CVE-2017-12852 : Vulnerability Insights and Analysis
Learn about CVE-2017-12852, a vulnerability in Numpy 1.13.1 and older versions that allows DoS attacks due to lack of input validation in the numpy.pad function. Find mitigation steps and prevention measures.
CVE-2017-12852, related to a vulnerability in Numpy 1.13.1 and older versions, allows for a DoS attack due to lack of input validation in the numpy.pad function.
Understanding CVE-2017-12852
In previous versions of Numpy, specifically Numpy 1.13.1 and older, a vulnerability exists in the numpy.pad function that can be exploited by attackers.
What is CVE-2017-12852?
The vulnerability in Numpy 1.13.1 and older versions arises from a lack of input validation in the numpy.pad function. This flaw can lead to an infinite loop when an empty list or ndarray is used as input, potentially enabling a DoS attack.
The Impact of CVE-2017-12852
The vulnerability could allow malicious actors to execute a Denial of Service (DoS) attack by causing the affected application to enter an infinite loop, rendering it unresponsive.
Technical Details of CVE-2017-12852
The technical aspects of the vulnerability in Numpy 1.13.1 and older versions.
Vulnerability Description
The numpy.pad function in Numpy 1.13.1 and older versions lacks input validation. This oversight allows an empty list or ndarray to trigger an infinite loop, creating a potential DoS attack vector.
Affected Systems and Versions
- Product: Numpy
- Vendor: N/A
- Versions: Numpy 1.13.1 and older
Exploitation Mechanism
The vulnerability can be exploited by providing an empty list or ndarray as input to the numpy.pad function, causing the application to enter an infinite loop.
Mitigation and Prevention
Measures to address and prevent the CVE-2017-12852 vulnerability.
Immediate Steps to Take
- Update Numpy to a patched version that addresses the input validation issue.
- Avoid using empty lists or ndarrays as input to the numpy.pad function.
Long-Term Security Practices
- Regularly update software components to mitigate known vulnerabilities.
- Implement input validation mechanisms in code to prevent similar issues.
Patching and Updates
Ensure that Numpy is regularly updated to the latest version to apply patches and security fixes.