Moderate severityNVD Advisory· Published Jan 3, 2024· Updated Sep 6, 2024

Segfault in paddle.dot

CVE-2023-38676

Description

Nullptr in paddle.dot in PaddlePaddle before 2.6.0. This flaw can cause a runtime crash and a denial of service.

AI Insight

LLM-synthesized narrative grounded in this CVE's description and references.

A null pointer dereference in paddle.dot in PaddlePaddle before 2.6.0 allows attackers to cause a denial of service via crafted input.

CVE-2023-38676 is a null pointer dereference vulnerability in the paddle.dot function of PaddlePaddle, a deep learning framework. The root cause lies in type checking logic within functions like CastPyArg2Ints, CastPyArg2Longs, and CastPyArg2Floats, which incorrectly treat a tensor object as a general Python sequence, leading to a null pointer dereference under certain conditions. This flaw is introduced in versions prior to 2.6.0 [1][3].

An attacker can exploit this vulnerability by providing a specially crafted input to the paddle.dot operation. No authentication is required, and the attack can be performed remotely by sending malicious data to a service using the vulnerable PaddlePaddle API. The absence of proper type validation allows the null pointer dereference to be triggered without any special privileges [1].

The impact of successful exploitation is a runtime crash, resulting in a denial of service (DoS). The vulnerability does not allow arbitrary code execution or data exfiltration, but it can disrupt services relying on the affected PaddlePaddle version [3][4].

The issue is fixed in PaddlePaddle version 2.6.0. Users are advised to upgrade to this version or later to mitigate the vulnerability. No workaround is available for older versions, making patching the recommended action [1][4].

References

AI Insight generated on May 20, 2026. Synthesized from this CVE's description and the cited reference URLs; citations are validated against the source bundle.

Affected packages

Versions sourced from the GitHub Security Advisory.

Package	Affected versions	Patched versions
PaddlePaddlePyPI	< 2.6.0	2.6.0

Affected products

ghsa-coords
pkg:pypi/paddlepaddle
Range: < 2.6.0
PaddlePaddle/PaddlePaddlev5
Range: 0

Patches

19da5c0c4d8c

fix security bug (#55782)

https://github.com/PaddlePaddle/PaddlewanghuancoderAug 2, 2023via ghsa

commit

18 files changed · +90 −6

paddle/fluid/pybind/op_function_common.cc+4 −4 modified

@@ -412,7 +412,7 @@ std::vector<int> CastPyArg2Ints(PyObject* obj,
             i));
       }
     }
-  } else if (PySequence_Check(obj)) {
+  } else if (PySequence_Check(obj) && !PyObject_TypeCheck(obj, p_tensor_type)) {
     Py_ssize_t len = PySequence_Size(obj);
     value.reserve(len);
     PyObject* item = nullptr;
@@ -488,7 +488,7 @@ std::vector<int64_t> CastPyArg2Longs(PyObject* obj,
             i));
       }
     }
-  } else if (PySequence_Check(obj)) {
+  } else if (PySequence_Check(obj) && !PyObject_TypeCheck(obj, p_tensor_type)) {
     Py_ssize_t len = PySequence_Size(obj);
     PyObject* item = nullptr;
     for (Py_ssize_t i = 0; i < len; i++) {
@@ -567,7 +567,7 @@ std::vector<float> CastPyArg2Floats(PyObject* obj,
             i));
       }
     }
-  } else if (PySequence_Check(obj)) {
+  } else if (PySequence_Check(obj) && !PyObject_TypeCheck(obj, p_tensor_type)) {
     Py_ssize_t len = PySequence_Size(obj);
     PyObject* item = nullptr;
     for (Py_ssize_t i = 0; i < len; i++) {
@@ -642,7 +642,7 @@ std::vector<double> CastPyArg2Float64s(PyObject* obj,
             i));
       }
     }
-  } else if (PySequence_Check(obj)) {
+  } else if (PySequence_Check(obj) && !PyObject_TypeCheck(obj, p_tensor_type)) {
     Py_ssize_t len = PySequence_Size(obj);
     PyObject* item = nullptr;
     for (Py_ssize_t i = 0; i < len; i++) {

paddle/phi/infermeta/binary.cc+6 −0 modified

@@ -2663,6 +2663,12 @@ void SearchsortedInferMeta(const MetaTensor& sorted_sequence,
                            MetaTensor* out) {
   auto sequences_dims = sorted_sequence.dims();
   auto values_dims = value.dims();
+  PADDLE_ENFORCE_GE(
+      sequences_dims.size(),
+      1,
+      phi::errors::InvalidArgument(
+          "Input sequences's dimension(%d) must be greater or equal than 1",
+          sequences_dims.size()));
 
   bool flag = true;
   if (sequences_dims.size() != values_dims.size()) {

paddle/phi/kernels/cpu/broadcast_kernel.cc+5 −0 modified

@@ -28,6 +28,11 @@ void BroadcastKernel(const Context& dev_ctx,
                      const DenseTensor& x,
                      int root,
                      DenseTensor* out) {
+  PADDLE_ENFORCE_GT(
+      x.numel(),
+      0,
+      phi::errors::InvalidArgument("Tensor need be broadcast must not empyt."));
+
 #if defined(PADDLE_WITH_GLOO)
   dev_ctx.template Alloc<T>(out);
   auto comm_context =

paddle/phi/kernels/cpu/dot_kernel.cc+3 −0 modified

@@ -27,6 +27,9 @@ void DotKernel(const Context& dev_ctx,
                const DenseTensor& x,
                const DenseTensor& y,
                DenseTensor* out) {
+  if (out->numel() <= 0) {
+    return;
+  }
   auto const *x_ptr = x.data<T>(), *x_ptr_ = &x_ptr[0];
   auto const *y_ptr = y.data<T>(), *y_ptr_ = &y_ptr[0];
   T* z = dev_ctx.template Alloc<T>(out);

paddle/phi/kernels/cpu/eig_kernel.cc+4 −0 modified

@@ -24,6 +24,10 @@ void EigKernel(const Context& dev_ctx,
                const DenseTensor& x,
                DenseTensor* out_w,
                DenseTensor* out_v) {
+  PADDLE_ENFORCE_GT(
+      x.numel(),
+      0,
+      errors::InvalidArgument("EigKernel input tensor is empty."));
   if (!IsComplexType(x.dtype())) {
     dev_ctx.template Alloc<phi::dtype::Complex<T>>(out_w);
     dev_ctx.template Alloc<phi::dtype::Complex<T>>(out_v);

paddle/phi/kernels/cpu/reduce_kernel.cc+4 −0 modified

@@ -29,6 +29,10 @@ void ReduceKernel(const Context& dev_ctx,
                   int root,
                   int reduce_type,
                   DenseTensor* out) {
+  PADDLE_ENFORCE_GT(
+      x.numel(),
+      0,
+      phi::errors::InvalidArgument("Tensor need be reduced must not empyt."));
 #if defined(PADDLE_WITH_GLOO)
   out->Resize(x.dims());
   dev_ctx.template Alloc<T>(out);

paddle/phi/kernels/cpu/top_k_kernel.cc+6 −0 modified

@@ -153,6 +153,12 @@ void TopkKernel(const Context& dev_ctx,
   }
 
   int k = k_scalar.to<int>();
+  PADDLE_ENFORCE_GE(
+      x.numel(),
+      k,
+      errors::InvalidArgument(
+          "x has only %d element, can not find %d top values.", x.numel(), k));
+
   if (k_scalar.FromTensor()) {
     auto out_dims = out->dims();
     // accroding to axis to set K value in the dim

paddle/phi/kernels/funcs/gather_scatter_functor.cc+0 −1 modified

@@ -122,7 +122,6 @@ struct cpu_gather_scatter_functor {
 
           self_idx = is_scatter_like ? replace_index : index_idx;
           src_idx = is_scatter_like ? index_idx : replace_index;
-
           reduce_op((tensor_t*)(self_data + self_idx),  // NOLINT
                     (tensor_t*)(src_data + src_idx));   // NOLINT
           index_idx++;

paddle/phi/kernels/funcs/reduce_function.h+9 −0 modified

@@ -988,6 +988,10 @@ void ReduceKernel(const KPDevice& dev_ctx,
                   const TransformOp& transform,
                   const std::vector<int>& origin_reduce_dims,
                   bool is_mean = false) {
+  PADDLE_ENFORCE_GT(
+      x.numel(),
+      0,
+      phi::errors::InvalidArgument("Tensor need be reduced must not empyt."));
 #ifdef PADDLE_WITH_XPU_KP
   auto stream = dev_ctx.x_context()->xpu_stream;
 #else
@@ -1298,6 +1302,11 @@ void ReduceKernelImpl(const Context& dev_ctx,
                       const std::vector<int64_t>& dims,
                       bool keep_dim,
                       bool reduce_all) {
+  PADDLE_ENFORCE_GT(
+      input.numel(),
+      0,
+      phi::errors::InvalidArgument("Tensor need be reduced must not empyt."));
+
   dev_ctx.template Alloc<OutT>(output);
 
   if (reduce_all) {

paddle/phi/kernels/funcs/repeat_tensor2index_tensor.h+5 −0 modified

@@ -32,6 +32,11 @@ void RepeatsTensor2IndexTensor(const Context& ctx,
 
   int64_t index_size = 0;
   for (int i = 0; i < repeats.dims()[0]; i++) {
+    PADDLE_ENFORCE_GE(repeats_data[i],
+                      0,
+                      phi::errors::InvalidArgument(
+                          "repeats must grater or equal than 0, but got %d",
+                          repeats_data[i]));
     index_size += repeats_data[i];
   }
   std::vector<RepeatsT> index_vec(index_size);

paddle/phi/kernels/gpu/broadcast_kernel.cu+5 −0 modified

@@ -28,6 +28,11 @@ void BroadcastKernel(const Context& dev_ctx,
                      const DenseTensor& x,
                      int root,
                      DenseTensor* out) {
+  PADDLE_ENFORCE_GT(
+      x.numel(),
+      0,
+      phi::errors::InvalidArgument("Tensor need be broadcast must not empyt."));
+
 #if defined(PADDLE_WITH_NCCL) || defined(PADDLE_WITH_RCCL)
   dev_ctx.template Alloc<T>(out);
   gpuStream_t stream = dev_ctx.stream();

paddle/phi/kernels/gpu/dot_kernel.cu+3 −0 modified

@@ -31,6 +31,9 @@ void DotKernel(const Context& dev_ctx,
                const DenseTensor& x,
                const DenseTensor& y,
                DenseTensor* out) {
+  if (out->numel() <= 0) {
+    return;
+  }
   dev_ctx.template Alloc<T>(out);
   if (out->dims().size() == 0) {
     auto eigen_out = phi::EigenScalar<T>::From(*out);

paddle/phi/kernels/gpu/lerp_kernel.cu+10 −0 modified

@@ -51,6 +51,16 @@ void LerpKernel(const Context &ctx,
                 const DenseTensor &y,
                 const DenseTensor &weight,
                 DenseTensor *out) {
+  PADDLE_ENFORCE_GT(
+      x.numel(),
+      0,
+      phi::errors::InvalidArgument("LerpKernel's input x must not empyt."));
+
+  PADDLE_ENFORCE_GT(
+      y.numel(),
+      0,
+      phi::errors::InvalidArgument("LerpKernel's input y must not empyt."));
+
   int rank = out->dims().size();
   PADDLE_ENFORCE_GE(
       rank,

paddle/phi/kernels/gpu/reduce_kernel.cu+4 −0 modified

@@ -29,6 +29,10 @@ void ReduceKernel(const Context& dev_ctx,
                   int root,
                   int reduce_type,
                   DenseTensor* out) {
+  PADDLE_ENFORCE_GT(
+      x.numel(),
+      0,
+      phi::errors::InvalidArgument("Tensor need be reduced must not empyt."));
 #if defined(PADDLE_WITH_NCCL) || defined(PADDLE_WITH_RCCL)
   out->Resize(x.dims());
   dev_ctx.template Alloc<T>(out);

paddle/phi/kernels/gpu/top_k_kernel.cu+5 −0 modified

@@ -77,6 +77,11 @@ void TopkKernel(const Context& dev_ctx,
   if (axis < 0) axis += in_dims.size();
 
   int k = k_scalar.to<int>();
+  PADDLE_ENFORCE_GE(
+      x.numel(),
+      k,
+      errors::InvalidArgument(
+          "x has only %d element, can not find %d top values.", x.numel(), k));
   if (k_scalar.FromTensor()) {
     phi::DDim out_dims = out->dims();
     out_dims[axis] = k;

paddle/phi/kernels/impl/lerp_kernel_impl.h+10 −0 modified

@@ -83,6 +83,16 @@ void LerpKernel(const Context& ctx,
                 const DenseTensor& y,
                 const DenseTensor& weight,
                 DenseTensor* out) {
+  PADDLE_ENFORCE_GT(
+      x.numel(),
+      0,
+      phi::errors::InvalidArgument("LerpKernel's input x must not empyt."));
+
+  PADDLE_ENFORCE_GT(
+      y.numel(),
+      0,
+      phi::errors::InvalidArgument("LerpKernel's input y must not empyt."));
+
   int rank = out->dims().size();
   PADDLE_ENFORCE_GE(
       rank,

paddle/phi/kernels/impl/repeat_interleave_kernel_impl.h+5 −0 modified

@@ -58,6 +58,11 @@ void RepeatInterleaveKernel(const Context& ctx,
                             int repeats,
                             int dim,
                             DenseTensor* out) {
+  PADDLE_ENFORCE_GT(repeats,
+                    0,
+                    phi::errors::InvalidArgument(
+                        "repeats must grater than 0, but got %d", repeats));
+
   auto place = ctx.GetPlace();
   auto cpu_place = phi::CPUPlace();

python/paddle/tensor/manipulation.py+2 −1 modified

@@ -543,6 +543,8 @@ def unstack(x, axis=0, num=None):
         raise ValueError(
             '`axis` must be in the range [-{0}, {0})'.format(x.ndim)
         )
+    if num is not None and (num < 0 or num > x.shape[axis]):
+        raise ValueError(f'`num` must be in the range [0, {x.shape[axis]})')
     if in_dynamic_mode():
         if num is None:
             num = x.shape[axis]
@@ -4372,7 +4374,6 @@ def repeat_interleave(x, repeats, axis=None, name=None):
     if axis is None:
         x = paddle.flatten(x)
         axis = 0
-
     if in_dynamic_mode():
         if isinstance(repeats, Variable):
             return _C_ops.repeat_interleave_with_tensor_index(x, repeats, axis)

Vulnerability mechanics

Generated on May 9, 2026. Inputs: CWE entries + fix-commit diffs from this CVE's patches. Citations validated against bundle.

References

News mentions

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cvss	0.260
epss	0.000
exploit	0.000
kev	0.000
patch	-0.070
ransomware	0.000