[AutoPR- Security] Patch pytorch for CVE-2025-3001 [MEDIUM]

SPECS/pytorch/CVE-2025-3001.patch

@@ -0,0 +1,119 @@
+From 55c933d182200c684639f1e77a60eae196e31d84 Mon Sep 17 00:00:00 2001
+From: Yuxingwang-intel <yuxing.wang@intel.com>
+Date: Fri, 19 Dec 2025 10:20:47 +0000
+Subject: [PATCH] Fix segmentation fault caused by invalid gate weight size in
+ lstm_cell (#168348)
+
+This PR adds parameter checks for LSTM weights to fix https://github.com/pytorch/pytorch/issues/149626
+Pull Request resolved: https://github.com/pytorch/pytorch/pull/168348
+Approved by: https://github.com/jiayisunx, https://github.com/mingfeima, https://github.com/albanD, https://github.com/cyyever
+
+Signed-off-by: Azure Linux Security Servicing Account <azurelinux-security@microsoft.com>
+Upstream-reference: https://github.com/pytorch/pytorch/commit/999d94b5ede5f4ec111ba7dd144129e2c2725b03.patch
+---
+ aten/src/ATen/native/RNN.cpp | 15 ++++++++++++++-
+ test/test_nn.py              | 33 +++++++++++++++++++++++++++++++++
+ 2 files changed, 47 insertions(+), 1 deletion(-)
+
+diff --git a/aten/src/ATen/native/RNN.cpp b/aten/src/ATen/native/RNN.cpp
+index 015e7797..4b3b2f89 100644
+--- a/aten/src/ATen/native/RNN.cpp
++++ b/aten/src/ATen/native/RNN.cpp
+@@ -689,6 +689,15 @@ void check_rnn_cell_forward_hidden(const Tensor& input, const Tensor& hx, c10::S
+     "hidden", hidden_label, " has inconsistent hidden_size: got ", hx.sym_size(1), ", expected ", hidden_size);
+ }
+
++template<int64_t gate_count>
++inline void check_rnn_cell_forward_weights(const Tensor& w_ih, const Tensor& w_hh, const c10::SymInt& hidden_size){
++    TORCH_CHECK(w_ih.size(0) == gate_count * hidden_size, "weight_ih first dim must be ", gate_count, " * hidden_size = ",
++        gate_count * hidden_size, ", but got ", w_ih.size(0));
++    TORCH_CHECK(w_hh.size(0) == gate_count * hidden_size, "weight_hh first dim must be ", gate_count, " * hidden_size = ",
++        gate_count * hidden_size, ", but got ", w_hh.size(0));
++}
++
++
+ template<typename hidden_type_tmpl, typename cell_params_tmpl>
+ struct Cell {
+   using hidden_type = hidden_type_tmpl;
+@@ -1536,8 +1545,9 @@ std::tuple<Tensor, Tensor> lstm_cell(
+   const Tensor& b_hh = c10::value_or_else(b_hh_opt, [] {return Tensor();});
+
+   TORCH_CHECK(hx.size() == 2, "lstm_cell expects two hidden states");
+-  check_rnn_cell_forward_input(input, w_ih.sym_size(1));
+   auto hidden_size = w_hh.sym_size(1);
++  check_rnn_cell_forward_input(input, w_ih.sym_size(1));
++  check_rnn_cell_forward_weights<4>(w_ih, w_hh, hidden_size);
+   check_rnn_cell_forward_hidden(input, hx[0], hidden_size, 0);
+   check_rnn_cell_forward_hidden(input, hx[1], std::move(hidden_size), 1);
+   static at::Tensor undefined;
+@@ -1651,6 +1661,7 @@ Tensor gru_cell(
+
+   check_rnn_cell_forward_input(input, w_ih.size(1));
+   check_rnn_cell_forward_hidden(input, hx, w_hh.size(1), 0);
++  check_rnn_cell_forward_weights<3>(w_ih, w_hh, w_hh.size(1));
+   static at::Tensor undefined;
+   return GRUCell<CellParams>{}(input, hx, CellParams{w_ih, w_hh, b_ih, b_hh, undefined});
+ }
+@@ -1664,6 +1675,7 @@ Tensor rnn_tanh_cell(
+   const Tensor& b_hh = c10::value_or_else(b_hh_opt, [] {return Tensor();});
+
+   static at::Tensor undefined;
++  check_rnn_cell_forward_weights<1>(w_ih, w_hh, w_hh.size(1));
+   check_rnn_cell_forward_input(input, w_ih.size(1));
+   check_rnn_cell_forward_hidden(input, hx, w_hh.size(1), 0);
+   return SimpleCell<tanh_f, CellParams>{}(input, hx, CellParams{w_ih, w_hh, b_ih, b_hh, undefined});
+@@ -1678,6 +1690,7 @@ Tensor rnn_relu_cell(
+   const Tensor& b_hh = c10::value_or_else(b_hh_opt, [] {return Tensor();});
+
+   static at::Tensor undefined;
++  check_rnn_cell_forward_weights<1>(w_ih, w_hh, w_hh.size(1));
+   check_rnn_cell_forward_input(input, w_ih.size(1));
+   check_rnn_cell_forward_hidden(input, hx, w_hh.size(1), 0);
+   return SimpleCell<relu_f, CellParams>{}(input, hx, CellParams{w_ih, w_hh, b_ih, b_hh, undefined});
+diff --git a/test/test_nn.py b/test/test_nn.py
+index 08200e9d..ea69f97d 100644
+--- a/test/test_nn.py
++++ b/test/test_nn.py
+@@ -7335,6 +7335,39 @@ tensor(..., device='meta', size=(1,), requires_grad=True)""")
+         with self.assertRaises(RuntimeError):
+             res = arg_class(*arg_4)
+
++    def test_rnn_cell_gate_weights_size(self):
++        def test_rnn_cell(cell_fn, gate_count):
++            input_size = 8
++            hidden_size = 16
++            x = torch.randn(4, input_size)
++            hx = torch.randn(4, hidden_size)
++            cx = torch.randn(4, hidden_size)
++
++            w_ih_invalid = torch.randn((gate_count * hidden_size) + 1, 8)
++            w_ih = torch.randn(gate_count * hidden_size, 8)
++            w_hh_invalid = torch.randn((gate_count * hidden_size) + 1, 16)
++            w_hh = torch.randn(gate_count * hidden_size, 16)
++            b_ih = torch.randn(gate_count * hidden_size)
++            b_hh = torch.randn(gate_count * hidden_size)
++
++            if cell_fn is torch.lstm_cell:
++                state = (hx, cx)
++            else:
++                state = hx
++
++            with self.assertRaisesRegex(RuntimeError, "weight_ih"):
++                cell_fn(x, state, w_ih_invalid, w_hh, b_ih, b_hh)
++
++            with self.assertRaisesRegex(RuntimeError, "weight_hh"):
++                cell_fn(x, state, w_ih, w_hh_invalid, b_ih, b_hh)
++        for cell_fn, gate_count in [
++            (torch.lstm_cell, 4),
++            (torch.gru_cell, 3),
++            (torch.rnn_relu_cell, 1),
++            (torch.rnn_tanh_cell, 1),
++        ]:
++            test_rnn_cell(cell_fn, gate_count)
++
+ class TestFusionEval(TestCase):
+     @set_default_dtype(torch.double)
+     @given(X=hu.tensor(shapes=((5, 3, 5, 5),), dtype=np.double),
+-- 
+2.45.4
+

SPECS/pytorch/pytorch.spec

@@ -2,7 +2,7 @@
 Summary:        Tensors and Dynamic neural networks in Python with strong GPU acceleration.
 Name:           pytorch
 Version:        2.2.2
-Release:        9%{?dist}
+Release:        10%{?dist}
 License:        BSD-3-Clause
 Vendor:         Microsoft Corporation
 Distribution:   Azure Linux
@@ -35,6 +35,7 @@ Patch10:        CVE-2025-2953.patch
 Patch11:        CVE-2025-55552.patch
 Patch12:        CVE-2025-55560.patch
 Patch13:        CVE-2025-46152.patch
+Patch14:        CVE-2025-3001.patch
 
 %description
 PyTorch is a Python package that provides two high-level features:
@@ -96,6 +97,9 @@ cp -arf docs %{buildroot}/%{_pkgdocdir}
 %{_docdir}/*
 
 %changelog
+* Thu Dec 25 2025 Azure Linux Security Servicing Account <azurelinux-security@microsoft.com> - 2.2.2-10
+- Patch for CVE-2025-3001
+
 * Thu Dec 04 2025 Azure Linux Security Servicing Account <azurelinux-security@microsoft.com> - 2.2.2-9
 - Patch for CVE-2025-55560 & CVE-2025-46152