v0.5.0
feyninc/catsuv0.5.0Oct 20, 2026by github-actions[bot]
Upgrading?
Key Highlights
- Python 3.11-3.14 support with async scanning as the default runtime
- Rust native backend for GET scans and wordlist generation
- Category-based wordlists (PHP, Python, Java, etc.) and template support
- MySQL and PostgreSQL report formats
- Security hardening including safe XML handling and supply-chain pinning
Breaking Changes
- Legacy pickle sessions are no longer loaded directly
- Old dependency installer path was removed
- Optional DB dependencies moved to explicit extras instead of mandatory runtime dependencies
New Features
- Python API with DirsearchFuzzer and structured results
- JSON controller state for resumable sessions
- Advanced match/filter controls for status, size, regex, and time
- Wildcard and duplicate-result filtering improvements
- Template wordlist support with placeholders
Full Release Notes
# 0.5.0 — upstream v0.4.12 migration This release aligns applicable host APIs with NandhaKishorM/laya `ffc142012af63d015fa19f05f29f9c5ff329a2cc`: batch and long-text inference, structured decisions, hooks, calibration, evaluation, Router, HTTP/MCP, and framework/tool integrations. Native execution and explicit unsupported backend boundaries are preserved. Compatibility changes include upstream Router defaults, validation and output semantics. The LlamaIndex adapter supports the actual current BaseSelector and SingleSelection contracts. See MIGRATION_0_4.md for behavior and dependency details. The 341-entry API audit and three-checkpoint FP32/FP16 numerical/stability campaign passed. The qualification report records unchanged applicable upstream assertions, real SDK HTTP checks, real optional-framework checks and clean-wheel inference. Package 0.5.0 changes version metadata relative to the recorded 0.4.0 development qualification builds; release gates verify the final distribution separately. Performance comparisons use upstream PyTorch 2.14, matched FP32 and fixed inputs. MLX wins all 18 P50 comparisons in the final matrix. Core ML's alternating-order confirmation wins all 18 P50/P95/mean-throughput comparisons; the initial four sequential regressions are retained in the report. Core ML adaptive graph and shape caches trade more resident memory for lower warm-call latency. Cold calls, other hardware and arbitrary workloads are outside these finite guarantees. See QUALIFICATION_0_4_12.md and UPSTREAM_0412_PROGRESS.json for raw evidence and limitations. Original historical benchmark and package receipts remain unchanged. MLX preserves native precision, compilation, row batch limits and bounded question-prefix caching. Local-only model resolution is supported by Agent and Router; ordinary inference needs no PyTorch or Transformers. Publication verified on 2026-10-20: GitHub Release and PyPI wheel/sdist hashes match, public wheel source matches the tag, and a public-index reinstall passes all three offline real-model checks. See `benchmarks/results/release-v050.json`. Post-release CI runs exposed intermittent failures in the unchanged upstream idle-window timing test. Running host contracts before native model tests in a separate process passed one complete CI, but a subsequent run reproduced the timing failure; isolation has not eliminated it. No runtime, assertion or timeout was changed. Failure diagnostics also run the original upstream idle-window tests for comparison. [CI evidence](https://github.com/mizorewww/laya-mlx/actions/runs/39354961828). The original pinned upstream suite reproduced the same idle-window failure on the same CI runner; all 254 native/integration tests passed. This timing instability remains documented in [CI evidence](https://github.com/mizorewww/laya-mlx/blob/main/docs/CI_TIMING.md).
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