### Issue type

Bug

### Have you reproduced the bug with TensorFlow Nightly?

Yes

### Source

source

### TensorFlow version

tf2.17

### Custom code

Yes

### OS platform and distribution

_No response_

### Mobile device

_No response_

### Python version

_No response_

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

_No response_

### GPU model and memory

_No response_

### Current behavior?

Version
TensorFlow Version: 2.x (based on log structure)
CUDA Version: Unknown (from CUDA driver logs)
GPU Environment: Multi-GPU system with NVIDIA cards
Python Version: 3.x
Current Behavior
Error Description
The application crashes with a core dump when executing tf.debugging.assert_lesswith an extremely large negative summarizeparameter (-36028797018963968). The failure sequence involves:
GPU memory allocation failure
Asynchronous memory copy errors with invalid handles
GPU utility layer fatal error
Error Log Sequence
2025-11-21 10:22:02.116225: I tensorflow/stream_executor/cuda/cuda_driver.cc:733] failed to allocate 2.2K (2304 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory
2025-11-21 10:22:02.839022: I tensorflow/stream_executor/stream.cc:1035] [stream=0xc02540e0,impl=0xbb3c970] did not wait for [stream=0xc06ef1b0,impl=0xbb3b720]
2025-11-21 10:22:02.839103: E tensorflow/stream_executor/cuda/cuda_driver.cc:1157] failed to enqueue async memcpy from device to host: CUDA_ERROR_INVALID_HANDLE: invalid resource handle; host dst: 0x1bc7040; GPU src: 0x7fc847c00200; size: 1=0x1
2025-11-21 10:22:02.839141: E tensorflow/stream_executor/stream.cc:320] Error recording event in stream: Error recording CUDA event: CUDA_ERROR_INVALID_HANDLE: invalid resource handle; not marking stream as bad, as the Event object may be at fault. Monitor for further errors.
2025-11-21 10:22:02.839225: F tensorflow/core/common_runtime/gpu/gpu_util.cc:303] GPU->CPU Memcpy failed
Aborted (core dumped)


### Standalone code to reproduce the issue

```shell
### code
import tensorflow as tf
x = tf.constant([1, 2, 3], dtype=tf.float32)
y = tf.constant([4, 5, 6], dtype=tf.float32)
tf.debugging.assert_less(x, y, message='x should be less than y', summarize
    =-36028797018963968, name='assert_less_check')
```

### Relevant log output

```shell

```