Moderate severityNVD Advisory· Published Jan 3, 2024· Updated Jun 17, 2025

Segfault in paddle.put_along_axis

CVE-2023-52303

Description

Nullptr in paddle.put_along_axis 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.

Null-pointer dereference in PaddlePaddle's put_along_axis function allows a denial of service via crafted input.

Overview

CVE-2023-52303 is a null-pointer dereference vulnerability in the put_along_axis function of PaddlePaddle, a deep learning framework. The flaw exists in versions before 2.6.0 and can cause a runtime crash, leading to a denial of service. The root cause is improper handling of Python sequence types when the input is a paddle tensor, which bypasses checks and results in a null pointer access [1].

Exploitation

The vulnerability can be triggered by providing a specially crafted input to the put_along_axis API. No authentication is required if the attacker can supply input to a PaddlePaddle model or application. The attack vector is network-based, as the framework is often used in server-side inference or training environments [2][3].

Impact

Successful exploitation results in a denial of service due to the application crash. The vulnerability does not appear to allow arbitrary code execution or data exfiltration, only availability impact [1][3].

Mitigation

The issue has been patched in PaddlePaddle version 2.6.0. Users should upgrade to this version or later. No workarounds are known. The vulnerability is not listed in CISA's Known Exploited Vulnerabilities (KEV) catalog as of the publication date [1][3].

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

No linked articles in our index yet.

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