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

FPE in paddle.lerp

CVE-2023-52306

Description

FPE in paddle.lerp 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 floating point exception in PaddlePaddle's paddle.lerp function before version 2.6.0 can cause a denial of service via crafted input.

The vulnerability is a floating point exception (FPE) in the paddle.lerp function in PaddlePaddle versions prior to 2.6.0. The root cause is improper input validation in the CastPyArg2Ints, CastPyArg2Longs, CastPyArg2Floats, and CastPyArg2Float64s helper functions, which could be triggered by passing a tensor object where a sequence was expected, leading to an FPE [1].

An attacker can exploit this by providing specially crafted input to the paddle.lerp API, bypassing the type check that was missing before the fix. No authentication is required if the attacker can supply input to a PaddlePaddle model or application using this function. The vulnerability can be triggered remotely if the application accepts external data.

The impact is a denial of service (DoS) caused by the runtime crash due to the FPE. While the vulnerability does not lead to data exfiltration or code execution, it can disrupt the availability of services relying on PaddlePaddle [3].

The issue has been fixed in PaddlePaddle version 2.6.0 [1]. Users are advised to upgrade to this version or later. No workarounds have been publicly documented.

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
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