Repository: pytorch
Issue:  Aborted (core dumped) in torch.fft.irfftn/hfftn/ihfftn with malloc(): corrupted top size
Labels: needs reproduction,module: crash,module: cpu,triaged,module: fft

### 🐛 Describe the bug

When I pass  torch.fft.irfftn/hfftn/ihfftn a tensor with a shape larger than two dimensions, and the shape value is too large, the code triggers Aborted (core dumped) and outputs "malloc(): corrupted top size ", which seems to corrupt the heap. 




Here is an example:

```
import torch
input = torch.randn(10, 512, 512)
output = torch.fft.irfftn(input)  #crash
#output = torch.fft.hfftn(input) # crash
#output = torch.fft.ihfftn(input) # crash
print(output)
```

output Logs:
```
malloc(): corrupted top size
malloc(): corrupted top size
malloc(): corrupted top size
malloc(): corrupted top size
malloc(): corrupted top size
malloc(): corrupted top size
malloc(): corrupted top size
malloc(): corrupted top size
malloc(): corrupted top size
malloc(): corrupted top size
malloc(): corrupted top size
malloc(): corrupted top size
malloc(): corrupted top size
malloc(): corrupted top size
malloc(): corrupted top size
malloc(): corrupted top size
malloc(): corrupted top size
malloc(): corrupted top size
malloc(): corrupted top size
malloc(): corrupted top size
malloc(): corrupted top size
malloc(): corrupted top size
malloc(): corrupted top size
malloc(): corrupted top size
malloc(): corrupted top size
malloc(): corrupted top size
malloc(): corrupted top size
malloc(): corrupted top size
malloc(): corrupted top size
malloc(): corrupted top size
malloc(): corrupted top size
Aborted (core dumped)
```

When input = torch.randn(10, 512, 10), output "malloc(): corrupted top size" a different number of times. It is possible to trigger "malloc(): corrupted top size" a specified number of times depending on the shape value, which may be maliciously exploited to cause harm.

output Logs:
```
malloc(): corrupted top size
malloc(): corrupted top size
Aborted (core dumped)
````

### Versions

Collecting environment information...
PyTorch version: 2.3.0
Is debug build: False
CUDA used to build PyTorch: 11.8
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.3 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: Could not collect
CMake version: version 3.25.1
Libc version: glibc-2.35

Python version: 3.12.3 | packaged by Anaconda, Inc. | (main, May  6 2024, 19:46:43) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-6.5.0-27-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 12.1.105
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: 
GPU 0: NVIDIA GeForce RTX 3090
GPU 1: NVIDIA GeForce RTX 3090

Nvidia driver version: 535.171.04
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.0
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Architecture:                       x86_64
CPU op-mode(s):                     32-bit, 64-bit
Address sizes:                      46 bits physical, 48 bits virtual
Byte Order:                         Little Endian
CPU(s):                             104
On-line CPU(s) list:                0-103
Vendor ID:                          GenuineIntel
Model name:                         Intel(R) Xeon(R) Gold 6230R CPU @ 2.10GHz
CPU family:                         6
Model:                              85
Thread(s) per core:                 2
Core(s) per socket:                 26
Socket(s):                          2
Stepping:                           7
CPU max MHz:                        4000.0000
CPU min MHz:                        1000.0000
BogoMIPS:                           4200.00
Flags:                              fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cdp_l3 invpcid_single intel_ppin ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req vnmi pku ospke avx512_vnni md_clear flush_l1d arch_capabilities
Virtualization:                     VT-x
L1d cache:                          1.6 MiB (52 instances)
L1i cache:                          1.6 MiB (52 instances)
L2 cache:                           52 MiB (52 instances)
L3 cache:                           71.5 MiB (2 instances)
NUMA node(s):                       2
NUMA node0 CPU(s):                  0-25,52-77
NUMA node1 CPU(s):                  26-51,78-103
Vulnerability Gather data sampling: Mitigation; Microcode
Vulnerability Itlb multihit:        KVM: Mitigation: VMX disabled
Vulnerability L1tf:                 Not affected
Vulnerability Mds:                  Not affected
Vulnerability Meltdown:             Not affected
Vulnerability Mmio stale data:      Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Retbleed:             Mitigation; Enhanced IBRS
Vulnerability Spec rstack overflow: Not affected
Vulnerability Spec store bypass:    Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1:           Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:           Mitigation; Enhanced / Automatic IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Mitigation; TSX disabled

Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] torch==2.3.0
[pip3] torchaudio==2.3.0
[pip3] torchvision==0.18.0
[conda] blas                      1.0                         mkl  
[conda] ffmpeg                    4.3                  hf484d3e_0    pytorch
[conda] libjpeg-turbo             2.0.0                h9bf148f_0    pytorch
[conda] mkl                       2023.1.0         h213fc3f_46344  
[conda] mkl-service               2.4.0           py312h5eee18b_1  
[conda] mkl_fft                   1.3.8           py312h5eee18b_0  
[conda] mkl_random                1.2.4           py312hdb19cb5_0  
[conda] numpy                     1.26.4          py312hc5e2394_0  
[conda] numpy-base                1.26.4          py312h0da6c21_0  
[conda] pytorch                   2.3.0           py3.12_cuda11.8_cudnn8.7.0_0    pytorch
[conda] pytorch-cuda              11.8                 h7e8668a_5    pytorch
[conda] pytorch-mutex             1.0                        cuda    pytorch
[conda] torchaudio                2.3.0               py312_cu118    pytorch
[conda] torchvision               0.18.0              py312_cu118    pytorch


cc @jgong5 @mingfeima @XiaobingSuper @sanchitintel @ashokei @jingxu10 @mruberry @peterbell10