The DELETE /api/skills/{skill_id} handler resolves the caller, loads the
skill with skills_manager.load(owner=user), and verifies ownership with
_verify_owner(match, user) — but then calls
skills_manager.delete_skill(match.get("name")) without the owner.
SkillsManager.delete_skill filters candidates with
`(sk.owner or "") != (owner or "")`, so when owner is None an owner-scoped
skill is skipped and the method returns False. The route then raises a
spurious 404 "Skill not found" — meaning a logged-in user can never delete
their own skills through the API.
Pass the resolved owner through to delete_skill so the skill is matched and
removed.
tests/test_skills_delete_owner.py drops a real owner-scoped SKILL.md on disk
and (1) checks the manager directly: delete_skill without owner returns
False (regression lock) while delete_skill(owner="alice") returns True and
removes the dir; (2) drives the real DELETE route handler and asserts it
returns {"ok": True} and deletes the file. The route test fails before this
change (404). Real SkillsManager + real filesystem, no mocking.
_ping_endpoint() is the reachability fallback the model-endpoint POST
handler invokes when _probe_endpoint() returns no model ids. It GETs
base + "/models" and, on any sub-500 response, returns immediately with
`reachable = (status < 400)`. That early return runs before the
Ollama-native /api/version / /api/tags fallback below it.
For an Ollama URL without /v1 (the quickstart accepts both
http://localhost:11434 and http://127.0.0.1:11434, and the reporter
on #1025 explicitly tried both), the OpenAI-style probe target is
http://127.0.0.1:11434/models. Ollama returns 404 there because /models
only lives under /v1. _ping_endpoint then returned reachable=False and
the picker showed "Added (offline — will retry on next load)" on an
install that was running fine. /api/version was never tried.
Same shape for http://127.0.0.1:11434/api (the native Ollama root):
/api/models is also 404, same premature offline verdict.
_probe_endpoint() does fall through to /api/tags on a 4xx (the response
raises via raise_for_status), so the endpoint quietly recovers once
cached_models becomes non-empty on the next background refresh —
matching the second commenter's "had to disconnect manually then
reconnect for it to be detected" note. The bug is most visible while
no models are pulled yet (cached_models stays empty, _ping_endpoint
keeps voting offline).
Fix:
- Hoist the Ollama-shaped-URL test (port == 11434 or "ollama" in
hostname — the same condition _probe_endpoint already uses) to the
top of the function so both code paths share it.
- Stop short-circuiting on 4xx when the URL looks like Ollama: fall
through to the existing /api/version + /api/tags reachability loop
so an alive Ollama gets recognised even when its OpenAI surface has
the wrong prefix for the user's input.
- Fix the `root` computation in that loop to strip a trailing /api as
well as /v1, so http://127.0.0.1:11434/api no longer gets probed at
/api/api/version.
- 4xx on non-Ollama hosts keeps the current semantics: a 401 from
api.openai.com/v1/models is still a definitive offline verdict, not
a reason to GET /api/version on OpenAI.
Closes#1025.
* Fix Cookbook dependency install completion state
Mark Cookbook dependency installs as complete when the background runner
exits successfully, even when HuggingFace-specific download markers are
absent.
* Add focused regression coverage for cookbook dependency completion.
Keep the fix narrowly scoped while carrying env_path through dependency tasks and locking the completion reconciliation behavior with targeted tests.
* Cookbook: Engine filter + intelligent hardware-computed serve profiles
Two related Cookbook serving improvements for accurate, hardware-aware model
serving (especially on consumer GPUs that can only run GGUF/llama.cpp).
Engine filter
- New "Engine" dropdown (All / llama.cpp / vLLM / SGLang) beside the quant
picker. Pure client-side view filter over the fetched list via the same
_detectBackend() the serve commands use, so what you filter to is exactly what
would launch. Re-renders from cache (no refetch). Empty-state message + the
instant-cache-paint path account for it too.
Intelligent serve profiles (Quality / Balanced / Speed)
- services/hwfit/profiles.py: compute_serve_profiles() turns detected VRAM +
model size into concrete llama.cpp flags (n_gpu_layers, n_cpu_moe, cache-type,
context). Encodes the by-hand tuning: a too-big MoE offloads experts to CPU
instead of failing; a model that fits stays fully on GPU; quant tracks profile
intent; vision models keep image-encoder headroom. Reuses models.py VRAM math
so filtering and serving agree on what fits. Pure/deterministic (no t/s claims
— partial-offload speed isn't reliably predictable; fit is what's computed).
- /api/hwfit/profiles endpoint returns the profiles + the model's trained
context limit, with loose name matching (strips org/ prefix, -GGUF suffix,
quant tag) so a local GGUF folder name resolves to its catalog entry.
- _buildServeCmd (llama.cpp) now emits --n-cpu-moe / --flash-attn /
--cache-type-k/v when set, with llama-cpp-python fallback equivalents. It
previously only set -ngl/-c, which is why it OOM'd or ran slow.
- Serve panel: profile chips that fill the fields on click, plus CPU-MoE / KV
Cache / Flash Attn fields. Context is clamped to the model's trained limit
(and an absolute 1M sanity ceiling) on type/blur/profile-load and at launch —
fixes a crash where a stale 256k/16M preset + quantized KV cache caused an
amdgpu ErrorDeviceLost.
Tests: tests/test_serve_profiles.py (7) — offload vs full-GPU fit, never exceed
VRAM, context cap, launchable flags, vision headroom, no-GPU empty.
Checks: py_compile + node --check pass; pytest test_serve_profiles + test_hwfit_amd
green; verified live on an RDNA4 box (gfx1200) — Balanced lands ~ncm18 q4 128k,
matching hand-tuning.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* Cookbook: make column-header sorting discoverable (incl. Newest)
Sorting in Cookbook is via clickable column headers (pewds' design), but the
headers had no visual cue that they're interactive — so sorting in general, and
the Newest sort on the Model header specifically, was undiscoverable.
- Style sortable headers as interactive: pointer cursor, hover underline, and
the active sort column bolded/highlighted. There was no CSS for
.hwfit-sortable / .hwfit-sort-active at all; this helps every existing sort,
not just Newest.
- The Model column header sorts by release_date (newest first), reusing the
existing header-click sort wiring and the "newest" SORT_KEY.
No new sort control — uses the existing column-header paradigm.
Checks: node --check passes.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* Cookbook serve profiles: keep the on-disk file's quant fixed (don't propose Q6/Q2)
In the Serve tab the model is a specific GGUF file already on disk, so its quant
can't change — but the profiles were suggesting "Quality · Q6_K" / "Speed · Q2_K"
as if you could re-quantize it. That's meaningless when serving a fixed file.
- compute_serve_profiles gains serve_weights_gb / serve_quant. When set (SERVE
mode), the quant is locked to the file's and profiles differ only in the real
serving knobs — n_cpu_moe, KV-cache type, context. _weights_gb / _cpu_moe_for_budget
use the file's actual size instead of a quant-derived estimate. DOWNLOAD mode
(no override) still varies the quant to show download options.
- /api/hwfit/profiles accepts serve_weights_gb & serve_quant.
- The Serve panel parses the file's size (from m.size "20.6 GB") and quant (from
the repo/file name) and passes them, so profiles match what's actually served.
Result for a 20.6 GB Q4_K_M file: all three profiles stay Q4_K_M and differ by
KV/ctx/offload (Quality q8 KV 128k ncm21, Balanced q4 128k ncm17, Speed q4 32k
ncm15) — no nonsensical quant changes.
Tests: test_serve_mode_keeps_fixed_quant. Full serve-profile suite green (9).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* Cookbook serve: Vision toggle (auto-find mmproj) + live VRAM/RAM-spillover monitor
Two serve-panel additions:
1. **Vision toggle.** A "Vision" checkbox that serves the model with its
multimodal projector so it can read images. The mmproj path is resolved at
runtime (find mmproj-*.gguf next to the model), so dropping an mmproj file in
the model folder makes the toggle just work; `--mmproj … --image-max-tokens
1024` (native) / `--clip_model_path` (llama-cpp-python) only when on + found.
2. **Live GPU-memory monitor.** A readout that polls /api/cookbook/gpus every 4s
while the panel is open and shows VRAM used/total/%, free, and — crucially on
a discrete card — **RAM spillover** (AMD gtt_used_mb), with a plain-language
health hint: green/healthy, amber/tight, red/"spilled to RAM — slow (raise
CPU MoE or lower context)". Surfaces gtt_used_mb from the gpus endpoint
(previously read for total only and discarded for 'used').
Lets you see at a glance whether a config fits VRAM (fast) or is paging to system
RAM over PCIe (slow) instead of guessing.
Checks: node --check + py_compile pass.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Adds a diagnosis pattern for the 'Failed to infer device type' error
vLLM raises when no CUDA or ROCm GPU is found (e.g. systems with only
integrated or Intel Xe graphics). The existing pattern only caught
'No CUDA GPUs are available' which fires later in startup; this new
entry catches the earlier device-probe failure and the NVML/amdsmi
library-not-found messages that precede it.
Surfaces in the Cookbook serve card as: "vLLM could not find a supported
GPU — switch to llama.cpp or Ollama" instead of a raw Python traceback.
Co-authored-by: Claude Sonnet 4.6 (1M context) <noreply@anthropic.com>
Windows has 'App Execution Aliases' that can make shutil.which('python3')
and shutil.which('python') resolve to a Microsoft Store stub instead of
real Python -- even when Python is properly installed. The stub outputs:
'Python was not found; run without arguments to install from the
Microsoft Store, or disable this shortcut from Settings > Apps >
Advanced app settings > App execution aliases.'
and exits 9009, producing empty stdout. The JSON parse of the local
model cache scan then fails with 'Expecting value: line 1 column 1
(char 0)', and the Cookbook model list shows nothing.
Fix: prefer sys.executable as the interpreter for the local scan.
Odysseus already runs inside its own venv, so sys.executable always
points to the real venv Python and bypasses PATH / Store alias lookup
entirely. which_tool() is kept as a fallback.
Cross-platform: sys.executable works identically on Linux and macOS
(returns the real interpreter path), so this change is safe everywhere.
When the operator sets AUTH_ENABLED=false, three owner-scoped endpoints still
returned 401 (api/models, api/research/*, api/email/*), so the front-end
redirected the browser to /login and the app was unusable despite auth being
turned off. require_user() in src/auth_helpers.py already documents and honors
this contract (issue #622) via 'if _auth_disabled(): return ""', but these
endpoints did their own get_current_user/is_configured check without it.
Make _require_user (research), the /api/models anti-leak guard, and
email_helpers._require_auth consult _auth_disabled() and let anonymous through
(owner='') only when the operator explicitly disabled auth. The 401 protection
is fully intact when AUTH_ENABLED=true. Verified end-to-end: with
AUTH_ENABLED=false the SPA now loads instead of bouncing to /login.
The spinoff endpoint authenticated the caller (_require_user) but never
verified the research session belonged to them before reading the
persisted report and seeding it into a new chat session owned by the
caller. Any authenticated user who knew or guessed another user's
research session ID could exfiltrate that user's full report into their
own session — a cross-user data disclosure (IDOR).
Every other endpoint in this router gates on _owns_in_memory /
_assert_owns_research right after validating the session ID; spinoff was
the lone exception. Add the same _owns_in_memory check (covers both the
in-memory task and the on-disk JSON) so a non-owner gets a 404 before any
data is read or a session is created.
Add regression tests pinning the anonymous (401) and wrong-owner (404)
cases.
The /api/vault/unlock handler ran `bw` as
`_run_bw(["unlock", req.master_password, "--raw"])`. _run_bw launches it with
`asyncio.create_subprocess_exec(bw_path, *args)`, so the master password became
a process argument — readable by any local user through `ps` and
`/proc/<pid>/cmdline` for the lifetime of the unlock subprocess. The Bitwarden
master password decrypts the entire vault, so this is a serious credential
exposure on any multi-user / shared host (CWE-214).
The sibling /login handler already avoids this by feeding the password on
stdin; unlock was the outlier. Hand the password to `bw` through the
environment instead (`--passwordenv BW_PASSWORD`), mirroring how BW_SESSION is
already passed — `/proc/<pid>/environ` is readable only by the process owner,
not other local users. Add regression tests pinning that the secret reaches
the subprocess env and never appears in argv.
The dependency-install fallback chain unconditionally ran
'pip install --user', which fails inside a virtualenv (and as root in
LXC/containers) with 'Can not perform a --user install. User site-packages
are not visible in this virtualenv.' — even though the function's docstring
already noted --user is invalid in venvs.
Guard the --user fallback with a venv check so it only runs outside a venv
(where --user is actually valid for PEP-668 system Pythons). Derive the venv
probe interpreter from the install command (python for 'pip', python3 for
'pip3'/'python3 -m pip') so the check runs in pip's own environment. System
PEP-668 installs keep the --user fallback; venv/LXC-root installs no longer
hit the --user error. Updated the unit test for the new chain.
Closes#388
* macOS/Apple Silicon: detect Metal backend, surface MLX models, brew tmux hint
- hardware.py: add _detect_macos() via sysctl/system_profiler; report
backend=metal + unified_memory on Apple Silicon instead of cpu_arm
- fit.py: add Apple Silicon (M1-M5) unified-memory bandwidths + metal
FALLBACK_K so throughput estimates use the real bandwidth formula
- setup.py: Mac-specific 'brew install tmux' hint
Verified on M5 Pro 48GB: backend=metal, 273GB/s matched, 6 MLX models now
visible (were hidden), cuda still hides MLX, no new test failures.
* Fix native macOS tailnet launch and Metal GPU probe
---------
Co-authored-by: Elijah (Hermes) <hermes@local>
Split 2/4 of the companion bridge (#863 was 1/4). A paired bearer-token caller
runs as the sandboxed 'api' pseudo-user, so its sessions were stranded in a
separate 'api'-owned silo, invisible to the owner's desktop UI.
Add effective_user(): for a bearer token it resolves to the token's real owner
(request.state.api_token_owner); for cookie sessions it is identical to
get_current_user, so the swap is a no-op for browser users. Route session
ownership/attribution in routes/session_routes.py through it.
Tests (tests/test_session_owner_attribution.py):
- cookie/browser users are unchanged
- a bearer token attributes to its owner; with no owner it does NOT escalate
- _verify_session_owner: a bearer token for owner A cannot verify owner B's
session (404); owner verifies their own; missing -> 404; unauth -> 403
POST /api/v1/chat (the n8n/Make/Activepieces sync-chat endpoint) verified
session ownership with `_tok_user and _sess_owner and _sess_owner != _tok_user`.
The `_sess_owner and` clause skipped the check entirely whenever the session's
owner was null — so any chat-scoped API token (e.g. a token minted for a paired
mobile device) could pass a legacy/migrated null-owner session id, inject a
message into that session, and read back its conversation history plus reuse
the owner's endpoint credentials.
This is the same `if owner and owner != user` null-owner-bypass pattern that
was already hardened in the gallery, calendar, and notes routes (see
test_null_owner_gates.py) and in session_routes._verify_session_owner. Make
this gate strict and fail closed too: require a resolvable caller and an exact
owner match, mirroring _verify_session_owner. Extract the decision into
_caller_owns_session() and pin it with regression tests.
When the selected model fails before producing output, stream_llm_with_fallback
quietly switches to the next candidate and the reply is shown under the
originally selected model's name, so a misconfigured provider looks like it
works. (Concretely: a Bedrock gateway that 400s every Anthropic/Claude request
appears fine because another model silently answers under the Claude label.)
Emit a `fallback` SSE event ({selected_model, answered_by, reason}) the first
time a non-primary candidate produces output, forward it through the agent loop
and both chat-route paths, stamp the response metrics with the model that
actually answered, and show a notice + relabel the reply in the UI.
Tested: python -m pytest tests/test_llm_core_fallback.py (3 pass);
python -m py_compile src/llm_core.py src/agent_loop.py routes/chat_routes.py;
node --check static/js/chat.js.
When serving with the llama.cpp backend and no .gguf file exists on the host,
the GGUF launcher prelude exits with 'ERROR: No GGUF found on this host', but
_diagnose_serve_output had no matching pattern, so the UI showed a generic
crash instead of explaining the cause. Add a diagnosis pattern for the
no-GGUF case so users are told a .gguf is required and pointed at downloading
a GGUF build, instead of an opaque crash.
Closes#811
Two changes close the cross-tenant topic leak in /api/conversations/topics.
The route at routes/history_routes.py:478 used get_current_user, which
returns None when no auth middleware has set request.state.current_user
(loopback-bypass, AUTH_ENABLED=false, or any path that short-circuits the
middleware). It then forwarded owner=None to analyze_topics.
The helper at src/topic_analyzer.py:21 used an 'if owner:' short-circuit
in its owner filter, so the None owner took the no-filter path and the
helper silently aggregated topic frequencies and per-snippet session_id,
session_name, role, and snippet text across every user's sessions.
analyze_topics now returns an empty result when owner is falsy. The
inner short-circuit is removed because the filter is now strict by
construction. The route is switched to require_user, which raises 401
when auth_manager.is_configured is True and the caller is anonymous,
matching the pattern used by calendar_routes, skills_routes, and other
authenticated routes.
The test test_history_topics_owner_scope.py was rewritten to drive the
real route through FastAPI's TestClient with a stub AuthMiddleware that
mirrors the loopback-bypass branch, and now asserts a strict 401 from
the route and an empty result from the helper. The previous version of
the test accepted either a 200-with-empty-topics or a 401; the strict
assertion means a future regression that drops the require_user wrapper
or re-adds the inner short-circuit is caught immediately.
The "don't wipe endpoint_url/model on endpoint delete" half of #587 landed
in 6a78b02 (Fix endpoint model preservation for tasks). The three remaining
follow-up pieces from the original PR — flagged in the review on #786 —
are:
- routes/model_routes.py: toggle_model_endpoint (PATCH) now accepts
api_key and base_url, so the admin UI can rotate a key or fix a typo'd
URL without going through delete+recreate. base_url is normalized the
same way the POST handler does (strip /models, /chat/completions,
/completions, /v1/messages, then _normalize_base). Cache invalidation
matches the POST/DELETE paths and the response includes base_url so the
frontend can confirm what was saved.
- routes/chat_routes.py: new _recover_empty_session_model picks
cached_models[0] from the endpoint that matches sess.endpoint_url and
persists it onto the Session row before the LLM call goes out. Wired
into both /api/chat and /api/chat_stream after the existing
_clear_orphaned_session_endpoint guard, so the order is: drop
truly-orphaned sessions first, then heal the "picker showed it, session
never knew" case.
- routes/chat_routes.py: when recovery fails (no endpoint, no cached
models) raise HTTP 400 with a clear message instead of letting
model="" reach the upstream as 401/503.
Closes#587.
A Cookbook download whose repo/quant selector matched no files (e.g. a
':Q4_K_M' tag that does not exist) printed 'Fetching 0 files' and was still
reported as a successful '✓ Downloaded' / completed task. Detect the
zero-file signature in the download snapshot and mark the task as an error
with a clear diagnosis (no matching files — check the repo or quant/filename
pattern) so users know nothing was actually downloaded. Normal multi-file
and fully-cached downloads (which print 'Fetching N files', N>0) are
unaffected.
Closes#839
read_skill_md and read_skill_reference walk all skill files via
_iter_skill_files and return the first match by slug, regardless
of owner. In a multi-user deployment where two users have skills
with the same slug under different categories, a caller scoped
to owner='alice' can read Bob's skill content.
This is the same cross-tenant leak class as the update_skill /
delete_skill fix (PR #755, merged), but on the read path.
Changes:
- read_skill_md / read_skill_reference accept owner= param (default
None = match ownerless only, matching the write-path convention).
- 7 callers updated: tool_implementations.py (view, view_ref, patch),
builtin_actions.py (test_skills), skills_routes.py (audit, source,
test routes).
- Tests: read scoping (alice reads hers, not bob's), positive update
scoping (alice can mutate her own), ownerless-match default.
Streamed deltas flagged thinking:true (reasoning-model traces) were being folded
into full_response and persisted as part of the assistant message, so saved
replies were polluted with the model's chain-of-thought. Forward those deltas to
the client (for a live thinking indicator) but exclude them from the accumulated
saved reply, in both chat and research-stream paths. Mirrors the existing rewrite
path's handling.
* Dedupe URL routing helpers and tighten adjacent hostname checks
* Match providers by hostname, not substring, in _detect_provider
_detect_provider used `"anthropic.com" in url`-style substring checks, so a URL
that merely contained a provider's domain in its path or query — or a look-alike
host like `anthropic.com.example` — was misclassified and picked the wrong
auth-header/payload shape. Switch it to the existing `_host_match` helper
(hostname exact/subdomain match), the same way the human-readable labels and
curated model lists already work, finishing that migration. Also harden
`_host_match` against trailing-dot FQDNs.
Not a credential-leak fix: _detect_provider only classifies a URL the admin
already configured next to its key, and the URL — not this function — decides
where the request goes. This is a correctness/consistency cleanup.
Adds tests that import the real helpers (test_endpoint_resolver.py tests local
copies, so it can't catch this) covering the substring false-positives.
Refs #768.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* Import build_headers under its real name in model_routes
It was imported as `build_headers as _provider_headers`, which collides with
the unrelated llm_core._provider_headers(provider, headers) — same name,
different signature. Use the real name to remove the confusion.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* Use hostname matching in URL builders, not raw suffix checks
PR review flagged that _detect_provider() was hardened to match on
hostname, but several helpers still used raw host.endswith("anthropic.com")
/ host.endswith("ollama.com"), which match adjacent hosts like
notanthropic.com / notollama.com.
Route the remaining checks through _host_match(): _is_ollama_native_url
and _ollama_api_root in llm_core, and _anthropic_api_root / _ollama_api_root
in endpoint_resolver. With _detect_provider already hostname-correct, the
trailing "or host.endswith(...)" clauses in build_chat_url / build_models_url
are redundant, so drop them rather than fix the substring match in place.
Add builder-level tests asserting look-alike and domain-in-path hosts route
to the OpenAI-compatible default. They import the real builders and fail on
the pre-fix code.
Co-Authored-By: Claude <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>
Background tasks (e.g. the Email Tags / check_email_urgency action)
resolve their model through resolve_endpoint("utility") → Default Chat.
When the configured model is one the user has since disabled on the
endpoint, the resolver still dispatched to it — on Groq that surfaces as
every email failing with "HTTP 400: model ... requires terms acceptance".
Two paths fed this:
- The auto-pick fallback selected from cached_models without excluding
the endpoint's hidden_models, so a disabled model listed first won.
- A stale default_model left pointing at a now-disabled model (seeded at
endpoint registration from raw model_ids[0]) was used verbatim.
Fix resolve_endpoint / resolve_endpoint_by_id to drop a configured model
that's in hidden_models and to pick the first ENABLED chat model. Also
seed default_model on registration via _first_chat_model so we never pin
the global default to an embedding/tts entry a provider lists first.
Checks: python -m pytest tests/test_endpoint_resolver.py
tests/test_model_routes.py tests/test_model_context.py (all pass);
python -m py_compile app.py routes/model_routes.py
src/endpoint_resolver.py.
Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>
Every research endpoint interpolates session_id into filesystem paths
(Path('data/deep_research') / f'{session_id}.json') without checking
for traversal sequences. A crafted ID like '../../data/auth' reaches
arbitrary JSON files — readable via research_detail (which also leaks
file paths in error messages), writable via research_archive, and
deletable via research_delete.
Add _validate_session_id() which rejects anything outside
[a-zA-Z0-9-]{1,128}. Called before filesystem access in all 12
endpoints that accept a session_id path parameter.
The /api/chat/stream_status handler did a membership test against
_active_streams followed by an indexed read of the same key. Between
those two ops, a sibling stream's finally block (or a stop / cleanup
path) can pop the entry, turning the indexed read into a KeyError that
bubbles up as a 500. The race is the exact one _stream_set was already
written to avoid; the comment on the helper at the top of the module
spells out why a single .get() is the right pattern here too.
Collapse the two-step into a single .get() call so the lookup either
returns the live record or None, and report 'detached' / 404 based on
that single read. No behavior change on the happy path; the failure
mode under concurrent stream cleanup is now handled deterministically.
Closes#658.
After a successful password change, revoke all browser sessions for the
same user except the one that submitted the request. This prevents stale
sessions on other devices from remaining valid after credentials are
updated.
Keep API-token behavior unchanged. The current browser session is
preserved so the user can continue from the tab that changed the
password.
Add focused regression tests for preserving the current session, revoking
other sessions, persisting revocation, and avoiding revocation when the
current password is incorrect.
Require admin access before serving provider discovery data from
GET /api/providers. This prevents normal authenticated users from
triggering provider discovery or receiving cached provider host data.
Keep GET /api/models available to normal users and leave the existing
admin-only GET /api/discover behavior unchanged.
Add a focused regression test to ensure unauthorized callers cannot
trigger discovery and cannot receive cached provider data.
The email auto-calendar pass (settings.email_auto_calendar / the
extract_email_events task) scans recently received mail and lets an LLM
create / update / cancel calendar events. Two problems made it a cross-tenant,
remotely triggerable hole:
1. No owner scoping. _auto_summarize_pass(account_id=None) fans out over EVERY
enabled account of EVERY user. For each message it fetched an upcoming-events
snapshot with NO owner filter (all tenants' events) and handed those uids +
titles to the extraction LLM, then executed the model's ops via
do_manage_calendar(...) with owner=None. do_manage_calendar only filters by
owner when owner is not None, so create/update/delete ran across ALL users'
calendars. Net: every user's event titles/times were disclosed to the model,
and the model could cancel/move/duplicate any tenant's events by uid.
2. No prompt-injection wrapping. The raw email From/Subject/body were
interpolated straight into an instruction-shaped extraction prompt (unlike
the chat path, which wraps external text via src/prompt_security). Anyone
who can email a user whose instance has auto-calendar enabled could inject
operations: create attacker-controlled "meeting" events (the path even
auto-harvests URLs from the body into the event location/description — a
phishing primitive) or cancel/modify the victim's real events, with zero
human in the loop.
Fix:
- Add core.database.get_upcoming_events(owner) and use it for the snapshot, so
the LLM only ever sees the processed account owner's events.
- Look up the EmailAccount owner in _auto_summarize_pass_single and pass owner=
to every do_manage_calendar call, so create/update/delete are scoped to that
user (owner=None stays the single-user / legacy escape hatch).
- Tell the extraction model the email is untrusted data and not to follow
instructions inside it (defense-in-depth against injection).
Add tests/test_calendar_owner_scope.py: get_upcoming_events returns only the
given owner's events (and everything when owner is None). Fails against the old
unscoped query.
* fix: run bcrypt off the event loop in auth routes
The auth routes are async, but each bcrypt call ran synchronously on the event
loop. bcrypt (checkpw/hashpw) is intentionally CPU-expensive (~100-300 ms), so
every login / signup / setup / change-password froze the single event loop for
that window, stalling all other in-flight requests (chat streams, polling, ...).
/api/auth/login is the worst case: it is reachable unauthenticated, runs bcrypt
twice (verify_password, then create_session re-verifies), and is rate-limited
only per-IP. A burst of login attempts serializes the whole server — cheap
DoS amplification.
Offload the bcrypt-bearing AuthManager calls (setup, signup/create_user,
login's verify_password + create_session, change_password) via
asyncio.to_thread, matching how the codebase already offloads blocking work
(e.g. src/builtin_actions._run_subprocess, email summarize). The event loop
stays responsive while bcrypt runs on a worker thread.
Add tests/test_auth_event_loop.py: asserts login runs verify_password and
create_session on a worker thread, not the loop thread. Fails if those calls
are awaited inline again.
* test: isolate auth event-loop test from heavy core/* import chain
The regression test imported routes.auth_routes, which pulls in
core.auth and so triggers core/__init__.py — transitively importing
src.llm_core (hangs at import under the project venv) and the SQLAlchemy
declarative models (metaclass error on a bare core.database import / under
the conftest sqlalchemy stubs). Reported by the maintainer: collection
failed on system Python and hung under the venv.
Stub core.auth/core.database before the import, mirroring the existing
_ensure_stub pattern in test_auth_regressions.py and test_null_owner_gates.py.
AuthManager is only a type hint here and the handler is exercised with a
MagicMock, so no real core machinery is needed. Test now imports cleanly
and passes in <0.3s without bcrypt/sqlalchemy installed.
/api/auth/settings is auth-exempt (the frontend + the pre-login page read it for
keybinds/TTS prefs), so non-admin and unauthenticated callers get a scrubbed
copy. The previous scrub only blanked TOP-LEVEL string values whose key matched a
short suffix list — so a secret nested under a non-secret parent key, or stored
under a key outside the list, would leak. A real exposure when the app is
reachable over a Cloudflare tunnel / reverse proxy.
- src/settings_scrub.py: NEW stdlib-only module with the scrub helpers (deep/
recursive; broadened secret-key patterns). Kept separate from auth_routes so it
imports + unit-tests WITHOUT pulling the FastAPI / auth / database chain
(addresses review: the test no longer fails at collection on the DB import).
- routes/auth_routes.py: import scrub_settings from the module.
- tests/test_settings_scrub.py: import the tiny module directly.
Ran: pytest tests/test_settings_scrub.py (8 passed); verified the test pulls no
db/auth modules into sys.modules; py_compile routes/auth_routes.py.
Co-authored-by: Kanaru92 <107661007+Kanaru92@users.noreply.github.com>
