CVE-2023-34454

Advisory

Catboost 1.2.1 updates its dependency 'snappy-java' to version '1.1.10.1' to include a fix for an Integer Overflow vulnerability.
https://github.com/catboost/catboost/commit/b51a3b2302a1d6b1a596b406efef347c872d9a0e

Affected package

Affected versions

Fixed versions

Vulnerability changelog

New features
* Allow to optimize specific ranking loss functions with YetiRank and YetiRankPairwise by specifying `mode` parameter. See [Which Tricks are Important for Learning to Rank?](https://arxiv.org/abs/2204.01500) paper for details (this family of losses is called `YetiLoss` there). CPU-only for now.
* Add Kernel Gradient Boosting support (use `catboost.sample_gaussian_process` function). 2408, thanks to TakeOver. See [Gradient Boosting Performs Gaussian Process Inference](https://arxiv.org/abs/2206.05608) paper for details.
* LambdaMart loss: support new target metrics MRR, ERR and MAP.
* StochasticRank loss: support new target metrics ERR and MRR.
* Support MultiRMSE on GPU. 2264, 2390
* Load JSON model format in Java Client. 1627, thanks to timotta
* Implement exporting of Multiclass models to C++ and Python. 2284, thanks to antoninkriz

Improvements
* Speedup BM25 feature calcers 3x
* Use `int` instead of deprecated `numpy.int`. 2378
* Add `ModelCalcerWrapper::CalcFlatTransposed`, 2413 thanks to faucct
* Update dependencies to avoid known vulnerabilities

Bugfixes
* Fix __shfl_up_sync mask. 2339
* TFocalMetric negative values fix. 2386, thanks to diditforlulz273
* Focal loss: Use user-defined alpha and gamma
* Fix exception propagation: Rethrow exceptions caused by user's python code as C++ exceptions
* CatBoost trained with user defined objective was incompatible with ShapValues calculation
* Avoid nan's in Newton step calculation for RMSEWithUncertainty
* Fix score method for y with shape (N, 1). 2405
* Fix scalePosWeight support for Spark. 2470

Resources

• https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2023-34454