diff --git a/.gitignore b/.gitignore
index 7dc6c890..4d27ec8c 100644
--- a/.gitignore
+++ b/.gitignore
@@ -122,3 +122,7 @@ node_modules
 
 exports
 trash
+
+# Claude
+CLAUDE.md
+.claude/
diff --git a/docs/changes.rst b/docs/changes.rst
index e8daaf65..fc9751b9 100644
--- a/docs/changes.rst
+++ b/docs/changes.rst
@@ -11,6 +11,9 @@ skops Changelog
 
 v0.14
 -----
+- Trust internal scikit-learn types needed by GradientBoosting and
+  HistGradientBoosting models, so they no longer surface as untrusted types.
+  :pr:`513` by :user:`cakedev0 <cakedev0>` and `Adrin Jalali`_.
 
 v0.13
 -----
diff --git a/pixi.lock b/pixi.lock
index a86da1a1..ae203402 100644
--- a/pixi.lock
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-- conda: https://conda.anaconda.org/conda-forge/noarch/packaging-26.0-pyhcf101f3_0.conda
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-  timestamp: 1769093650580
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 - conda: https://conda.anaconda.org/conda-forge/osx-64/pandas-2.0.3-py310h5e4fcda_1.conda
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 - conda: https://conda.anaconda.org/conda-forge/linux-64/pillow-12.2.0-py311hf88fc01_0.conda
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 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pillow-12.2.0-py313h45e5a15_0.conda
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 - conda: https://conda.anaconda.org/conda-forge/win-64/pillow-12.2.0-py312h31f0997_0.conda
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-  - libopengl >=1.7.0,<2.0a0
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   license: LGPL-3.0-only
   license_family: LGPL
   purls:
-  - pkg:pypi/pyside6?source=compressed-mapping
-  size: 11066229
-  timestamp: 1775055198084
+  - pkg:pypi/pyside6?source=hash-mapping
+  - pkg:pypi/shiboken6?source=hash-mapping
+  size: 11209595
+  timestamp: 1776276413741
 - conda: https://conda.anaconda.org/conda-forge/win-64/pyside6-6.9.1-py39h5f4aa1c_0.conda
   sha256: ffd82f56464c75301ec5e5f2f1c743795ab95f52c4ac75960bf92b8588767e9a
   md5: 85d380da5c107a27506c80b740981c48
@@ -20550,7 +20550,7 @@ packages:
   license: MIT
   license_family: MIT
   purls:
-  - pkg:pypi/pyyaml?source=compressed-mapping
+  - pkg:pypi/pyyaml?source=hash-mapping
   size: 202391
   timestamp: 1770223462836
 - conda: https://conda.anaconda.org/conda-forge/osx-64/pyyaml-6.0.2-py39hd18e689_2.conda
@@ -20865,9 +20865,9 @@ packages:
   purls: []
   size: 1377020
   timestamp: 1720814433486
-- conda: https://conda.anaconda.org/conda-forge/linux-64/qt6-main-6.11.0-pl5321h16c4a6b_2.conda
-  sha256: e83dabfeb6209c863a1555edad81b12b08a953a0d952359bf3f0be3250ab12ce
-  md5: c6ba2de6b22dedf2f20eba3bde1dbe8e
+- conda: https://conda.anaconda.org/conda-forge/linux-64/qt6-main-6.11.0-pl5321h16c4a6b_4.conda
+  sha256: d2cb212a4abd66c13df44771c22ee23c0b013ba1d5dbb5e10e8a13e261a47c6b
+  md5: c81127acb50fdc7760682495fc9ab088
   depends:
   - libxcb
   - xcb-util
@@ -20876,69 +20876,67 @@ packages:
   - xcb-util-image
   - xcb-util-renderutil
   - xcb-util-cursor
+  - libgl-devel
+  - libegl-devel
   - libgcc >=14
   - __glibc >=2.17,<3.0.a0
   - libstdcxx >=14
-  - libllvm21 >=21.1.8,<21.2.0a0
-  - zstd >=1.5.7,<1.6.0a0
-  - libwebp-base >=1.6.0,<2.0a0
-  - xorg-libxcomposite >=0.4.7,<1.0a0
-  - double-conversion >=3.4.0,<3.5.0a0
-  - pcre2 >=10.47,<10.48.0a0
-  - xorg-libxrandr >=1.5.5,<2.0a0
-  - libxkbcommon >=1.13.1,<2.0a0
   - libvulkan-loader >=1.4.341.0,<2.0a0
-  - libzlib >=1.3.2,<2.0a0
-  - libclang-cpp21.1 >=21.1.8,<21.2.0a0
-  - xcb-util-keysyms >=0.4.1,<0.5.0a0
-  - xcb-util-wm >=0.4.2,<0.5.0a0
-  - xorg-libxxf86vm >=1.1.7,<2.0a0
-  - icu >=78.3,<79.0a0
-  - libxml2
-  - libxml2-16 >=2.14.6
-  - xcb-util-image >=0.4.0,<0.5.0a0
-  - xcb-util-renderutil >=0.3.10,<0.4.0a0
-  - libglib >=2.86.4,<3.0a0
-  - libcups >=2.3.3,<2.4.0a0
-  - libpq >=18.3,<19.0a0
-  - xorg-libice >=1.1.2,<2.0a0
+  - libwebp-base >=1.6.0,<2.0a0
   - libgl >=1.7.0,<2.0a0
-  - harfbuzz >=13.2.1
-  - xorg-libxdamage >=1.1.6,<2.0a0
-  - xorg-libsm >=1.2.6,<2.0a0
-  - xorg-libxcursor >=1.2.3,<2.0a0
-  - krb5 >=1.22.2,<1.23.0a0
+  - libegl >=1.7.0,<2.0a0
+  - openssl >=3.5.6,<4.0a0
   - dbus >=1.16.2,<2.0a0
-  - libdrm >=2.4.125,<2.5.0a0
-  - libjpeg-turbo >=3.1.2,<4.0a0
+  - libxkbcommon >=1.13.1,<2.0a0
+  - pcre2 >=10.47,<10.48.0a0
+  - krb5 >=1.22.2,<1.23.0a0
   - fontconfig >=2.17.1,<3.0a0
   - fonts-conda-ecosystem
-  - xcb-util-cursor >=0.1.6,<0.2.0a0
-  - alsa-lib >=1.2.15.3,<1.3.0a0
   - libxcb >=1.17.0,<2.0a0
-  - openssl >=3.5.5,<4.0a0
-  - libegl >=1.7.0,<2.0a0
-  - libpng >=1.6.56,<1.7.0a0
+  - zstd >=1.5.7,<1.6.0a0
+  - xcb-util >=0.4.1,<0.5.0a0
+  - xorg-libxcomposite >=0.4.7,<1.0a0
+  - xorg-libxxf86vm >=1.1.7,<2.0a0
+  - icu >=78.3,<79.0a0
+  - xorg-libxdamage >=1.1.6,<2.0a0
+  - xcb-util-renderutil >=0.3.10,<0.4.0a0
+  - xcb-util-image >=0.4.0,<0.5.0a0
   - wayland >=1.25.0,<2.0a0
-  - libbrotlicommon >=1.2.0,<1.3.0a0
-  - libbrotlienc >=1.2.0,<1.3.0a0
-  - libbrotlidec >=1.2.0,<1.3.0a0
+  - libzlib >=1.3.2,<2.0a0
+  - xcb-util-keysyms >=0.4.1,<0.5.0a0
+  - xorg-libxcursor >=1.2.3,<2.0a0
+  - double-conversion >=3.4.0,<3.5.0a0
+  - alsa-lib >=1.2.15.3,<1.3.0a0
+  - xorg-libxext >=1.3.7,<2.0a0
+  - harfbuzz >=14.1.0
+  - libsqlite >=3.53.0,<4.0a0
   - libfreetype >=2.14.3
   - libfreetype6 >=2.14.3
-  - xorg-libxext >=1.3.7,<2.0a0
-  - xcb-util >=0.4.1,<0.5.0a0
+  - libtiff >=4.7.1,<4.8.0a0
+  - libdrm >=2.4.125,<2.5.0a0
+  - libjpeg-turbo >=3.1.4.1,<4.0a0
+  - xcb-util-wm >=0.4.2,<0.5.0a0
+  - libcups >=2.3.3,<2.4.0a0
   - xorg-libx11 >=1.8.13,<2.0a0
+  - libpng >=1.6.58,<1.7.0a0
   - xorg-libxtst >=1.2.5,<2.0a0
-  - libclang13 >=21.1.8
-  - libsqlite >=3.52.0,<4.0a0
-  - libtiff >=4.7.1,<4.8.0a0
+  - xorg-libice >=1.1.2,<2.0a0
+  - xorg-libxrandr >=1.5.5,<2.0a0
+  - libbrotlicommon >=1.2.0,<1.3.0a0
+  - libbrotlienc >=1.2.0,<1.3.0a0
+  - libbrotlidec >=1.2.0,<1.3.0a0
+  - xcb-util-cursor >=0.1.6,<0.2.0a0
+  - libxml2
+  - libxml2-16 >=2.14.6
+  - xorg-libsm >=1.2.6,<2.0a0
+  - libpq >=18.3,<19.0a0
+  - libglib >=2.86.4,<3.0a0
   constrains:
   - qt ==6.11.0
   license: LGPL-3.0-only
-  license_family: LGPL
   purls: []
-  size: 60993086
-  timestamp: 1774634904948
+  size: 59928585
+  timestamp: 1776322501700
 - conda: https://conda.anaconda.org/conda-forge/linux-64/qt6-main-6.9.1-h6ac528c_2.conda
   sha256: 8795462e675b7235ad3e01ff3367722a37915c7084d0fb897b328b7e28a358eb
   md5: 34ccdb55340a25761efbac1ff1504091
@@ -21001,40 +20999,40 @@ packages:
   purls: []
   size: 53080009
   timestamp: 1753420196625
-- conda: https://conda.anaconda.org/conda-forge/win-64/qt6-main-6.11.0-pl5321hfcac499_3.conda
-  sha256: 206ba63d0c0c0f69f8d0ed612190b05f42207e5f84a80d1fd4a5a2607b0a8728
-  md5: b53193a38f677045dae28f7039122035
+- conda: https://conda.anaconda.org/conda-forge/win-64/qt6-main-6.11.0-pl5321hfcac499_4.conda
+  sha256: c7b7e8f40e393837de5f7f50b8da0ba2d42df64069d1d587b3761fa5e2f7d018
+  md5: 1aca2896ea9f0d1f0761a7b278f670a0
   depends:
   - vc >=14.3,<15
   - vc14_runtime >=14.44.35208
   - ucrt >=10.0.20348.0
-  - libwebp-base >=1.6.0,<2.0a0
-  - harfbuzz >=13.2.1
-  - libpng >=1.6.56,<1.7.0a0
-  - libglib >=2.86.4,<3.0a0
-  - libjpeg-turbo >=3.1.2,<4.0a0
+  - libvulkan-loader >=1.4.341.0,<2.0a0
+  - krb5 >=1.22.2,<1.23.0a0
+  - libsqlite >=3.53.0,<4.0a0
   - libbrotlicommon >=1.2.0,<1.3.0a0
   - libbrotlienc >=1.2.0,<1.3.0a0
   - libbrotlidec >=1.2.0,<1.3.0a0
-  - krb5 >=1.22.2,<1.23.0a0
-  - openssl >=3.5.5,<4.0a0
-  - libsqlite >=3.52.0,<4.0a0
-  - libzlib >=1.3.2,<2.0a0
-  - double-conversion >=3.4.0,<3.5.0a0
-  - libvulkan-loader >=1.4.341.0,<2.0a0
+  - libglib >=2.86.4,<3.0a0
+  - pcre2 >=10.47,<10.48.0a0
+  - libpng >=1.6.57,<1.7.0a0
   - icu >=78.3,<79.0a0
+  - openssl >=3.5.6,<4.0a0
   - zstd >=1.5.7,<1.6.0a0
-  - pcre2 >=10.47,<10.48.0a0
+  - libjpeg-turbo >=3.1.4.1,<4.0a0
+  - double-conversion >=3.4.0,<3.5.0a0
+  - libtiff >=4.7.1,<4.8.0a0
+  - libzlib >=1.3.2,<2.0a0
   - libfreetype >=2.14.3
   - libfreetype6 >=2.14.3
-  - libtiff >=4.7.1,<4.8.0a0
+  - harfbuzz >=14.1.0
+  - libwebp-base >=1.6.0,<2.0a0
   constrains:
   - qt ==6.11.0
   license: LGPL-3.0-only
   license_family: LGPL
   purls: []
-  size: 89538848
-  timestamp: 1774714575383
+  size: 89539622
+  timestamp: 1776298520993
 - conda: https://conda.anaconda.org/conda-forge/win-64/qt6-main-6.9.1-h02ddd7d_2.conda
   sha256: 0093da5504b42a3b26ec10cdedc07d91e83225775590f596407b9de769ca4a5f
   md5: 3cbddb0b12c72aa3b974a4d12af51f29
@@ -21383,6 +21381,7 @@ packages:
   - typing_extensions >=4.0.0,<5.0.0
   - python
   license: MIT
+  license_family: MIT
   purls:
   - pkg:pypi/rich?source=hash-mapping
   size: 208577
@@ -23474,7 +23473,7 @@ packages:
   license: MIT
   license_family: MIT
   purls:
-  - pkg:pypi/setuptools?source=compressed-mapping
+  - pkg:pypi/setuptools?source=hash-mapping
   size: 639697
   timestamp: 1773074868565
 - conda: https://conda.anaconda.org/conda-forge/noarch/six-1.17.0-pyhe01879c_1.conda
@@ -23492,7 +23491,7 @@ packages:
 - pypi: ./
   name: skops
   version: 0.14.dev0
-  sha256: c7af1710e6fc5dcfab436d88167f56d249a1e67bead44a799e6bf44200f85c98
+  sha256: 3bdc721e7411a4dbcb71fb5e8fbbfe44e7016362a512b78718a8442f61cd67bd
   requires_dist:
   - numpy>=1.25.0
   - packaging>=17.0
@@ -23903,7 +23902,7 @@ packages:
   license: Apache-2.0
   license_family: Apache
   purls:
-  - pkg:pypi/tornado?source=compressed-mapping
+  - pkg:pypi/tornado?source=hash-mapping
   size: 882996
   timestamp: 1774358035145
 - conda: https://conda.anaconda.org/conda-forge/linux-64/tornado-6.5.5-py314h5bd0f2a_0.conda
@@ -23917,7 +23916,7 @@ packages:
   license: Apache-2.0
   license_family: Apache
   purls:
-  - pkg:pypi/tornado?source=compressed-mapping
+  - pkg:pypi/tornado?source=hash-mapping
   size: 912476
   timestamp: 1774358032579
 - conda: https://conda.anaconda.org/conda-forge/osx-64/tornado-6.5.2-py39hb1cfd32_0.conda
@@ -23982,7 +23981,7 @@ packages:
   license: Apache-2.0
   license_family: Apache
   purls:
-  - pkg:pypi/tornado?source=compressed-mapping
+  - pkg:pypi/tornado?source=hash-mapping
   size: 882579
   timestamp: 1774358602446
 - conda: https://conda.anaconda.org/conda-forge/osx-64/tornado-6.5.5-py314h217eccc_0.conda
@@ -24023,7 +24022,7 @@ packages:
   license: Apache-2.0
   license_family: Apache
   purls:
-  - pkg:pypi/tornado?source=compressed-mapping
+  - pkg:pypi/tornado?source=hash-mapping
   size: 666858
   timestamp: 1774358813446
 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/tornado-6.5.5-py311hc949640_0.conda
@@ -24203,7 +24202,7 @@ packages:
   - python
   license: Apache-2.0 AND MIT
   purls:
-  - pkg:pypi/types-requests?source=compressed-mapping
+  - pkg:pypi/types-requests?source=hash-mapping
   size: 28039
   timestamp: 1775637500210
 - conda: https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.14.1-pyhe01879c_0.conda
@@ -24987,9 +24986,9 @@ packages:
   - pkg:pypi/virtualenv?source=hash-mapping
   size: 4381624
   timestamp: 1755111905876
-- conda: https://conda.anaconda.org/conda-forge/noarch/virtualenv-21.2.1-pyhcf101f3_0.conda
-  sha256: a3b38bb79ebbb830574b6e0ba1303f103601b5ed658ac400a3f9e43806e8e4fe
-  md5: fa76df129efc4550f272d8668acbe658
+- conda: https://conda.anaconda.org/conda-forge/noarch/virtualenv-21.2.4-pyhcf101f3_0.conda
+  sha256: 9a07c52fd7fc0d187c53b527e54ea57d4f46302946fee2f9291d035f4f8984f9
+  md5: 15be1b64e7a4501abb4f740c28ceadaf
   depends:
   - python >=3.10
   - distlib >=0.3.7,<1
@@ -25003,8 +25002,8 @@ packages:
   license_family: MIT
   purls:
   - pkg:pypi/virtualenv?source=compressed-mapping
-  size: 4658762
-  timestamp: 1775771531130
+  size: 4659433
+  timestamp: 1776247061232
 - conda: https://conda.anaconda.org/conda-forge/win-64/vs2015_runtime-14.44.35208-h38c0c73_34.conda
   sha256: 63ff4ec6e5833f768d402f5e95e03497ce211ded5b6f492e660e2bfc726ad24d
   md5: f276d1de4553e8fca1dfb6988551ebb4
@@ -25148,7 +25147,7 @@ packages:
   sha256: 8bbfe4fedc151b83a52edbf0de945fd94358b09a81998f2945ad330fd5f20cd6
   requires_dist:
   - numpy
-  - nvidia-nccl-cu12 ; platform_machine != 'aarch64' and platform_system == 'Linux'
+  - nvidia-nccl-cu12 ; platform_machine != 'aarch64' and sys_platform == 'linux'
   - scipy
   - dask ; extra == 'dask'
   - distributed ; extra == 'dask'
@@ -25168,7 +25167,7 @@ packages:
   sha256: 78d88da184562deff25c820d943420342014dd55e0f4c017cc4563c2148df5ee
   requires_dist:
   - numpy
-  - nvidia-nccl-cu12 ; platform_machine != 'aarch64' and platform_system == 'Linux'
+  - nvidia-nccl-cu12 ; platform_machine != 'aarch64' and sys_platform == 'linux'
   - scipy
   - dask ; extra == 'dask'
   - distributed ; extra == 'dask'
@@ -25188,7 +25187,7 @@ packages:
   sha256: 8df6da72963969ab2bf49a520c3e147b1e15cbeddd3aa0e3e039b3532c739339
   requires_dist:
   - numpy
-  - nvidia-nccl-cu12 ; platform_machine != 'aarch64' and platform_system == 'Linux'
+  - nvidia-nccl-cu12 ; platform_machine != 'aarch64' and sys_platform == 'linux'
   - scipy
   - dask ; extra == 'dask'
   - distributed ; extra == 'dask'
@@ -25208,7 +25207,7 @@ packages:
   sha256: 523db01d4e74b05c61a985028bde88a4dd380eadc97209310621996d7d5d14a7
   requires_dist:
   - numpy
-  - nvidia-nccl-cu12 ; platform_machine != 'aarch64' and platform_system == 'Linux'
+  - nvidia-nccl-cu12 ; platform_machine != 'aarch64' and sys_platform == 'linux'
   - scipy
   - dask ; extra == 'dask'
   - distributed ; extra == 'dask'
@@ -25228,7 +25227,7 @@ packages:
   sha256: 0d169736fd836fc13646c7ab787167b3a8110351c2c6bc770c755ee1618f0442
   requires_dist:
   - numpy
-  - nvidia-nccl-cu12 ; platform_system == 'Linux'
+  - nvidia-nccl-cu12 ; sys_platform == 'linux'
   - scipy
   - dask ; extra == 'dask'
   - distributed ; extra == 'dask'
@@ -25247,7 +25246,7 @@ packages:
   sha256: 2f661966d3e322536d9c448090a870fcba1e32ee5760c10b7c46bac7a342079a
   requires_dist:
   - numpy
-  - nvidia-nccl-cu12 ; platform_system == 'Linux'
+  - nvidia-nccl-cu12 ; sys_platform == 'linux'
   - scipy
   - dask ; extra == 'dask'
   - distributed ; extra == 'dask'
@@ -25266,7 +25265,7 @@ packages:
   sha256: 99b4a6bbcb47212fec5cf5fbe12347215f073c08967431b0122cfbd1ee70312c
   requires_dist:
   - numpy
-  - nvidia-nccl-cu12 ; platform_system == 'Linux'
+  - nvidia-nccl-cu12 ; sys_platform == 'linux'
   - scipy
   - dask ; extra == 'dask'
   - distributed ; extra == 'dask'
@@ -25285,7 +25284,7 @@ packages:
   sha256: eabbd40d474b8dbf6cb3536325f9150b9e6f0db32d18de9914fb3227d0bef5b7
   requires_dist:
   - numpy
-  - nvidia-nccl-cu12 ; platform_system == 'Linux'
+  - nvidia-nccl-cu12 ; sys_platform == 'linux'
   - scipy
   - dask ; extra == 'dask'
   - distributed ; extra == 'dask'
@@ -25851,18 +25850,6 @@ packages:
   purls: []
   size: 63944
   timestamp: 1753484092156
-- conda: https://conda.anaconda.org/conda-forge/noarch/zipp-3.23.0-pyhcf101f3_1.conda
-  sha256: b4533f7d9efc976511a73ef7d4a2473406d7f4c750884be8e8620b0ce70f4dae
-  md5: 30cd29cb87d819caead4d55184c1d115
-  depends:
-  - python >=3.10
-  - python
-  license: MIT
-  license_family: MIT
-  purls:
-  - pkg:pypi/zipp?source=hash-mapping
-  size: 24194
-  timestamp: 1764460141901
 - conda: https://conda.anaconda.org/conda-forge/noarch/zipp-3.23.0-pyhd8ed1ab_0.conda
   sha256: 7560d21e1b021fd40b65bfb72f67945a3fcb83d78ad7ccf37b8b3165ec3b68ad
   md5: df5e78d904988eb55042c0c97446079f
@@ -25874,6 +25861,18 @@ packages:
   - pkg:pypi/zipp?source=hash-mapping
   size: 22963
   timestamp: 1749421737203
+- conda: https://conda.anaconda.org/conda-forge/noarch/zipp-3.23.1-pyhcf101f3_0.conda
+  sha256: 523616c0530d305d2216c2b4a8dfd3872628b60083255b89c5e0d8c42e738cca
+  md5: e1c36c6121a7c9c76f2f148f1e83b983
+  depends:
+  - python >=3.10
+  - python
+  license: MIT
+  license_family: MIT
+  purls:
+  - pkg:pypi/zipp?source=compressed-mapping
+  size: 24461
+  timestamp: 1776131454755
 - conda: https://conda.anaconda.org/conda-forge/linux-64/zlib-ng-2.3.3-hceb46e0_1.conda
   sha256: ea4e50c465d70236408cb0bfe0115609fd14db1adcd8bd30d8918e0291f8a75f
   md5: 2aadb0d17215603a82a2a6b0afd9a4cb
diff --git a/skops/io/_general.py b/skops/io/_general.py
index ec4b6247..8437f863 100644
--- a/skops/io/_general.py
+++ b/skops/io/_general.py
@@ -21,6 +21,7 @@
     PRIMITIVE_TYPE_NAMES,
     SCIPY_UFUNC_TYPE_NAMES,
     SKLEARN_ESTIMATOR_TYPE_NAMES,
+    SKLEARN_INTERNAL_TYPE_NAMES,
 )
 from ._utils import (
     LoadContext,
@@ -479,7 +480,10 @@ def __init__(
 
         self.children = {"attrs": attrs}
         # TODO: what do we trust?
-        self.trusted = self._get_trusted(trusted, default=SKLEARN_ESTIMATOR_TYPE_NAMES)
+        self.trusted = self._get_trusted(
+            trusted,
+            default=SKLEARN_ESTIMATOR_TYPE_NAMES + SKLEARN_INTERNAL_TYPE_NAMES,
+        )
 
     def _construct(self):
         cls = gettype(self.module_name, self.class_name)
diff --git a/skops/io/_sklearn.py b/skops/io/_sklearn.py
index 2916b782..9ead73da 100644
--- a/skops/io/_sklearn.py
+++ b/skops/io/_sklearn.py
@@ -79,7 +79,7 @@
         CyPinballLoss,
     }
 except ImportError:
-    pass
+    CyHalfMultinomialLoss = None
 
 # This import is for the parent class of all loss functions, which is used to
 # set the dispatch function for all loss functions.
@@ -230,18 +230,30 @@ def loss_get_state(obj: Any, save_context: SaveContext) -> dict[str, Any]:
         state = reduce_get_state(obj, save_context)
         state["__loader__"] = "LossNode"
     elif type(obj) == reduce[1][0]:
-        # the output is of the form:
+        # The output is commonly of the form:
         # >>> CyPinballLoss(1).__reduce__()
         # (<cyfunction __pyx_unpickle_CyPinballLoss at 0x7b1d00099ff0>,
         #             (<class '_loss.CyPinballLoss'>, 232784418, (1.0,)))
+        #
+        # CyHalfMultinomialLoss differs slightly and may return a 3-tuple:
+        # >>> CyHalfMultinomialLoss().__reduce__()
+        # (<cyfunction __pyx_unpickle_CyHalfMultinomialLoss at 0x...>,
+        #  (<class '_loss.CyHalfMultinomialLoss'>, 238750788, None), ())
+        #
+        # In that case, the constructor takes no args and the state lives
+        # in reduce[2].
         state = {
             "__class__": obj.__class__.__name__,
             "__module__": get_module(type(obj)),
             "__loader__": "LossNode",
         }
         state["__reduce__"] = {}
-        state["__reduce__"]["args"] = get_state(reduce[1][2], save_context)
-        state["content"] = get_state({}, save_context)
+        if len(reduce) == 3:
+            state["__reduce__"]["args"] = get_state((), save_context)
+            state["content"] = get_state(reduce[2], save_context)
+        else:
+            state["__reduce__"]["args"] = get_state(reduce[1][2], save_context)
+            state["content"] = get_state({}, save_context)
 
     return state
 
@@ -326,6 +338,11 @@ def _construct(self):
 if CyLossFunction is not None:
     GET_STATE_DISPATCH_FUNCTIONS.append((CyLossFunction, loss_get_state))
 
+# CyHalfMultinomialLoss is not a subclass of CyLossFunction, so it needs its
+# own dispatch entry. It's already in ALLOWED_LOSSES so LossNode will trust it.
+if CyHalfMultinomialLoss is not None:
+    GET_STATE_DISPATCH_FUNCTIONS.append((CyHalfMultinomialLoss, loss_get_state))
+
 for type_ in UNSUPPORTED_TYPES:
     GET_STATE_DISPATCH_FUNCTIONS.append((type_, unsupported_get_state))
 
diff --git a/skops/io/_trusted_types.py b/skops/io/_trusted_types.py
index e5bee6b8..1f337b95 100644
--- a/skops/io/_trusted_types.py
+++ b/skops/io/_trusted_types.py
@@ -20,6 +20,70 @@
     if get_type_name(estimator_class).startswith("sklearn.")
 ]
 
+# Internal sklearn types used by GradientBoosting and HistGradientBoosting models.
+# These are not public estimators but are safe internal types (loss functions, link
+# functions, binning, and predictor objects) needed for serialization of fitted models.
+_SKLEARN_INTERNAL_TYPES: list[type] = []
+
+try:
+    from sklearn._loss.link import (
+        HalfLogitLink,
+        IdentityLink,
+        Interval,
+        LogitLink,
+        LogLink,
+        MultinomialLogit,
+    )
+
+    _SKLEARN_INTERNAL_TYPES.extend(
+        [HalfLogitLink, IdentityLink, Interval, LogitLink, LogLink, MultinomialLogit]
+    )
+except ImportError:
+    pass
+
+try:
+    from sklearn._loss.loss import (
+        AbsoluteError,
+        ExponentialLoss,
+        HalfBinomialLoss,
+        HalfGammaLoss,
+        HalfMultinomialLoss,
+        HalfPoissonLoss,
+        HalfSquaredError,
+        HuberLoss,
+        PinballLoss,
+    )
+
+    _SKLEARN_INTERNAL_TYPES.extend(
+        [
+            AbsoluteError,
+            ExponentialLoss,
+            HalfBinomialLoss,
+            HalfGammaLoss,
+            HalfMultinomialLoss,
+            HalfPoissonLoss,
+            HalfSquaredError,
+            HuberLoss,
+            PinballLoss,
+        ]
+    )
+except ImportError:
+    pass
+
+try:
+    from sklearn.ensemble._hist_gradient_boosting.binning import _BinMapper
+    from sklearn.ensemble._hist_gradient_boosting.predictor import TreePredictor
+
+    _SKLEARN_INTERNAL_TYPES.extend([_BinMapper, TreePredictor])
+except ImportError:
+    pass
+
+SKLEARN_INTERNAL_TYPE_NAMES = [
+    get_type_name(t)
+    for t in _SKLEARN_INTERNAL_TYPES
+    if get_type_name(t).startswith("sklearn.")
+]
+
 with warnings.catch_warnings():
     # This is to suppress deprecation warning coming from the fact that scipy reports
     # numpy.core for ufuncs, and numpy.core is deprecated and renamed to numpy._core
diff --git a/skops/io/tests/test_persist.py b/skops/io/tests/test_persist.py
index 861cf2eb..6b8eed41 100644
--- a/skops/io/tests/test_persist.py
+++ b/skops/io/tests/test_persist.py
@@ -14,12 +14,19 @@
 import joblib
 import numpy as np
 import pytest
+import sklearn
 from scipy import sparse, special
 from sklearn.base import BaseEstimator, is_regressor
 from sklearn.compose import ColumnTransformer
 from sklearn.datasets import load_sample_images, make_classification, make_regression
 from sklearn.decomposition import SparseCoder
 from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis
+from sklearn.ensemble import (
+    GradientBoostingClassifier,
+    GradientBoostingRegressor,
+    HistGradientBoostingClassifier,
+    HistGradientBoostingRegressor,
+)
 from sklearn.exceptions import SkipTestWarning
 from sklearn.experimental import enable_halving_search_cv  # noqa
 from sklearn.feature_extraction.text import TfidfVectorizer
@@ -438,6 +445,112 @@ def test_can_trust_types(type_):
     assert len(untrusted_types) == 0
 
 
+@pytest.mark.skipif(
+    parse_version(sklearn.__version__) < parse_version("1.4"),
+    reason=(
+        "Before scikit-learn 1.4, GradientBoosting uses different internal loss "
+        "objects (sklearn.ensemble._gb_losses) that are not supported as trusted types."
+    ),
+)
+@pytest.mark.parametrize(
+    ("estimator", "problem_type"),
+    [
+        pytest.param(
+            GradientBoostingClassifier(loss="log_loss", n_estimators=5),
+            "multiclass",
+            id="GradientBoostingClassifier-log_loss-multiclass",
+        ),
+        pytest.param(
+            GradientBoostingClassifier(loss="exponential", n_estimators=5),
+            "binary",
+            id="GradientBoostingClassifier-exponential",
+        ),
+        pytest.param(
+            GradientBoostingRegressor(loss="squared_error", n_estimators=5),
+            "regression",
+            id="GradientBoostingRegressor-squared_error",
+        ),
+        pytest.param(
+            GradientBoostingRegressor(loss="absolute_error", n_estimators=5),
+            "regression",
+            id="GradientBoostingRegressor-absolute_error",
+        ),
+        pytest.param(
+            GradientBoostingRegressor(loss="huber", n_estimators=5),
+            "regression",
+            id="GradientBoostingRegressor-huber",
+        ),
+        pytest.param(
+            GradientBoostingRegressor(loss="quantile", n_estimators=5, alpha=0.8),
+            "regression",
+            id="GradientBoostingRegressor-quantile",
+        ),
+        pytest.param(
+            HistGradientBoostingClassifier(loss="log_loss", max_iter=5),
+            "binary",
+            id="HistGradientBoostingClassifier-log_loss",
+        ),
+        pytest.param(
+            HistGradientBoostingRegressor(loss="gamma", max_iter=5),
+            "positive_regression",
+            id="HistGradientBoostingRegressor-gamma",
+        ),
+        pytest.param(
+            HistGradientBoostingRegressor(loss="poisson", max_iter=5),
+            "positive_regression",
+            id="HistGradientBoostingRegressor-poisson",
+        ),
+    ],
+)
+def test_gradient_boosting_estimators_have_no_untrusted_types(estimator, problem_type):
+    """Fitted GB/HGB models should save and load without any untrusted types,
+    even though they contain non-public sklearn internals (loss functions, link
+    functions, Cython loss classes, _BinMapper, TreePredictor)."""
+    set_random_state(estimator, random_state=0)
+
+    if problem_type == "binary":
+        X, y = make_classification(
+            n_samples=N_SAMPLES,
+            n_features=N_FEATURES,
+            n_classes=2,
+            n_informative=5,
+            random_state=0,
+        )
+    elif problem_type == "multiclass":
+        # n_samples must be > n_classes * n_clusters_per_class to avoid errors
+        X, y = make_classification(
+            n_samples=140,
+            n_features=N_FEATURES,
+            n_classes=3,
+            n_informative=8,
+            n_clusters_per_class=1,
+            random_state=0,
+        )
+    elif problem_type == "positive_regression":
+        X, y = make_regression(
+            n_samples=N_SAMPLES,
+            n_features=N_FEATURES,
+            random_state=0,
+        )
+        y = np.abs(y) + 1
+    else:
+        X, y = make_regression(
+            n_samples=N_SAMPLES,
+            n_features=N_FEATURES,
+            random_state=0,
+        )
+
+    estimator.fit(X, y)
+
+    dumped = dumps(estimator)
+    untrusted_types = get_untrusted_types(data=dumped)
+    assert untrusted_types == []
+
+    loaded = loads(dumped)
+    assert_params_equal(estimator.__dict__, loaded.__dict__)
+    assert_method_outputs_equal(estimator, loaded, X)
+
+
 @pytest.mark.parametrize(
     "estimator", _unsupported_estimators(), ids=_get_check_estimator_ids
 )