CVE-2021-41496: NumPy Buffer Overflow DoS Vulnerability

CVE-2021-41496 Overview

CVE-2021-41496 is a buffer overflow vulnerability in the array_from_pyobj function within fortranobject.c of NumPy versions prior to 1.19. This vulnerability allows attackers to cause a Denial of Service (DoS) condition by carefully constructing an array with negative values. It is important to note that the vendor disputes this classification as a vulnerability, stating that negative dimensions can only be created by an already privileged user or internally within the application.

Critical Impact
Local attackers with low privileges can exploit this buffer overflow to cause application crashes and denial of service conditions in systems using vulnerable NumPy versions.

Affected Products

NumPy versions prior to 1.19
Applications and libraries that depend on vulnerable NumPy versions
Oracle products incorporating affected NumPy versions (see Oracle July 2022 Security Alert)

Discovery Timeline

2021-12-17 - CVE CVE-2021-41496 published to NVD
2024-11-21 - Last updated in NVD database

Technical Details for CVE-2021-41496

Vulnerability Analysis

This vulnerability exists in the array_from_pyobj function located in fortranobject.c, which is part of NumPy's Fortran interface component (F2PY). The function handles the conversion of Python objects to NumPy arrays and fails to properly validate array dimension parameters before processing them. When an attacker provides carefully crafted negative values for array dimensions, the function does not perform adequate bounds checking, leading to a buffer overflow condition.

The vulnerability is classified as CWE-120 (Buffer Copy without Checking Size of Input), a classic buffer overflow pattern where input data size is not validated before being copied to a buffer. The local attack vector requires an attacker to have local access to the system and the ability to pass malicious input to applications using the affected NumPy functions.

Root Cause

The root cause of this vulnerability lies in insufficient input validation within the array_from_pyobj function. Specifically, the function does not properly check for negative dimension values when creating arrays from Python objects. In C/C++ code, negative values passed to memory allocation or array indexing operations can cause unexpected behavior including buffer overflows, memory corruption, or crashes. The F2PY component, which bridges Python and Fortran code, relies on this function for array conversions, making it a critical point of failure when presented with malformed input.

Attack Vector

The attack vector for CVE-2021-41496 is local, meaning an attacker must have some level of access to the target system. The attacker can exploit this vulnerability by:

Creating a specially crafted Python array object with negative dimension values
Passing this malformed object to NumPy functions that internally call array_from_pyobj
Triggering the buffer overflow condition, which results in application crash or denial of service

The vulnerability requires low privileges to exploit and does not require user interaction. However, the impact is limited to availability (denial of service) rather than confidentiality or integrity breaches.

The exploitation mechanism involves crafting arrays with negative values that bypass normal validation and cause the buffer overflow condition within the Fortran object conversion routines. For technical details, refer to the GitHub Issue Discussion.

Detection Methods for CVE-2021-41496

Indicators of Compromise

Application crashes or unexpected termination in systems using NumPy for array processing
Memory-related error messages in application logs referencing fortranobject.c or F2PY components
Segmentation faults occurring during Python-to-Fortran array conversions

Detection Strategies

Implement software composition analysis (SCA) to identify NumPy versions prior to 1.19 in your environment
Monitor application logs for abnormal crash patterns related to array operations
Use static analysis tools to detect code paths that pass user-controlled input to NumPy array functions
Deploy runtime application self-protection (RASP) solutions to detect buffer overflow attempts

Monitoring Recommendations

Enable verbose logging for applications heavily utilizing NumPy for scientific computing
Monitor system resources for unusual memory consumption patterns that may indicate exploitation attempts
Implement crash dump analysis for applications that terminate unexpectedly
Set up alerts for repeated application restarts that may indicate DoS attacks

How to Mitigate CVE-2021-41496

Immediate Actions Required

Upgrade NumPy to version 1.19 or later to address the vulnerable array_from_pyobj function
Audit applications to identify code paths that accept external input for array creation
Implement input validation at the application level to reject negative dimension values before passing to NumPy
Review dependencies that may bundle or require vulnerable NumPy versions

Patch Information

The vulnerability affects NumPy versions prior to 1.19. Organizations should upgrade to NumPy 1.19 or later to remediate this issue. For environments where immediate upgrade is not possible, refer to the GitHub Issue Discussion for additional context and workaround guidance. Oracle has also addressed this vulnerability in affected products through the Oracle July 2022 Security Alert.

Workarounds

Implement strict input validation to sanitize array dimension values before NumPy processing
Use application-level checks to reject arrays with negative or abnormally large dimensions
Isolate NumPy processing in sandboxed environments to limit the impact of potential crashes
Consider implementing process monitoring and automatic restart capabilities for critical applications

bash

# Configuration example
# Upgrade NumPy to patched version
pip install --upgrade "numpy>=1.19"

# Verify installed version
python -c "import numpy; print(numpy.__version__)"

# For conda environments
conda update numpy