CVE-2025-3000: PyTorch Buffer Overflow Vulnerability

CVE-2025-3000 Overview

A critical memory corruption vulnerability has been identified in PyTorch 2.6.0, affecting the torch.jit.script function. This vulnerability allows local attackers to trigger memory corruption through manipulation of the JIT scripting functionality. The exploit has been publicly disclosed and may be actively used, making prompt attention essential for organizations utilizing PyTorch in their machine learning workflows.

Critical Impact

Memory corruption in PyTorch's JIT compilation functionality can lead to arbitrary code execution, data corruption, or denial of service in machine learning applications and AI infrastructure.

Affected Products

• PyTorch 2.6.0 (Python package)
• Linux Foundation PyTorch distributions
• Applications and services using torch.jit.script functionality

Discovery Timeline

• 2025-03-31 - CVE-2025-3000 published to NVD
• 2025-05-29 - Last updated in NVD database

Technical Details for CVE-2025-3000

Vulnerability Analysis

This vulnerability falls under CWE-119 (Improper Restriction of Operations within the Bounds of a Memory Buffer), a class of memory safety issues that can have severe security implications. The vulnerability specifically targets the torch.jit.script function, which is responsible for converting Python functions into TorchScript for optimization and deployment purposes.

The JIT (Just-In-Time) compilation system in PyTorch performs complex transformations on Python code to generate optimized machine code. During this process, improper handling of certain inputs leads to memory corruption conditions where data can be written outside intended buffer boundaries.

The local attack vector requires an attacker to have local access to the system running PyTorch, but the manipulation that leads to memory corruption can potentially be triggered through crafted model files or malicious scripts processed by the affected function.

Root Cause

The root cause lies in improper memory boundary validation within the torch.jit.script function implementation. When processing certain inputs during JIT compilation, the function fails to properly validate buffer boundaries, leading to memory corruption. This type of vulnerability (CWE-119) typically occurs when:

• Buffer size calculations are incorrect or missing
• Index bounds are not properly validated before memory access
• Memory allocation does not account for all possible input sizes
• Pointer arithmetic operates beyond allocated memory regions

Attack Vector

The vulnerability requires local access to exploit, meaning an attacker must have the ability to execute code on the target system. The attack can be performed with low privileges and does not require user interaction. An attacker could exploit this vulnerability by:

1. Crafting malicious input data or Python scripts that trigger the vulnerable code path in torch.jit.script
2. Processing specially crafted model files that exploit the memory corruption during JIT compilation
3. Manipulating parameters passed to the JIT scripting function to cause out-of-bounds memory operations

The memory corruption can result in confidentiality, integrity, and availability impacts as arbitrary memory regions may be read, modified, or corrupted.

Detection Methods for CVE-2025-3000

Indicators of Compromise

• Unexpected crashes or segmentation faults in applications using PyTorch JIT functionality
• Memory-related errors in application logs when torch.jit.script is invoked
• Anomalous behavior in machine learning model compilation or inference processes
• Core dumps or memory dump files indicating buffer overflow conditions

Detection Strategies

• Monitor PyTorch application logs for memory corruption errors, segfaults, or unexpected exceptions during JIT compilation
• Implement runtime memory analysis tools (Valgrind, AddressSanitizer) in development and staging environments
• Deploy endpoint detection solutions capable of identifying memory corruption attack patterns
• Audit usage of torch.jit.script function calls and track input sources

Monitoring Recommendations

• Enable verbose logging for PyTorch JIT operations to capture compilation errors and anomalies
• Set up alerts for process crashes in ML pipelines that utilize TorchScript functionality
• Monitor system memory usage patterns for unexpected allocations during model processing
• Implement file integrity monitoring on PyTorch model files and scripts

How to Mitigate CVE-2025-3000

Immediate Actions Required

• Inventory all systems and applications using PyTorch 2.6.0 and the torch.jit.script function
• Restrict local access to systems running vulnerable PyTorch installations
• Review and validate all input sources processed by torch.jit.script functionality
• Consider temporarily disabling JIT scripting in non-critical applications until a patch is available

Patch Information

At the time of publication, review the GitHub Issue Discussion for the latest status on official patches from the PyTorch development team. Organizations should monitor the PyTorch security advisories and upgrade to patched versions as soon as they become available.

Additional technical details and threat analysis are available at the VulDB #302049 Threat Analysis.

Workarounds

• Implement strict input validation for any data processed by torch.jit.script to reject potentially malicious inputs
• Use memory-safe compilation environments with ASLR and stack canaries enabled
• Run PyTorch applications in sandboxed environments or containers to limit the impact of potential exploitation
• Apply principle of least privilege to limit the potential damage from successful exploitation
• Consider using torch.compile as an alternative to torch.jit.script where functionally equivalent

# Configuration example - Enable memory protections in Linux environment
# Verify ASLR is enabled
cat /proc/sys/kernel/randomize_va_space
# Should return 2 for full ASLR

# Run PyTorch applications with limited privileges
sudo -u pytorch-user python your_ml_script.py

# Use container isolation for ML workloads
docker run --security-opt=no-new-privileges --read-only pytorch/pytorch:latest