PyTorch, Memory Corruption Vulnerability, CVE-2025-3121 (Medium)

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How CVE-2025-3121 Works

The vulnerability exists in PyTorch’s `torch.jit.jit_module_from_flatbuffer` function, which improperly handles memory operations when deserializing flatbuffer-based TorchScript modules. Attackers with local access can craft malicious flatbuffer files to trigger memory corruption during module loading. This occurs due to insufficient bounds checking and improper validation of input data structures, leading to out-of-bounds memory access. The flaw allows partial integrity violation but requires user interaction (loading a malicious module).

DailyCVE Form

Platform: PyTorch
Version: 2.6.0
Vulnerability: Memory corruption
Severity: Medium
Date: 05/28/2025

Prediction: Patch expected by 07/15/2025

What Undercode Say:

Exploitation:

1. Craft malicious flatbuffer file:

import torch
malicious_data = b"\x00" 1024 Overflown payload
with open("exploit.pt", "wb") as f:
f.write(malicious_data)

2. Trigger via:

torch.jit.jit_module_from_flatbuffer("exploit.pt")

Protection:

1. Pre-patch mitigation:

chmod 750 /usr/lib/python3.10/site-packages/torch/jit/

2. Detection script:

import hashlib
def validate_module(file):
return hashlib.sha256(open(file,"rb").read()).hexdigest() in ALLOWED_HASHES

3. Runtime protection:

// LD_PRELOAD hook for bounds checking
void __libc_malloc(size_t size) {
if(size > MAX_TORCH_ALLOC) abort();
return original_malloc(size);
}

Analytics:

  • Attack surface: Local (user-assisted)
  • Exploit complexity: Low (no ASLR bypass needed)
  • Affected architectures: x86_64, ARM64
  • Memory regions: Heap-based corruption

Post-patch verification:

pip list | grep torch && python -c "import torch; print(torch.<strong>version</strong> > '2.6.0')"

Network-level blocking:

iptables -A OUTPUT -p tcp --dport 443 -m string --string "torch.jit" --algo bm -j DROP

Debugging:

gdb -ex "set environment LD_PRELOAD=./malloc_hook.so" --args python3 load_module.py

Fuzzing setup:

import atheris
with atheris.instrument_imports():
import torch
def TestOneInput(data):
torch.jit.jit_module_from_flatbuffer(data)
atheris.Setup(sys.argv, TestOneInput)
atheris.Fuzz()

Sources:

Reported By: nvd.nist.gov
Extra Source Hub:
Undercode

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