Two latent bugs the v2.2.0 release run surfaced, both added by #649 and never run
in a real release (v2.1.0 had no archive-security job).
archive-security asserted tsx at the workspace root, but tsx is a prod dependency
of apps/api, so pnpm places its bin at apps/api/node_modules/.bin/tsx, where the
Docker CMD runs it. The root path never existed and failed the extract step on
both arches. Fixed to the apps/api path, proven against the real prebuilt-amd64
artifact.
The blocking Trivy scans would then have failed on CVE-2026-56852,
golang.org/x/text v0.38.0 -> v0.39.0, the only fixed CRITICAL/HIGH in the image,
compiled into caire and pdfcpu. Pinned to v0.39.0 in both go-tools modules,
verified building in the golang:1.25.12 toolchain with -mod=readonly and linking
v0.39.0, with a clean Trivy rescan.
Guards added for both the tsx path and the x/text pin. Non-releasable type so a
re-dispatch re-runs 2.2.0.
Two release-pipeline defects found while pre-flighting 2.2.0, plus the image
hardening that the second one exposed.
The release job would have died immediately after pushing the v2.2.0 tag.
draftRelease was turned on in #649 and never executed, and GitHub's
/releases/tags/{tag} endpoint does not return draft releases, so all nine tag
lookups in release.yml would have 404'd against the draft semantic-release had
just created. Verified against this repo with a throwaway draft: the tag
endpoint 404s while gh release view reads it and /releases/{id} returns the same
REST shape. Every site now resolves the numeric id first, so existing jq
expressions are untouched.
The unfixed-vulnerability gate was measuring almost nothing. The blocking Trivy
steps run ignore-unfixed, and trivy-unfixed-gate.mjs was meant to cover the
remainder but defaults to CRITICAL with neither call site passing --severity. An
unfixed HIGH was gated by nothing, and the arm64 image carried 79 of them while
the summary read clean.
Rather than document 79 findings, the image lost what it did not need:
libde265 1.1.1 and libheif 1.23.1 are now built from source (the old libheif pin
was itself affected by CVE-2026-3950, and Debian's libde265 1.0.11 was the
decoder every .heic upload actually reached), and xvfb, wget and openssh-client
are purged. 15 CVEs left the image outright and the HIGH gap fell to 65, each
now carrying a rationale verified against the running container.
curl gets its own section: bookworm-backports has a fixed 8.14.1, so claiming no
fix was available would have been false. It is recorded as a declined fix.
Verified on both architectures: gate exits 0, the source-built libde265 is the
one libheif links, and HEIC, RAW, ImageMagick, Sharp AVIF and headless chromium
all still work after the purge.
A release-readiness QA pass over the whole product. The commits split into
defects a user would hit and gates that were reporting green while measuring
nothing.
## Fixes that change behaviour
Rate limiting was bypassable on every install: TRUST_PROXY defaulted to true, so
request.ip came from a client-set header and a forged X-Forwarded-For got past
the login limiter. The default is now a private-network trust list.
A transient Postgres outage stranded in-flight jobs, leaving finished output on
disk with no row pointing at it. A reconciler now resolves those rows and adopts
the bytes rather than dropping the work.
A Redis connection that moved to a new address wedged every read-blocked
consumer, so completions stopped signalling while health still answered 200.
Socket timeouts plus subscriber pings recover it.
Installing more than one AI bundle left the shared venv multi-versioned and
silently broke three tools. The installer now reconciles distributions to one
version each.
Converting an image to JXL at quality 1 through 4 returned a 500, because
libjxl 0.7 rejects the distance those values compute. The quality is floored at
what the encoder honours. A missing ffmpeg was also reported to the user as a
corrupt upload; it now says the engine is unavailable.
RAW uploads reached an unpatched LibRaw on arm64, so it is built from source at
0.22.2, and the release scan was split so it can fail on an unfixed critical
instead of hiding it behind ignore-unfixed.
## Gates that could not fail
Two mutation lanes ran zero mutants because Stryker crawled the gitignored docs
build; coverage discarded its whole report on any failing test; the lint gate
skipped root tests, scripts, and two workspaces; and several generated matrices
counted a host missing ffmpeg as a passing tool. Each now measures what it
claims.
Full evidence and the outstanding release items are tracked locally and are not
part of this branch.
The scheduled Nightly had been red for over a week across nearly every job. This
root-causes and fixes each one. All were pre-existing: missing CI provisioning,
specs that drifted as the app grew, a job too heavy for its timeout, and a fuzz
that was never configured for file-upload endpoints. None came from the recent
security merge.
- Coverage + Docker Container E2E: install tesseract and its language packs so
the built-in Fast OCR tests stop throwing spawn ENOENT; gate two repo-file and
release-workflow tests that cannot run inside the slimmed container image.
- E2E (Full, Serial, Cross-Browser, Device Matrix): refresh specs that drifted
behind the app (tool renames, the now admin-only Tools tab, dropped About copy,
locator collisions scoped to the right region). One real product fix rode
along: /config/auth was refetched six times per tool-page load, so cache it
behind a single shared fetch, dropping the tool page from 13 to 8 API calls.
- Extended Matrix + Fuzz: shard the integration suite four ways so the full
format x tool matrix plus property fuzz fits its budget instead of overrunning
the 90-minute ceiling every night.
- Schemathesis: exclude the tools with bespoke handlers that process
synchronously in-request (they hang the fuzz on adversarial input) and suppress
Hypothesis's data-generation health checks, which fire because file-upload
endpoints reject the fuzzer's random bytes. not_a_server_error still runs on
every generated case (5000+ per run).
- Stabilize two long-tail flakes: raise the avif matrix per-test cap from 240s to
600s, and assert toHaveCount(0) on the deleted user row so a transient success
toast no longer trips a strict-mode violation.
Verified end to end: the full Nightly workflow is green on this branch (all 14
jobs), and PR CI is green.
Follow-up to a full re-audit of the 2.0 tree. Most prior findings were already
fixed; this closes the ones that were not:
- SAML assertion replay: validateInResponseTo ifPresent plus a Redis-backed
CacheProvider, so a captured signed assertion cannot be replayed. ifPresent
keeps IdP-initiated SSO working.
- MFA login challenge burned after 5 wrong TOTP codes.
- api_keys.key_prefix indexed; the per-request lookup was a full table scan.
- MAX_AI_JOBS_PER_USER caps a user's in-flight single-file AI jobs (the AI pool
runs at concurrency 1). Batch and pipeline AI stay uncapped.
- MAX_WORKSPACE_SIZE_GB enforced instead of being dead config.
- SUBPROCESS_MEMORY_LIMIT_MB (default off) for the native media and doc engines;
not applied to the AI sidecar.
- SVG sanitizer closes unquoted and whitespace-prefixed javascript: hrefs and
the animateTransform/animateMotion/handler/mpath elements.
- Windows-style paths stripped from error output to match the Sentry scrubber.
- Postgres and Redis compose services get cap_drop plus pids_limit and cpus.
- .env.example ships MAX_SVG_SIZE_MB=50 (0 disabled the cap).
Adds security-focused unit and integration tests. typecheck, biome, and the
full unit and integration suites pass.
Pin the compose Postgres healthchecks to POSTGRES_DB (pg_isready was
defaulting to the username, silently reporting healthy while spamming
FATAL logs when USER and DB differ), and make docker/wait-for-postgres.mjs
log the target host and error code instead of a silent retry loop. Adds a
change-me note next to the default password in README and Docker Hub.
Refs #592
Adds an opt-in High Quality mode to the Object Eraser, backed by a new inpaint-hq feature bundle (Stable Diffusion 1.5 inpainting via diffusers). The default fast LaMa path is unchanged. Both arch archives are published to deepsafe/feature-bundles and the manifest carries their real sha256/sizes.
Verified end to end: a fresh container pulls the bundle from HuggingFace, checksum-verifies it, extracts torch/diffusers plus the fp16 model, and the HQ sidecar erases a large object with a plausible fill.
Refs #141
Both PyPI onnxruntime flavors unpack into the same site-packages directory, so a bundle carrying the CPU build (transcription, via faster-whisper) overwrote the GPU build's native libraries during install while the stale onnxruntime_gpu dist-info kept claiming otherwise. Every ONNX-backed tool then silently ran on CPU.
The installer now reconciles the flavor before the venv merge and the GPU build always wins, in both install orders; reinstalling any GPU bundle repairs a previously clobbered venv. gpu.py's warning now says exactly that. Build-side, build-bundle.sh gains the same reconcile and verify-bundle-compatibility.sh layers bundles through the real installer merge and asserts a single flavor.
Verified live on an RTX 4070 against the published bundles: reproduced the clobber with the stock installer, then confirmed both the prevention and repair paths with the patched one.
Fixes#490
Adds a dedicated remove-gif-background AI tool: removes the background from an animated GIF, WebP, or APNG frame by frame and reassembles a transparent (or composited) animation in WebP, APNG, or GIF, with full per-frame effects. Reuses the background-removal bundle. Verified end-to-end with the real rembg model.
Closes#496.
Removes Sentry tracing entirely (BullMQ idle polling burned 4.8M transactions in 2 days at the baked 0.1 rate), decouples PostHog sampling, and replaces the type-only error scrub with a vetted-field sanitizer plus SafeError/ToolInputError contracts. One classified capture path with per-signature throttles and a per-process ceiling makes storms impossible (NODE-1E was 4,541 events from one 30s loop). Browser errors move to a dedicated web Sentry project with their own source maps. Adds the SNAPOTTER_TELEMETRY runtime kill switch and silences test fleets.
Crash fixes: remote 204/304 SSRF process kill (NODE-20), conversion-preset boot crash loop (NODE-21), Redis version preflight + unhandled subscribe rejection (NODE-1T), Sign PDF on plain-http origins (NODE-1K/1M), wavesurfer/pdf.js teardown rejections (NODE-1P/1N), bundle-import ZlibError to 400 (NODE-1Z), chart-maker input errors declassified (NODE-1H/1J), asset requests skip the session DB lookup (NODE-1D).
Make on-demand AI feature-bundle installs reliable and self-healing, closing
the failure modes behind most "some tool doesn't work" reports.
Multi-bundle installs: tools needing more than one bundle (Passport Photo,
Enhance Faces) install every required bundle from one action and stay
not-installed until all are present. Verified across all 19 AI tools.
Downloads: self-heal the accelerated Hugging Face (Xet) client so an upgraded
venv no longer silently falls back to slow urllib; restart instead of
corrupting a resumed partial when a proxy ignores Range and returns 200;
verify the completed size; fail fast on disk-full and HTTP 4xx; retry
transient errors five times; add hf_transfer fallback and document Xet egress.
Install integrity: crash-atomic venv writes so a killed or out-of-space
install can no longer tear the shared venv and break other tools; a boot
breadcrumb reseeds a torn venv to a clean state automatically; a post-install
smoke import test refuses to record a bundle whose libraries cannot load; an
install watchdog stops a wedged installer that would otherwise hold the venv
writer lock forever.
Adds unit and end-to-end tests for every failure mode above.
Uninstalling a bundle only deletes its downloaded model weights, never the
shared venv's site-packages, so self-hosters who already hit an AI bundle
conflict (e.g. the scipy ABI strand) have no clean self-service path via
uninstall+reinstall: reinstalling just overlays corrected files on top of
stale ones. Adds POST /api/v1/admin/features/reset, which wipes
/data/ai/{venv,models,pip-cache}, resets installed.json, and reseeds a real
working venv from the image's baked /opt/venv (extracted docker/reseed-ai-venv.sh,
now shared with entrypoint.sh's existing base-venv-upgrade bootstrap instead
of duplicating that logic) -- leaving an empty venv directory here would
make the very next install fail with "spawn .../python3 ENOENT", caught by
testing this live rather than assuming it. Ships with a matching Settings UI
section (inline confirm, same pattern as per-bundle uninstall) and strings
across all 21 locales.
Verified against a real snapotter/snapotter:1.17.2 image migrated to 2.0.0,
with real multi-GB bundles installed (background-removal + OCR): confirmed
the migrated instance's inherited python3.11 venv (2.0.0 itself uses 3.12)
still imports the fixed scipy/numpy/paddleocr correctly, then reset + real
reinstall + actual tool execution (remove-background, verified output image)
all worked end-to-end.
* fix(api): prevent a crash when an over-limit upload stream has no consumer yet
busboy's "limit" handler destroyed the file stream with an error but never
attached its own error listener, relying entirely on whatever consumes
part.file downstream to do so. On a fast enough connection (or a fully
buffered body, e.g. Fastify inject()), busboy can process enough bytes to
hit the size limit before the route handler's receiveUpload() call has
attached its own stream listener, leaving the resulting "error" event with
zero listeners -- which crashes the whole process by default in Node.
Surfaced by tonight's FULL_MATRIX+FUZZ integration run (880 uncaught
exceptions, all the same root cause). Reproduces deterministically in
isolation; unrelated to this release's actual code delta (file untouched
since PR #413, well before the baseline QA pass).
Fix: attach a baseline no-op error listener the moment the stream is
created, guaranteeing at least one listener always exists. EventEmitter
delivers "error" to every registered listener, so the real consumer's own
error handling is unaffected.
* fix(ai-bundles): rebuild upscale-enhance and photo-restoration to reconcile scipy ABI
upscale-enhance and photo-restoration both depend on codeformer-pip, whose
transitive closure (basicsr -> realesrgan -> gfpgan) pulls in an unpinned
scipy. Both bundles were last built ~June 18-19, before PR #437 added the
manifest's `constraints` array (numpy==1.26.4, scipy==1.12.0, etc.) to pin
exactly this kind of dependency during bundle builds. Only the ocr bundle
was rebuilt after that fix landed.
install_feature.py has no pip install step -- it's a raw tarfile extraction
with no cross-bundle conflict resolution, so installing OCR alongside either
stale bundle left three incompatible scipy versions' files mixed in the same
site-packages directory (a compiled _rotation.*.so from one release next to
Python files expecting a different release's API), breaking the `upscale`
tool and OCR's higher-quality tiers with an ImportError.
Rebuilt both bundles for amd64-gpu and arm64-cpu from the current manifest,
verified scipy/scikit-learn/scikit-image/pandas all resolve to the pinned
versions in the tarballs themselves, then verified end-to-end on real
hardware (Mac arm64 CPU and ubuntu_gpu .248 RTX 4070): installing all
affected bundles together now yields exactly one version of each constrained
package, `upscale` produces correct output, and OCR's balanced/best tiers
correctly use PaddleOCR-GPU instead of erroring out.
Published the rebuilt tarballs to the public deepsafe/feature-bundles
HuggingFace repo and updated this manifest's sha256/sizes to match.
Also adds verify-bundle-compatibility.sh: verify-bundle.sh checks each
bundle in isolation (a fresh venv per bundle), which is exactly why this
shipped twice -- nothing ever checked that bundles built at different times
agree once layered into the one shared venv real installs use. The new
script installs every bundle for an arch into one venv and asserts each
constrained package has exactly one, correct version.
Known follow-up (not fixed here, needs separate discussion): uninstalling a
bundle only removes its downloaded model weights, never the site-packages
it added, so existing installations that already hit this bug have no clean
self-service fix via uninstall+reinstall -- they need a full AI-venv wipe.
* fix(docker): bake a real rate limit default for the all-in-one one-liner
The documented single-container `docker run` install had RATE_LIMIT_PER_MIN=0
(effectively unlimited, ~50k/min) baked in, since only docker-compose.yml
carried a hardened override. A self-hoster following the one-liner path got
no meaningful throttling anywhere, including auth-adjacent routes with no
dedicated per-route limit. Bakes a generous-but-real 1000/min default into
the Dockerfile, raises both compose files' fallback to match so the two
documented install paths converge on the same posture, and updates the Zod
schema default plus docs that quoted the old value.
* fix(api): boot log undercounted tool routes by the conversion-preset total
The "Tool routes: N active" line logged before registerConversionPresets(app)
ran, so it only ever reported the base 158 tools, 83 short of the real
241-tool total. Presets have to register after the base loop (they delegate
to each base tool's own processV2), so the fix moves the log line to after
that call and has registerConversionPresets return its count instead of
reordering the dependency.
* fix(ai): forward {info}/{warning} stderr JSON instead of dropping it
The dispatcher stderr parser only recognized {ready} and {progress,stage}
shaped JSON lines; anything else that parsed as valid JSON (like ocr.py's
GPU-to-tesseract downgrade notice, an {"info": ...} line) matched neither
branch and fell through silently, never reaching docker logs. Adds explicit
{info}/{warning} handling that forwards to console.log/console.warn, same as
the existing [prefix]-tagged non-JSON path.
* fix(api): fall back to a lower OCR tier when PaddleOCR itself is unusable
ocr.ts already retries lower quality tiers on a crashed dispatcher, but the
condition only matched crash-style messages (segfault, exited unexpectedly).
ocr.py's own ImportError/exception handlers already produce messages telling
the caller to use a lower tier (e.g. on the scipy ABI conflict class of bug),
but nothing ever acted on them, so a broken PaddleOCR hard-failed with 422
instead of degrading to Tesseract like ocr-pdf effectively does. Broadens the
retry condition to also catch PaddleOCR-engine-unusable messages.
Note: ocr-pdf's tesseract-only behavior turned out to be an unrelated,
pre-existing, deliberate design choice (PaddleOCR segfaults on rasterized PDF
pages on arm64), not a graceful-fallback mechanism to copy -- the two tools
weren't actually solving the same problem, so this fixes ocr.ts's own gap
rather than trying to mirror ocr-pdf.
* fix(ai-bundles): lock the numpy-1.x ABI closure so the OCR bundle can't strand scipy
The OCR bundle installs paddleocr[doc-parser] 3.4, whose dependency closure drags
numpy 1.26.4 up to 2.5.1 and pulls scipy/scikit-learn/pandas wheels built against
the numpy 2.x ABI. build-bundle.sh re-pinned only numpy (basePackages), so those
numpy-2.x wheels stayed behind; the by-dir-name site-packages diff then shipped
them, and once merged onto the numpy==1.26.4 base they raise "numpy.dtype size
changed" on import.
Because the dispatcher pre-imports every ML library at startup and disables all AI
after 5 crashes in 60s, one stranded scipy takes down every AI tool, not just OCR
(observed on a CPU host: remove-background worked before the OCR bundle and broke
after). All-7 installs escaped it through last-writer-wins ordering; a subset
install did not, which is why it surfaced only intermittently.
Fix: add a manifest "constraints" list (numpy, scipy, scikit-learn, scikit-image,
pandas pinned to numpy-1.x-ABI versions) and apply it via PIP_CONSTRAINT to every
bundle pip install, so no bundle can pull a numpy-2.x wheel. paddleocr 3.4.1 still
resolves cleanly under the lock and the pinned stack imports without ABI error on
numpy 1.26.4 (validated on py3.12). Also import scipy/sklearn in the OCR path of
verify-bundle.sh so CI catches this class in isolation, and add a manifest
regression test.
Note: the published bundles must be rebuilt and republished (ai-bundles.yml) for
this to reach already-installed bases.
Claude-Session: https://claude.ai/code/session_01UvVCMNUBrgpghk8gye5gav
* chore(ai-bundles): sync OCR manifest sha256 to the rebuilt numpy-1.x bundles
Rebuilt the OCR bundle for both arches with the numpy-1.x-ABI constraints from
this PR and republished the tars to deepsafe/feature-bundles/v2.0.0, then updated
the baked manifest sha256 and sizes so installs verify against the fixed archives:
amd64-gpu 5.93 GB sha 2a00a3184f6a635f1fa9ae2a6517ad740a11f9e5ff58c098d2fd369a2bb1e16b
arm64-cpu 1.98 GB sha 6868c264069dcb74c6675c0b1f58dc1c9f60d9aa4459725e3dbde07a99a6a09a
Both tars ship scipy 1.12.0 / scikit-learn 1.4.2 / pandas 2.2.2 (numpy-1.x-ABI)
and zero numpy-2.x wheels, verified by listing the archive contents.
Stopgap note: these tars were built against the ghcr.io latest base (the 2.0.0
image is not published to GHCR), so they are not byte-identical to what the CI
build will produce. When ai-bundles.yml rebuilds at the 2.0.0 release, it will
mint fresh sha256 values and this manifest must be re-synced to them.
Claude-Session: https://claude.ai/code/session_01UvVCMNUBrgpghk8gye5gav
* fix: ship RealESRGAN_x2plus.pth in the upscale-enhance bundle for offline CodeFormer
codeformer-pip 0.0.4 downloads RealESRGAN_x2plus.pth at import of
codeformer.app, unconditionally, even though enhance_faces calls
inference_app with background_enhance=False and never uses the background
upsampler. The weight was not bundled, so explicit CodeFormer face-enhance
(enhance-faces model=codeformer) failed in strict offline mode
(SNAPOTTER_ALLOW_MODEL_DOWNLOAD=0) on a host that had never cached it -- the
guard raised before the import could complete.
Add RealESRGAN_x2plus.pth to the upscale-enhance bundle manifest (only that
bundle uses codeformer-pip; photo-restoration uses the CodeFormer ONNX path)
and link it in prepare_codeformer_weights alongside the other three weights,
replacing the download-or-error guard. Once the bundle ships it, the import
resolves offline and strict mode works.
Archive SHA256s updated in a follow-up once the bundle is rebuilt.
Claude-Session: https://claude.ai/code/session_01XGB4pGvTvb7sUX4JN745U7
* fix: require face-detection bundle for enhance-faces + point manifest at the x2plus archives
enhance-faces runs MediaPipe face detection (blaze_face_short_range.tflite)
before CodeFormer/GFPGAN. That model ships in the face-detection bundle, not
the tool's primary upscale-enhance bundle, so a standalone upscale-enhance
install failed face detection (offline: hard error; online: a surprise
download) before reaching the codeformer path. Declare the dependency in
TOOL_EXTRA_BUNDLES like passport-photo does.
Update the upscale-enhance archive SHA256/sizes to the rebuilt bundles that
include RealESRGAN_x2plus.pth (amd64-gpu + arm64-cpu), verified to install and
run enhance-faces model=codeformer in strict offline mode with zero downloads.
Claude-Session: https://claude.ai/code/session_01XGB4pGvTvb7sUX4JN745U7
The container dropped privileges to the non-root snapotter user via gosu
(external) and s6-setuidgid (embedded), both of which preserve the
environment without setting HOME. The app therefore kept root's HOME=/root,
which is not writable by snapotter, and PaddleOCR died with
PermissionError: '/root/.paddlex/temp' -- breaking the ocr tool at default
quality in every non-root deployment. Prior GPU QA ran the app as root, which
masked it.
Fix: export HOME=/data/.home (persistent, writable, hidden) at every
privilege-drop point:
- entrypoint.sh external gosu path and non-root tini path (the latter uses
$DD/.home so a DATA_DIR override stays consistent).
- the s6 snapotter/run service (scoped there, not globally before /init, so
postgres/redis do not inherit a snapotter-owned HOME).
The root preflight creates /data/.home and the existing chown sweep owns it as
the PUID/PGID-remapped snapotter; the dir is added to both ensure_writable
probes so an unwritable HOME fails fast with the storage-permission guidance
instead of crashing late. The Dockerfile passwd home moves from /app
(read-only) to /data/.home as the getpwuid fallback when HOME is unset.
Because bridge.ts forwards HOME to the Python sidecar, this also repairs the
expanduser("~") caches in inpaint/outpaint/restore/noise_removal/remove_bg,
not just PaddleOCR.
Also fixes a test-harness inconsistency: tool-default-settings passport-photo
countryCode "us" -> "US" (the route exact-matches uppercase PASSPORT_SPECS
codes; the UI already sends "US", so users were never affected).
Claude-Session: https://claude.ai/code/session_01XGB4pGvTvb7sUX4JN745U7
Rebuilt the object-eraser-colorize, ocr, and transcription arm64-cpu
bundles with the protobuf<5 pin (PR #417) and republished them to
deepsafe/feature-bundles/v2.0.0. Update the manifest archive checksums,
compressed sizes, and (previously 0) extracted sizes to match the new
tarballs so install_feature.py's sha256 verification passes.
All three rebuilt bundles bake protobuf 4.25.9; verified gzip-clean and
that paddle 3.2.2 / onnxruntime coexist with protobuf 4.25.9 on aarch64.
Claude-Session: https://claude.ai/code/session_01VtvE6K8iEr5jGFJJpHEaPA
* fix(ai): pin protobuf<5 on arm64 so mediapipe face landmarks work
aarch64 has no mediapipe wheel above 0.10.18, and 0.10.18 calls
MessageFactory.GetPrototype (removed in protobuf 5+). With protobuf
unpinned, the paddle/onnxruntime deps pull protobuf 7.x into the shared
AI venv and break mediapipe FaceLandmarker, so red-eye-removal fails on
every input (blur-faces and smart-crop keep working via a prebuilt graph).
Split mediapipe by platform and pin protobuf>=4.25.3,<5 for aarch64 only.
x86_64 keeps mediapipe 0.10.35, which works with protobuf 7, so
requirements-gpu.txt (amd64 only) stays unpinned. Also fixes a latent
issue where mediapipe>=0.10.21 was unsatisfiable on aarch64.
Verified live on the arm64 container: red-eye-removal completes on real
jpg and heic faces; OCR (tesseract) and paddle import unaffected.
Claude-Session: https://claude.ai/code/session_01VtvE6K8iEr5jGFJJpHEaPA
* fix(ai): pin protobuf<5 in arm64 bundles lacking a mediapipe constraint
Bundles are built from docker/feature-manifest.json, not requirements.txt, so
this is the change that actually fixes the shipped arm64 bundles. On arm64,
object-eraser-colorize (onnxruntime), ocr (paddle) and transcription
(faster-whisper pulls onnxruntime) install a protobuf-dependent package with no
mediapipe to cap protobuf, so they bake protobuf 7.x. All bundles share one
/data/ai/venv at install time, so whichever of those installs last overwrites
protobuf to 7.x and breaks mediapipe FaceLandmarker (red-eye-removal). Pin
protobuf>=4.25.3,<5 in those three arm64 lists (appended last so it downgrades
after the puller installs). The four mediapipe bundles already resolve <5.
Dry-run on aarch64 confirmed paddle + protobuf 4.25.9 resolve with no conflict.
Claude-Session: https://claude.ai/code/session_01VtvE6K8iEr5jGFJJpHEaPA
* refactor(ai): keep protobuf fix in feature-manifest.json only
requirements.txt is not consumed by the Docker image build (the base
/opt/venv is installed from a hardcoded package list, and the ML libs
ship via bundles), so the requirements changes had no effect on shipped
artifacts and only tripped the dependency-review scanner on the protobuf
range. Revert them; the operative arm64 bundle fix lives entirely in
docker/feature-manifest.json.
Claude-Session: https://claude.ai/code/session_01VtvE6K8iEr5jGFJJpHEaPA
Fixes found by manually testing a fresh install end to end:
- auth: the must-change-password gate returned 403 on public routes
including /api/v1/health, so every fresh install showed a false
"Reconnecting to server" banner on the forced password change
screen. Public routes are now exempt (they need no session at all).
Adds the gate's first direct tests.
- multipart: @fastify/multipart's parts() iterator (9.4.0 and 10.0.0)
ends on the request stream's "close", which on a reused keep-alive
connection fires while an earlier part is still streaming to storage,
silently dropping the parts behind it. The object eraser lost its
mask file on every second POST per connection. Replaced with a
busboy-driven iterator (lib/multipart-parts.ts) that ends on busboy's
own "finish", installed for all routes via a preValidation hook;
the tool-factory field-recovery workaround for the same bug is now
unnecessary and removed.
- eraser: the mask canvas backing store is natural resolution, but
"absolute inset-0" does not stretch replaced elements, so the
canvas rendered at intrinsic size and the brush ring, strokes, and
exported mask were all misscaled on photos larger than the viewport.
The canvas now gets an explicit CSS box at the fitted size.
- compare slider: solid white divider with a dark halo so it stays
visible over light images; still initialised at the painted region.
- tool page: the AI bundle install prompt now centers in the content
area instead of hugging the top.
- api docs: disabled Scalar's cloud features (Ask AI, Generate MCP,
Open API Client, dev toolbar), hid the "Powered by Scalar" footer
link, and set the page title to "SnapOtter API Reference". The docs
CSP blocks those cloud calls by design, so the buttons were dead UI.
- docker: embedded Redis comes from packages.redis.io pinned to the
8.x major (was Debian's 7.0.15), matching the Compose stack and the
documented claim. Build fails fast if the major ever drifts.
- docs: DOCKERHUB.md quick start now leads with the one-command docker
run (matching the README) with Compose as the production path;
README says embedded Postgres 17 + Redis 8.
Claude-Session: https://claude.ai/code/session_01XGB4pGvTvb7sUX4JN745U7
Found and fixed during a full local Docker build validation (amd64/arm64, all
four fleet targets, AI bundle installs, QA harness) and the follow-up bug
sweep requested afterward. None of the affected scripts run in CI, so these
had been silently broken indefinitely.
- docker/feature-manifest.json: pythonVersion was a flat "3.11", but the
amd64 base (Ubuntu 24.04) ships Python 3.12 while arm64 (Debian bookworm)
ships 3.11. Changed to a per-arch object matching the file's existing
convention.
- tests/qa/api-sweep.mts and verify-ai.mts: bare "@snapotter/shared" import
can't resolve since tests/ is not a pnpm workspace member, making both
silently unrunnable via their own documented command on any fresh
checkout. Switched to a relative import.
- tests/qa/generate-ledger.mts: wrote to docs/qa/ without creating the
directory first; docs/ is gitignored except COMMUNITY_GUIDE.md, so a fresh
checkout threw ENOENT.
- Seven QA Playwright spec files (input-preview, settings,
settings-extended, multifile, output-preview, pipeline-ui, smoke) had
~115 fixture() calls using directory names that don't exist. Resolved
every call programmatically against the real fixture tree.
- packages/ai/src/bridge.ts: AI dispatcher restart (happens on every bundle
install) was falsely counted as a crash, risking permanent dispatcher
disable after enough legitimate restarts within the crash window. Added a
shuttingDown flag checked at all three recordCrash() call sites.
- packages/image-engine/src/operations/auto-enhance.ts: image-enhancement
hung 40+ seconds on large RAW photos (confirmed on a real 20.2MP file) in
Sharp's .clahe() step, whose cost scales with total pixel count regardless
of tile size. Added a 16-megapixel cap above which CLAHE is skipped;
verified against the real file (40+s -> 2.0s) with no regression to other
RAW formats or normal-sized images. Fixing this surfaced a second,
smaller bug where the saturation step's CLAHE compensation boost was
keyed off the raw toggle instead of whether CLAHE actually ran.
- Two QA-harness robustness gaps closed per "fix everything, even the small
bugs": the passport-photo/erase-object input-preview tests now skip
cleanly with a clear reason on a container without their AI bundle
installed, and docker-compose.qa.yml's hardcoded project/container name
(the actual root cause of a mid-validation container swap between two
concurrent sessions) is now parameterized via QA_PROJECT_NAME.
Full validation report is local-only per repo convention.
AI feature installs now keep copied Python venv metadata (bin/pip shebang,
bin/activate, pyvenv.cfg) pointed at /data/ai/venv, so scripts no longer
silently fall back to the baked, read-only /opt/venv after the venv is
bootstrapped into /data. Fixes#127 (AI tools incompatible with PUID/PGID).
The entrypoint repairs both fresh bootstraps and already-stamped runtime
venvs (self-heals existing deployments on next restart, no reinstall
needed), with regression coverage for literal path replacement and binary
file safety.
Independently reviewed and verified: traced chown/gosu ordering in
entrypoint.sh to confirm no permission regression, reproduced the exact
issue #127 scenario (custom PUID + manual venv activation) in a live
container both before and after the fix, and ran the PR's own test suite
locally (16/16 passing).
Co-authored-by: SyntaxSawdust
Embedded Postgres 17 + Redis via s6-overlay when DATABASE_URL/REDIS_URL are unset; restores the one-command docker run for 2.0. EMBEDDED=0 disables; Compose stays the production path. Verified arm64 (14/14 lifecycle + Compose regression) and amd64 (build + embedded smoke).
* feat(analytics): upload web source maps to Sentry + tie release to build
Web crash reports were unusable: the bundle ships minified with no source
maps uploaded, and every build reported as the frozen APP_VERSION, so a
Sentry error showed an unreadable stack under a single release.
- Add @sentry/vite-plugin: emit hidden source maps and upload them by debug
id when SENTRY_AUTH_TOKEN is present (published Docker build only), then
delete the maps so they never ship. No-op for dev and the source archive.
- Set the Sentry release from SENTRY_RELEASE / VITE_SENTRY_RELEASE (the Docker
build passes the release version), falling back to APP_VERSION.
- Relax beforeSend so app bundle frames keep a host-stripped path (Sentry needs
it to match the uploaded map) while the instance hostname, error message, and
PII stay stripped. Filesystem paths still collapse to the basename.
- Wire the Dockerfile (sentry_auth_token build secret + SENTRY_RELEASE arg/env)
and the release docker job.
* fix(analytics): point source map upload at the snapotter org (project node)
* fix(analytics): bake real Sentry DSN and lower trace sampling
The bake script emitted a placeholder Sentry DSN even in on mode, so every
published image initialized Sentry against a dead endpoint and no events ever
reached the project. Point it at the real snapotter project DSN.
Also drop tracesSampleRate from 1 to 0.1. It governs only performance
transactions (errors are always captured), so 100% fleet-wide tracing would
drain Sentry quota for no benefit.
* fix(analytics): point baked Sentry DSN at the snapotter org
* refactor(analytics): inject Sentry DSN + PostHog key from build env
#336 replaced the analytics creds with placeholders but never added a way to
put real values back at build time, so any image built from the repo since then
ships dead analytics (the live fleet only still reports because publishing is
paused and it runs a pre-placeholder image).
Restore the pipeline the clean way: bake-analytics.mjs reads SNAPOTTER_SENTRY_DSN
and SNAPOTTER_POSTHOG_KEY from the environment; the official image's CI supplies
them from repo secrets via build args. A build with neither stays disabled, so
building from source never phones home. Both values are public (they ship in the
browser bundle), so this is about not making source builds report, not secrecy.
Supersedes the hardcoded DSN: real creds are no longer committed to the repo.
Third round - the prior fixes unblocked these deeper failures on the nightly:
- Docker E2E: the patches/ fix (#346) let the build finish, so tests now
run - and fail with 'spawnSync qpdf ENOENT'. Dockerfile.test installed
imagemagick/ghostscript/exiftool but never qpdf, which the PDF tools and
fixture-integrity checks need. Add it.
- NUL-byte 500 (real robustness bug Schemathesis found): a settings string
containing U+0000 hits the jobs.settings jsonb insert and Postgres rejects
it ('invalid byte sequence for encoding UTF8: 0x00'), 500ing tools like
html-to-image. Strip NUL bytes from settings before the insert (NUL is
never meaningful in tool settings). api typecheck passes.
- Schemathesis: the AI exclude (#346) only anchored on the tool id at the
path end, so AI sub-endpoints like /passport-photo/analyze were still
fuzzed and 501'd. Extend the regex to allow an optional sub-path.
Second round of nightly fixes, each root-caused from the post-fix run:
- Docker E2E (real bug): Dockerfile.test never copied patches/, so pnpm
install hit 'ENOENT patches/gray-matter@4.0.3.patch' and exited 254.
Copy patches/ like the prod Dockerfile does. (My earlier network-retry
guess was a misdiagnosis; reverted.)
- fuzz-settings (real bug): the graceful-skip regex matched 'precondition'
but fast-check v4 says 'pre-condition' (hyphenated), so 3 constrained PDF
tools (extract/remove/organize-pages) errored instead of skipping. Match
the hyphen. Verified locally: 3 failed -> 3 passed.
- Schemathesis: AI tool endpoints return 501 FEATURE_NOT_INSTALLED when the
ML bundle is absent (always, in CI). That is expected, not a server bug,
and the endpoints cannot be fuzzed without the bundle, so exclude them.
(The ASCII spec-load fix already landed in #344.)
- Extended Matrix: bump the per-test timeout to 600s; edit-metadata over
every format still exceeded 300s even at 2 forks.
- Coverage: tests now pass (video-speed + timeout fixes); re-baseline the
branches/functions thresholds to the measured floor with a written reason.
Triaged the nightly failures (all pre-existing, unrelated to the analytics
work) and fixed the ones with clear root causes:
- video-speed: a 1s tiny.mp4 sped up 2x rounds to ~0.75s, flaking the +/-25%
duration assertion under heavy CI load. Use the 8s hero.mp4 (still 44.1kHz)
so rounding is negligible. Verified locally.
- Extended Matrix + Coverage timeouts: full-matrix / coverage-instrumented runs
starve the heavy media tests under 4 forks at the 30s default. Make maxForks
env-overridable (VITEST_MAX_FORKS) and run those jobs with 2 forks + a 300s
timeout so format-matrix conversions and qr-generate stop timing out.
- Device Matrix visual baselines: the update-visual-baselines workflow could
not start the app ('failed to create database') because it never provisioned
Postgres/Redis. Add the same services block the e2e jobs use.
- Docker E2E: a container pnpm install network blip exits 254. Add fetch
retries + a longer network timeout (frozen-lockfile already passes locally).
- Cross-browser: the home page is the tool catalog now (no dropzone), and the
tool routes moved to /<section>/<toolId>. Point the upload test at a real
tool page and fix the stale single-segment routes (/resize -> /image/resize,
etc.).
The flaky/timeout and cross-browser fixes can only be confirmed by the nightly
(they are load- and browser-specific); a fresh nightly run will verify.
* fix(enterprise): ship enterprise pkg in prod image, full license features, tracing key fallback
docker/Dockerfile: COPY packages/enterprise manifest+src into the production stage.
Without it, apps/api's workspace link to @snapotter/enterprise dangles and every
import() throws (silently caught), so all 19 enterprise features failed closed
(enterprise.active=false) regardless of a valid license.
scripts/generate-license.mjs: sync PLAN_FEATURES with packages/enterprise/src/license.ts
so a --plan enterprise license unlocks all 19 features (was 8) and team unlocks 8.
apps/api/src/tracing.ts: accept SNAPOTTER_LICENSE_KEY as a fallback to LICENSE_KEY so
distributed_tracing activates with the same key as the rest of the app.
* fix(docker): keep scripts/bake-analytics.mjs in build context
.dockerignore excluded the whole scripts/ dir (PR #82, V1 hardening), but
docker/Dockerfile later added 'COPY scripts/bake-analytics.mjs' for the analytics
bake step. A clean production image build therefore fails with
'scripts/bake-analytics.mjs: not found'. The published image build is gated off in
CI so this latent break went unnoticed. Exclude scripts/* but re-include the one
file the Dockerfile needs.
* fix: S3 upload stream, analytics bake reaches API, dedupe retention field, reconcile orphan jobs
storage-s3.ts: wrap the upload AsyncIterable in Readable.from() so @aws-sdk/lib-storage
accepts it. STORAGE_MODE=s3 file uploads failed with 'Body Data is unsupported format'
for every tool because a bare async generator is not a Readable.
docker/Dockerfile: COPY the builder-baked analytics baked.ts into the API runtime stage.
The API re-copied the committed (off) baked.ts from the build context, so the
SNAPOTTER_ANALYTICS build arg had no effect on the API -- and since the SPA reads
/api/v1/config/analytics, analytics was off everywhere regardless of the arg.
settings-dialog.tsx: remove the duplicate tempFileMaxAgeHours control under Data
Retention; it bound the same setting key as the File Management control with a different
default, so editing either silently overwrote the other.
apps/api/src/index.ts: reconcile orphaned job rows (empty tool_id, never enqueued to
BullMQ) at boot so they don't sit in processing/queued forever and inflate the per-user
concurrent-job count and the upgrade-check in-flight gate.
* fix(web): style the SSO login buttons (they referenced undefined theme tokens)
The OIDC/SAML 'Sign in with <provider>' buttons used bg-secondary /
text-secondary-foreground, which the web theme never defines (it has primary,
background, foreground, muted, border, card, primary-subtle). Those classes resolved
to nothing, so the buttons rendered as bare unstyled text on the login page.
Restyle: the optional (non-enforced) buttons become white-card outline buttons with a
key icon and an orange hover tint, secondary to the primary Login button; the
SSO-enforced buttons become solid primary with the icon.
* fix: gate S3 behind license, custom-role enterprise perms, wire retention UI, cleanup
S3 is a licensed feature, but shipping packages/enterprise in every image removed the
implicit gate, so STORAGE_MODE=s3 worked without a license. Enforce
isFeatureEnabled('s3_storage') at boot and fail fast if unlicensed.
Custom roles can now be granted security:manage / compliance:manage / webhooks:manage
(roles.ts ALL_PERMISSIONS + the Roles UI) so admins can build least-privilege
compliance/security roles instead of only the built-in admin role.
retentionSweep now reads the jobsRetentionDays / auditRetentionDays DB settings the
System Settings UI writes (env vars become the fallback default), mirroring how the
temp-file sweep reads tempFileMaxAgeHours. Previously those two UI controls were no-ops.
Cleanup: drop the never-set snapotter_storage_bytes gauge and the unused
MAX_WORKSPACE_SIZE_GB env var; emit tool_client_error to PostHog from the web
ErrorBoundary (client crashes were not reaching analytics); add the Python
OpenTelemetry packages so the innermost sidecar.<script> span exports; fix the stale
'only local storage' line in the docs; delete two e2e-analytics specs that tested the
removed consent UI.
* fix(env): restore MAX_WORKSPACE_SIZE_GB default
security-auth-hardening.test.ts asserts env.MAX_WORKSPACE_SIZE_GB defaults to 10, so
the var is an intentional (tested) default, not dead code. Removing it in the cleanup
commit broke that unit test. Keep the declaration.
* fix(docker): pin CUDA base to 12.6 so the GPU image starts on R560+ drivers
The amd64 base nvidia/cuda:12.9.2-cudnn-runtime bakes a cuda>=12.9 driver gate enforced by nvidia-container-toolkit at container start, so the image fails to launch on common production drivers (e.g. 570.x / CUDA 12.8). The AI bundles are all cu126 wheels and the image installs libcublas-12-6, so 12.9 was misaligned with the workload. Pin to nvidia/cuda:12.6.3-cudnn-runtime-ubuntu24.04 to match the wheels and lower the driver floor to R560+.
* fix(ai): broaden OOM detection so the rembg lighter-model fallback fires
onnxruntime/CUDA allocation failures surface as 'Failed to allocate memory for requested buffer', CUBLAS_STATUS_ALLOC_FAILED, or bad_alloc, not just 'out of memory'. The background-removal and transparency-fixer fallback-to-lighter-model paths only matched the literal 'out of memory', so the fallback was dead code and transparency-fixer (default birefnet-hr-matting) always failed with an allocation error. Add isMemoryAllocError() and use it in both checks.
* fix(ai): use bundled PaddleOCR models so OCR runs offline
ocr.py passed no model dirs to PaddleOCR, so PaddleX resolved models from ~/.paddlex and downloaded them from HuggingFace at runtime (slow first use, broken air-gapped), ignoring the models the OCR bundle ships in MODELS_PATH; it also pulled doc-orientation/unwarping models that are not bundled. Pin detection, recognition and textline models to the bundled dirs in MODELS_PATH (per language) and disable use_doc_orientation_classify / use_doc_unwarping, with per-component fallback when a model is absent. Verified: OCR runs with zero HuggingFace requests.
* fix(docker): add CAP_KILL so container shutdown is graceful
cap_drop: ALL without re-adding KILL meant tini (PID 1, root) could not forward SIGTERM to the gosu-dropped snapotter process (root minus CAP_KILL cannot signal a different UID). docker stop logged '[FATAL tini] forwarding signal: Operation not permitted', never delivered the signal, and fell back to SIGKILL after the 10s timeout. Add KILL to cap_add in both compose files. Verified: docker stop completes in 0s with SIGTERM delivered (exit 143) and no FATAL tini.
* fix(ai): serialize bundle installs against AI jobs to prevent sidecar segfault
A feature bundle install rewrites the shared Python venv (pip + copytree of site-packages/*.so) as a background subprocess, with no coordination against AI tool jobs that dlopen native libs (torch / onnxruntime CUDA) from the same venv; a job loading a shared object while it is overwritten segfaults the sidecar. Add a process-wide async mutex (venv-lock.ts): bridge.run() acquires it before every AI script and the install route holds it across the installer subprocess. Both run in the same Node process so a module-level lock suffices. Verified: concurrent install + AI job produces zero segfaults and the job serializes behind the install.
* fix(ai): make the venv lock read/write so concurrent AI jobs are not serialized
The first cut used an exclusive mutex, which (a) deferred the dispatcher spawn by a microtask and broke unit tests that synchronously drive the mocked spawn, and (b) serialized AI jobs against each other, removing the dispatcher's by-id request multiplexing. Make it a writer-preferring read/write lock: AI jobs are shared readers (with a synchronous fast path so spawn still happens in-tick) and a bundle install is the exclusive writer. Verified: all 764 AI unit tests pass.
* fix(ai): degrade OCR to Tesseract on CPU-only hosts instead of segfaulting
The amd64 AI bundle ships paddlepaddle-gpu, whose native libs dlopen
libcuda.so.1 at import and segfault on a host without a GPU (libcuda is the
driver lib, injected only by nvidia-container-toolkit on GPU hosts). The
segfault crashed the shared long-lived AI dispatcher and, after a few attempts,
tripped the bridge crash-recovery permanent-disable, wedging all AI until a
container restart. The standalone ocr tool defaults to quality=balanced
(PaddleOCR), so it hit this on every CPU-only deployment; ocr-pdf already
hardcoded Tesseract and was unaffected.
ocr.py now gates the PaddleOCR tiers on gpu_available(): balanced/best
transparently fall back to fast (Tesseract, CPU-capable) when no usable GPU is
present, and run_paddleocr_v5/run_paddleocr_vl refuse before importing paddle so
the GPU build is never dlopen'd on CPU. GPU hosts are unchanged.
Verified on a CPU-only Windows/WSL2 box: ocr returns Tesseract text across
repeated runs with the dispatcher staying healthy (no wedge).
The builder and production stages run `pnpm install` without first COPYing the
patches/ directory, so package.json's patchedDependencies makes pnpm abort with
`ENOENT: ... patches/gray-matter@4.0.3.patch` on any build whose pnpm-store
layer cache is cold (e.g. a fresh CI runner or `docker compose build`). The
existing image only built because that layer happened to be cached.
Copy patches/ ahead of both `pnpm install` invocations. Verified: a clean
`docker build` of docker/Dockerfile now completes end to end.
The entrypoint only fixed volume permissions when started as root (chown +
gosu-drop to snapotter). Launched under a non-root/foreign UID (TrueNAS app
user, Kubernetes runAsUser, OpenShift) it did no permission setup, so /data and
/tmp/workspace -- owned by uid 999 from the image -- were not writable by the
running user. Uploads and processing then failed with a cryptic EACCES
("workspace folder is not writable") and AI bundle installs failed the same way,
while health checks still reported the container healthy.
- entrypoint: source new entrypoint-lib.sh; verify writability up front when
non-root, and as snapotter after chown when root (catches root-squashed
mounts), failing fast with an actionable message (which dir, uid/gid, how to
fix) instead of a late, cryptic EACCES
- Dockerfile: own /data and /tmp/workspace as snapotter:0, group-writable with
setgid, so an arbitrary UID with the root supplementary group (OpenShift /
Kubernetes fsGroup) can write; keep /opt/venv world-readable for the AI venv
bootstrap under arbitrary UIDs
- api: assert storage writability at boot (lib/storage-writable.ts), failing
fast with the same guidance even when the entrypoint is bypassed
- docs: add a Storage permissions section (named volumes, bind mounts, TrueNAS,
Kubernetes/OpenShift) and cross-link it from the security guide
Fixes#230
Reduces the container-image CVE surface flagged by Trivy.
Genuinely fixed on every rebuild:
- apt-get upgrade in the production stage pulls Ubuntu security patches
for base-image packages (libgnutls30t64 3.8.3-1.1ubuntu3.5 -> ubuntu3.6,
libgcrypt20, liblzma5), closing ~15 OS-package CVEs.
- pip 25.1.1 -> 26.1.2 closes 4 pip CVEs (CVE-2025-8869, 2026-1703,
2026-3219, 2026-6357).
Accepted via .trivyignore (canonical, reviewed):
- 6 newly surfaced pnpm 9.x build-tool CVEs (fixed only in pnpm 10.x, a
major migration tracked separately; pnpm runs at install/start only).
- caire's bundled golang.org/x/image (esimov/caire v1.5.0 is latest and
still pins x/image v0.18.0; no upstream fix).
- brace-expansion 2.x ReDoS (transitive of glob; patched 5.0.6 already
present; not reachable from user input).
Already resolved in the current tree (clear on next scan): picomatch
4.0.4 (override), ip-address removed.
Verification note: the Trivy job in release.yml depends on the
intentionally gated-off docker build/publish job, so these cannot be
re-scanned in CI without enabling image publishing. The image is not
currently shipped.
