f08aef1804
* Rebuild Studio branch on top of main * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix security and code quality issues for Studio PR #4237 - Validate models_dir query param against allowed directory roots to prevent path traversal in /api/models/local endpoint - Replace string startswith() with Path.is_relative_to() for frontend path traversal check in serve_frontend - Sanitize SSE error messages to not leak exception details to clients (4 locations in inference.py) - Bind port-discovery socket to 127.0.0.1 instead of all interfaces in llama_cpp backend - Import datasets_root and resolve_output_dir in embedding training function to fix NameError and use managed output directory - Remove stale .gitignore entries for package-lock.json and test directories so tests can be tracked in version control - Add venv-reexecution logic to ui CLI command matching the studio command behavior * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Move models_dir path validation before try/except block The HTTPException(403) was inside the try/except Exception handler, so it would be caught and re-raised as a 500. Moving the validation before the try block ensures the 403 is returned directly and also makes the control flow clearer for static analysis (path is validated before any filesystem operations). * Use os.path.realpath + startswith for models_dir validation CodeQL py/path-injection does not recognize Path.is_relative_to() as a sanitizer. Switched to os.path.realpath + str.startswith which is a recognized sanitizer pattern in CodeQL's taint analysis. The startswith check uses root_str + os.sep to prevent prefix collisions (e.g. /app/models_evil matching /app/models). * Never pass user input to Path constructor in models_dir validation CodeQL traces taint through Path(resolved) even after a startswith barrier guard. Fix: the user-supplied models_dir is only used as a string for comparison against allowed roots. The Path object passed to _scan_models_dir comes from the trusted allowed_roots list, not from user input. This fully breaks the taint chain. --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
67 lines
2.4 KiB
Python
67 lines
2.4 KiB
Python
# SPDX-License-Identifier: AGPL-3.0-only
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# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
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"""
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Pydantic response schemas for endpoints that previously returned raw dicts.
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These are small response models for training and model management routes.
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"""
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from pydantic import BaseModel, Field
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from typing import Optional, List
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# --- Training route response models ---
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class TrainingStopResponse(BaseModel):
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"""Response for stopping a training job"""
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status: str = Field(..., description = "Current status: 'stopped' or 'idle'")
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message: str = Field(..., description = "Human-readable status message")
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class TrainingMetricsResponse(BaseModel):
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"""Response for training metrics history"""
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loss_history: List[float] = Field(
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default_factory = list, description = "Loss values per step"
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)
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lr_history: List[float] = Field(
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default_factory = list, description = "Learning rate per step"
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)
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step_history: List[int] = Field(default_factory = list, description = "Step numbers")
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grad_norm_history: List[float] = Field(
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default_factory = list, description = "Gradient norm values"
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)
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grad_norm_step_history: List[int] = Field(
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default_factory = list, description = "Step numbers for gradient norm values"
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)
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current_loss: Optional[float] = Field(None, description = "Most recent loss value")
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current_lr: Optional[float] = Field(None, description = "Most recent learning rate")
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current_step: Optional[int] = Field(None, description = "Most recent step number")
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# --- Model management route response models ---
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class LoRABaseModelResponse(BaseModel):
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"""Response for getting a LoRA's base model"""
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lora_path: str = Field(..., description = "Path to the LoRA adapter")
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base_model: str = Field(..., description = "Base model identifier")
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class VisionCheckResponse(BaseModel):
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"""Response for checking if a model is a vision model"""
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model_name: str = Field(..., description = "Model identifier")
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is_vision: bool = Field(..., description = "Whether the model is a vision model")
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class EmbeddingCheckResponse(BaseModel):
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"""Response for checking if a model is an embedding model"""
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model_name: str = Field(..., description = "Model identifier")
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is_embedding: bool = Field(
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..., description = "Whether the model is an embedding/sentence-transformer model"
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)
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