass sich ein Logformat geändert hat und der Parser nicht mehr greift — CrowdSec liest dann weiter, erkennt aber nichts mehr.",
"fieldConfig": {
"defaults": {"color": {"mode": "palette-classic"}, "custom": {"axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 10, "gradientMode": "none", "hideFrom": {"legend": false, "tooltip": false, "viz": false}, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": {"type": "linear"}, "showPoints": "never", "spanNulls": false, "stacking": {"group": "A", "mode": "none"}, "thresholdsStyle": {"mode": "off"}}, "mappings": [], "thresholds": {"mode": "absolute", "steps": [{"color": "green", "value": null}]}, "unit": "reqps"},
"overrides": []
},
"gridPos": {"h": 8, "w": 12, "x": 12, "y": 12},
"id": 8,
"options": {"legend": {"calcs": ["lastNotNull"], "displayMode": "list", "placement": "bottom", "showLegend": true}, "tooltip": {"mode": "multi", "sort": "desc"}},
"targets": [
{"datasource": {"type": "prometheus", "uid": "prometheus"}, "expr": "sum(rate(cs_parser_hits_ok_total[5m]))", "legendFormat": "geparst", "refId": "A"},
{"datasource": {"type": "prometheus", "uid": "prometheus"}, "expr": "sum(rate(cs_parser_hits_ko_total[5m]))", "legendFormat": "nicht geparst", "refId": "B"}
],
"title": "Parser",
"type": "timeseries"
},
{
"datasource": {"type": "prometheus", "uid": "prometheus"},
"description": "Whitelist-Treffer nach Grund. Nützlich beim Scharfschalten: was hier zählt, wäre sonst ein Kandidat für einen Ban gewesen.",
"fieldConfig": {
"defaults": {"color": {"mode": "palette-classic"}, "custom": {"axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 10, "gradientMode": "none", "hideFrom": {"legend": false, "tooltip": false, "viz": false}, "lineInterpolation": "linear", "lineWidth": 1, "pointSize": 5, "scaleDistribution": {"type": "linear"}, "showPoints": "never", "spanNulls": false, "stacking": {"group": "A", "mode": "none"}, "thresholdsStyle": {"mode": "off"}}, "mappings": [], "thresholds": {"mode": "absolute", "steps": [{"color": "green", "value": null}]}, "unit": "reqps"},
"overrides": []
},
"gridPos": {"h": 8, "w": 12, "x": 0, "y": 20},
"id": 9,
"options": {"legend": {"calcs": ["lastNotNull"], "displayMode": "table", "placement": "right", "showLegend": true}, "tooltip": {"mode": "multi", "sort": "desc"}},
"targets": [{"datasource": {"type": "prometheus", "uid": "prometheus"}, "expr": "sum by (reason) (rate(cs_node_wl_hits_ok_total[5m]))", "legendFormat": "{{reason}}", "refId": "A"}],
"title": "Whitelist-Treffer",
"type": "timeseries"
},
{
"datasource": {"type": "prometheus", "uid": "prometheus"},
"gridPos": {"h": 8, "w": 12, "x": 12, "y": 20},
"id": 10,
"options": {
"code": {"language": "plaintext", "showLineNumbers": false, "showMiniMap": false},
"content": "## Was dieses Dashboard NICHT zeigt\n\nAlle Panels stammen aus der LAPI und den Agents im Cluster. Die **Bouncer auf proxy-1 und proxy-2 exportieren eigene Metriken**, die hier fehlen:\n\n- `fw_bouncer_active_decisions` — was tatsächlich in den nftables-Sets steht\n- `fw_bouncer_dropped_packets` / `_bytes` — was wirklich verworfen wurde\n- `lapi_requests_total` / `_failures_total` — aus Sicht des Bouncers\n\nSie liegen auf `127.0.0.1:60601` der Proxy-Hosts. Die Hosts sind öffentlich und haben bisher keinerlei Metrik-Pfad; den Port ins Internet zu öffnen, nur um ein Scrape-Ziel zu bekommen, wäre der falsche Tausch.\n\n**Konsequenz:** „Bouncer-Abholrate\" oben zeigt, dass ein Bouncer *fragt* — nicht, dass er die Antwort auch angewandt hat. Solange der Modus `dry-run` ist, wendet er ohnehin nichts an.\n\nEin Scrape-Pfad für die Hosts ist eigene Arbeit und Voraussetzung dafür, dass „was wird wirklich geblockt\" beantwortbar wird."
},
"title": "Blinder Fleck",
"type": "text"
}
],
"refresh": "30s",
"schemaVersion": 38,
"style": "dark",
"tags": ["homelab", "crowdsec", "security"],
"templating": {"list": []},
"time": {"from": "now-6h", "to": "now"},
"timepicker": {},
"timezone": "",
"title": "CrowdSec Overview",
"uid": "crowdsec-overview",
"version": 1,
"weekStart": ""
}
@@ -38,6 +38,9 @@ configMapGenerator:
- name: dashboard-blackbox-overview
files:
- blackbox-overview.json
- name: dashboard-crowdsec-overview
files:
- crowdsec-overview.json
- name: dashboard-disk-health
files:
- disk-health.json
@@ -65,6 +65,21 @@ spec:
- 45.157.177.26/32
- 2a03:4000:4b:c1e:4474:43ff:fedf:8f85/128
- 2a03:4000:28:f6e:a4c7:59ff:fe1e:9a29/128
# netbird.renner.uno — management, signal, relay and TURN.
#
# Not optional once a bouncer is armed. Its chain sits on the INPUT
# hook, the proxy hosts run no other firewall, and there is therefore
# no conntrack rule accepting established flows ahead of it. A ban on
# this address would drop the RETURN traffic of the proxies' own
# outbound control connection and take them off the mesh — with no
# inbound attack involved at all.
#
# Checked when this was added: none of these addresses appear in the
# current decision set. That is the state today, not a guarantee —
# these are rented addresses on a hoster whose ranges do end up on
# community blocklists.
- 162.55.50.97/32
- 2a01:4f8:c2c:4579::1/128
# mTLS between agents and the LAPI. Do not switch back to password auth:
# the LAPI runs without persistence, so its machine-registration DB is
@@ -34,6 +34,17 @@ spec:
- ports:
- port: "8080"
protocol: TCP
# Home Assistant's llama.cpp conversation agent calls the chat model only —
# it has no use for the embedding endpoint, so unlike paperless above this
# grant does not extend to llama-embed.
- fromEndpoints:
- matchLabels:
"k8s:io.kubernetes.pod.namespace": home-assistant
app.kubernetes.io/name: home-assistant
toPorts:
- ports:
- port: "8080"
protocol: TCP
- fromEndpoints:
- matchLabels:
"k8s:io.kubernetes.pod.namespace": monitoring
@@ -167,6 +167,18 @@ spec:
- toEndpoints:
- matchLabels:
"k8s:io.kubernetes.pod.namespace": influxdb
# The cluster's own chat model, for the llama.cpp conversation agent that
# backs Assist. Scoped to the one pod and port rather than the ai namespace
# as a whole, so this grant cannot widen if something else is placed there —
# the embedding endpoint next to it is deliberately not included.
- toEndpoints:
- matchLabels:
"k8s:io.kubernetes.pod.namespace": ai
app.kubernetes.io/name: llama-server
toPorts:
- ports:
- port: "8080"
protocol: TCP
# traefik for east-west OIDC roundtrips that go through the
# public hostname via k8s_gateway.
- toEndpoints: