Token-Permissions (0 -> 10): Set permissions: {} at workflow top level
across all 7 workflows, moved write scopes to per-job minimum.
SAST (0 -> 10): Added CodeQL workflow for JavaScript/TypeScript and
Python analysis on push, PR, and weekly schedule.
Vulnerabilities (0 -> ~8): Added 13 pnpm overrides to patch transitive
dependency vulnerabilities (38 -> 2 remaining, both in dev-only tools).
Pinned-Dependencies (5 -> 8-9): Pinned all Docker FROM images to SHA
digests, pinned pip-audit version in CI, pinned pip version in
Dockerfile.
Handle OOM kills (exit code 137) with actionable memory guidance,
filter ANSI/progress noise from error output, add --no-cache-dir to
pip installs, reduce download concurrency to 2, and bump default
container memory from 4g to 6g.
When AUTH_ENABLED=false, the anonymous user was assigned the "user" role
which lacks settings:write permission, making all settings saves return
403. Since no admin exists when auth is disabled, settings were
permanently read-only. Promote the anonymous user to "admin" so the
single user has full control of the instance.
Also adds DEFAULT_TOOL_VIEW env var (sidebar|fullscreen) following the
existing DEFAULT_THEME pattern, seeded via ensureDefaultSettings() on
first boot.
Closes#135
- LOGIN_ATTEMPT_LIMIT: 500 -> 30 in Dockerfile (matching code default)
- RATE_LIMIT_PER_MIN=0 now means no global limit (50K ceiling) instead
of 1 req/min, preserving backward compatibility with existing deploys
while keeping per-route limits (login brute-force) active
Auth: login rate limit 30/min (was 500), global rate limit 1000/min (was
unlimited), password/username max lengths on all Zod schemas, session
invalidation on role change, API key legacy scan bounded to 100 keys.
SVG: hardened regex sanitizer with CDATA stripping, XML entity decoding,
set/animate/iframe/embed blocking, comprehensive data: URI blocking,
use element external href blocking. 11 attack payload fixtures added.
SSRF: fixed DNS rebinding TOCTOU by pinning resolved IPs via custom
HTTP/HTTPS agents. Added 6to4 and NAT64 to blocked IPv6 ranges.
Docker: capability dropping (cap_drop ALL + minimal cap_add), resource
limits (4g/8g mem, 512/1024 pids), healthcheck timeout, password
removed from startup banner, default password warning comments.
Network: CSP and HSTS applied in all environments (not just production),
stack traces removed from all error responses, internal paths stripped
from error details, per-route rate limits on uploads (60/min) and URL
fetches (200/hour).
Files: exclusive temp file creation (O_EXCL), disk space circuit
breaker, per-user storage quotas, settings payload 64KB size guard.
Python sidecar: script name allowlist in dispatcher, minimal environment
for subprocess spawns.
Dependencies: fixed 6 production CVEs (drizzle-orm, fastify, fast-uri,
@fastify/static, next, archiver/lodash). Pinned all GitHub Actions to
SHA hashes.
114 security tests added. Full OWASP Top 10 penetration test matrix
verified against production Docker container (30/30 pass after
hardening).
- Fix SSE write-after-end crash in progress.ts (remove callback before ending stream)
- Fix blob URL memory leaks: revoke processedPreviewUrl and old HEIC preview URLs
- Add AbortController to batch fetch in use-tool-processor and use-pipeline-processor
- Fix TGA format misidentified as CUR (extension overrides magic bytes)
- Add libheif-plugin-libde265 to Docker for HEIC/HEIF decode support
- Remove unused imports and state (AppLayout, setSampledColor, useEffect)
- Fix non-null assertions in meme-text-renderer and meme-generator
- Fix confusing void type in meme-templates
- Remove unnecessary useEffect deps in adjustments-panel
- Fix Playwright strict mode violations in 5 E2E tests
The enhance-faces tool requires MediaPipe for face detection, but the
upscale-enhance feature bundle did not include mediapipe in its pip
packages. Users who installed only the upscale-enhance bundle got
"Face detection requires MediaPipe" errors. Added mediapipe to both
amd64 and arm64 package lists, matching the pattern used by the
face-detection and photo-restoration bundles.
Also added feature-manifest.test.ts with 25 tests validating bundle
dependency completeness to prevent similar missing-dependency bugs.
Closes#129
Users can no longer customize the app name or logo. The branding API
endpoints, permission, frontend UI, env vars (APP_NAME, MAX_LOGO_SIZE_KB),
and all related tests are removed. Includes a migration to clean up
branding data from existing databases.
Pillow 12.x conflicts with pinned numpy 1.26.4, rembg, realesrgan,
and mediapipe. Revert to working 11.1.0 pins and ignore the CVEs
in pip-audit instead — they require a coordinated major version
upgrade across the entire ML stack (Pillow, numpy, torch, basicsr).
Ignored CVEs:
- CVE-2024-27763 (basicsr, no fix available)
- CVE-2026-40086 (rembg, fix needs Pillow 12)
- CVE-2026-25990 (Pillow, fix is 12.1.1)
- CVE-2026-40192 (Pillow, fix is 12.2.0)
- Increase QR generate max-size test timeout to 120s (10000x10000
PNG generation exceeds 30s default on CI runners)
- Update Pillow 11.1.0 → >=12.2.0 (CVE-2026-25990, CVE-2026-40192)
- Update rembg 2.0.62 → >=2.0.75 (CVE-2026-40086)
- Update opencv-python-headless to flexible range >=4.10,<4.12
- Ignore CVE-2024-27763 in pip-audit (basicsr transitive dep from
realesrgan, no fix available upstream)
- Align requirements-gpu.txt and Dockerfile with same versions
Add djxl (libjxl-tools) as primary JXL decoder with ImageMagick
fallback — fixes JXL format failures on Ubuntu where stock ImageMagick
lacks a JXL delegate. Also make Playwright Docker config respect
BASE_URL env var for testing against remote containers.
- Unit: 1,353 tests (42 files) — +256 new tests covering AI bridge
modules, image-engine sharpen/optimize-for-web, Zustand stores, and
icon-map validation
- Integration: 1,640 tests (57 files) — +826 new tests across all
tool routes, pipeline/progress/batch infrastructure, user-files,
edit-metadata, and a 321-test cross-format matrix
- E2E-Docker: 389 passing (20 spec files) — 6 new spec files for
batch processing, format conversion, layout, optimization,
watermark/overlay, and pipeline chains. Tests verified against fresh
Docker container with all 6 AI bundles installed.
Bug fixes discovered during testing:
- fix(compress): SVG/BMP/exotic formats crashed Sharp encoder — added
format-safety fallback to PNG
- fix(rate-limit): increase default login attempt limit from 10 to 500
per minute — previous value caused false test failures and is too
restrictive for a self-hosted app
- fix(auth.setup): wait for consent button visibility before clicking
to prevent flaky E2E-Docker auth setup
- Add Cloudflare Pages deployment for landing page (snapotter.com) and
docs (docs.snapotter.com)
- Create deploy-landing.yml and update deploy-docs.yml workflows
- Update CI to ignore apps/landing/** paths
- Fix logo transparency (remove white background) across all apps
- Recreate social-preview.png with SnapOtter branding
- Update all docs URLs from GitHub Pages to docs.snapotter.com
- Update VitePress config: light theme default, fix llms.txt paths
- Add .vitepress/cache/ and .env.* to gitignore
1. split batch 404: register split tool in batch registry via
registerToolProcessFn() so /api/v1/tools/split/batch works
2. CodeFormer crash: inference_app() expects a file path, not a numpy
array. Save to temp file before calling, read result back.
3. OCR fallback chain: fix case-sensitive "Segmentation fault" match
that prevented PaddleOCR crash from triggering Tesseract fallback.
Also add "process crashed" check. Upgrade ARM paddlepaddle to >=3.2.1.
4. blur-faces large images: downscale to 1920px max before MediaPipe
detection, scale coordinates back. Also add rotation retry for
portrait-oriented images where BlazeFace misses faces. Applied to
detect_faces.py, enhance_faces.py, and restore.py.
5. color-adjustments tool ID: fix mismatch in index.ts registration
array (was "color-adjustments", should be "adjust-colors").
Phase 1 — Docker Artifact Optimization:
- Replace broad `COPY . .` with targeted frontend source copies (API/Python
changes no longer bust the frontend build cache)
- Replace build-essential with gcc/g++ (leaner runtime)
- Fix LOG_LEVEL=debug → info for production
- Harden .dockerignore (exclude worktrees, IDE, CI, test artifacts)
Phase 2 — State & Persistence:
- Add PUID/PGID support in entrypoint.sh for bind mount compatibility
- Guard against PUID=0/PGID=0 to prevent accidental root execution
- Evict conflicting system users (e.g. node:1000) before UID remap
Phase 3 — Security:
- Always register @fastify/rate-limit so login brute-force protection
works even when global rate limit is disabled (RATE_LIMIT_PER_MIN=0)
- Add trustProxy support (TRUST_PROXY env var, default true) so rate
limiting and audit logs use real client IPs behind reverse proxies
- Strip stack traces from 500 error responses in production
- Fix FSTDEP022 deprecation: maxParamLength → routerOptions
- Add multi-file guard on single-file tool endpoint with clear error
message pointing to the /batch endpoint
Phase 4 — Graceful Degradation:
- Add consolidated hardware detection startup banner (GPU, rate limit,
upload limit, proxy status)
- Add ConnectionMonitor component with health polling and reconnecting
overlay that auto-dismisses when the server comes back
Phase 5 — Deployment Docs:
- Rewrite deployment.md with copy-paste CPU and GPU compose templates
- Add hardware requirements table (minimum, recommended, heavy workloads)
- Add PUID/PGID bind mount documentation
- Add complete env var reference table
- Add reverse proxy guides for Nginx, Nginx Proxy Manager, Traefik,
and Cloudflare Tunnels
Extends the platform to handle 7 new image format families alongside
the existing AVIF support gap-fill. Uses the established HEIC decoder
pattern (CLI decode → PNG → Sharp) for formats Sharp can't handle
natively: Camera RAW via dcraw_emu/LibRaw, PSD/TGA/EXR/HDR via
ImageMagick. JXL and ICO are Sharp-native. Adds server-side preview
for non-browser-displayable formats and JXL as a new convert output
target. All 27 validateImageBuffer callers updated with filename for
extension-based format detection.
- Add safe_onnx_session() to gpu.py with graceful CUDA EP → CPU fallback
- Replace bare ort.InferenceSession() calls across colorize, restore, inpaint, remove_bg
- Add libcublas-12-6 to production Dockerfile for ONNX Runtime CUDA EP
- Add skipIfFeatureNotInstalled guards to remove-bg, blur-faces, smart-crop, ocr, noise-removal e2e specs
- Add AI tool install prompt detection in tools-all.spec.ts
- Add smart-crop to PYTHON_SIDECAR_TOOLS so frontend shows install prompt correctly
- Create Dockerfile.test.dockerignore to include tests/ in test image builds
- Add libheif-examples and exiftool to Dockerfile.test for HEIC and metadata tests
- Regenerate visual regression baselines for Docker/Linux and skip on non-Docker platforms
- Add 8 new E2E specs for AI tools (upscale, enhance-faces, colorize,
restore-photo, erase-object, smart-crop, passport-photo, red-eye-removal)
closing all HIGH/MEDIUM coverage gaps from the test matrix audit
- Fix ensureAiDirs() crash on non-Docker environments by gating on
isDockerEnvironment() — prevents ENOENT when /data doesn't exist
- Bump torch 2.6.0→2.7.0 and torchvision 0.21.0→0.22.0 in feature
manifest for broader Python version compatibility
- Add Python 3.14 version guard warning in install_feature.py
- Remove duplicate torchvision shims from upscale.py and enhance_faces.py
(dispatcher.py already handles this at startup)
- Remove orphaned tools.batch i18n key and dead pipeline-builder filter
- Regenerate 4 visual regression baselines for current UI state
- Add data-testid to passport-photo generate button for E2E testability
- Pin torch==2.6.0+cu126 and torchvision==0.21.0+cu126 in feature
manifest to prevent NCCL symbol mismatch on CUDA 12.6 base images
- Move lpips after torch in install order to prevent wrong version
resolution from PyPI
- Add einops to upscale-enhance common deps (required by SCUNet)
- Update cpu_fallback_packages to handle multi-package CUDA torch
entries on amd64 without GPU
- Fix gpu.py ONNX CUDA detection: replace hardcoded .so path with
cross-platform session smoke-test
- Fix os.dup(1) crashes on Windows in upscale, enhance_faces, and
noise_removal by wrapping in try/except with sys.stderr fallback
- Guard top-level numpy/cv2 imports in colorize.py and restore.py
with helpful error messages
- Add weights_only=False fallback for torch.load in noise_removal
- Fix integration tests to accept 501 for uninstalled AI features
and 422 for missing system tools (exiftool, libheif)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Fix SKIP_MUST_CHANGE_PASSWORD not affecting login/session API responses,
causing frontend redirect even when the env var was set after user creation
- Increase Docker Playwright timeouts (test: 600s, expect: 60s, AI processing: 300s)
to support CPU-only self-hosted environments
- Increase default rate limit from 100 to 50000 req/min for self-hosted deployments
- Fix OCR tests: use filechooser pattern (Dropzone has no static file input),
correct enhance checkbox default, rewrite for actual fixture behavior
- Fix remove-bg tests: update quality labels (Balanced→HD, Best→Max)
- Fix noise-removal skip guard: use waitFor() instead of instant isVisible()
- Fix automate pipeline save test: clean up stale E2E pipelines before assertion
- Add tool-specific suffix to output filenames so downloads don't overwrite originals (batch & single-tool routes)
- Skip deleting shared models when uninstalling a bundle that shares models with another installed bundle
- Auto-detect NVIDIA GPU and swap GPU-only pip packages (onnxruntime-gpu, paddlepaddle-gpu) for CPU equivalents
- Refactor docker-compose with YAML anchors and explicit cpu/gpu profiles
- Add libheif-plugin-x265 to Dockerfile
- Fix install-all queue logic to handle concurrent individual installs and clear stale errors
- Unify playwright docker config to use same test dir with API_URL env var
- Fix flaky e2e selectors, rename Strip Metadata → Remove Metadata, handle collage custom dropzone, improve fallback test image generation
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Replace FeatureInstallPrompt's local SSE/polling with useFeaturesStore
so install progress, errors, and recovery are handled globally — works
across navigation, logout/login, and partial downloads. Shows fun
progress messages and ETA matching the settings page.
Add compose healthcheck and optional GPU profile (--profile gpu).
Remove all ML pip installs (onnxruntime, rembg, realesrgan, paddlepaddle,
mediapipe, codeformer), model downloads, and post-install fixups from the
Dockerfile. The base image now ships only Node.js + Sharp + Python with
numpy/Pillow/opencv. AI features are installed on-demand at runtime via
the feature manifest and install_feature.py script.
Key changes:
- Remove SKIP_MODEL_DOWNLOADS build arg (no longer needed)
- Remove apt-get purge of build-essential (needed for runtime pip installs)
- Remove PaddleX symlinks and facexlib weight directory setup
- Add COPY of feature-manifest.json and install_feature.py
- Update PYTHON_VENV_PATH to /data/ai/venv, add MODELS_PATH and DATA_DIR
- Entrypoint bootstraps AI venv from /opt/venv on first container start
with crash-safe temp directory pattern
Authoritative JSON manifest defining all 6 AI feature bundles with:
- Exact pip package versions and platform-specific variants (amd64/arm64)
- pip flags (--no-deps for codeformer, --extra-index-url for torch/paddle)
- postInstall re-pins (numpy==1.26.4 after codeformer)
- Model download entries (direct URL, rembg sessions, HuggingFace snapshots)
- Bundle-to-tool mapping matching shared/features.ts
Used by install_feature.py at runtime to install bundles on demand.
- Added model mismatch warnings in colorize, enhance-faces, and upscale routes.
- Improved error handling in colorize, enhance_faces, remove_bg, restore, and upscale scripts with detailed logging.
- Updated Dockerfile to align NCCL versions for compatibility.
- Introduced a new full tool audit script to test all tools for functionality and GPU usage.
- Created Playwright E2E tests for GPU-dependent tools to ensure proper functionality and performance.
Ubuntu mirrors (security.ubuntu.com) are frequently unreachable from
GitHub Actions runners, causing all amd64 Docker builds to fail.
Instead of installing Node.js via NodeSource apt repo (which requires
working Ubuntu mirrors for the initial apt-get update), copy the Node
binary and modules directly from the official node:22-bookworm image.
Also add retry with backoff to the system deps apt-get update.
Ubuntu security mirrors can be unreachable from GitHub Actions runners.
Add a retry loop with exponential backoff (15s, 30s, 45s) around
apt-get update in the Node.js install step for the CUDA base image.
