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CVE-2019-16778 : Security Advisory and Response

Learn about CVE-2019-16778, a vulnerability in TensorFlow before version 1.15 that could lead to a heap buffer overflow in the UnsortedSegmentSum function. Find out the impact, affected systems, and mitigation steps.

Prior to version 1.15 of TensorFlow, a vulnerability could occur in the UnsortedSegmentSum function, leading to a heap buffer overflow when the Index template argument is set to int32. Although the likelihood of exploitation is low, TensorFlow addressed this issue in versions 1.15 and 2.0.

Understanding CVE-2019-16778

This CVE describes a heap buffer overflow vulnerability in the UnsortedSegmentSum function of TensorFlow.

What is CVE-2019-16778?

In TensorFlow versions before 1.15, a specific condition in the UnsortedSegmentSum function could trigger a heap buffer overflow, potentially allowing access to heap memory beyond the specified bounds.

The Impact of CVE-2019-16778

The impact of this vulnerability is considered low, with a CVSS base score of 2.6. Although the risk of exploitation is minimal, it could lead to accessing memory outside the intended boundaries.

Technical Details of CVE-2019-16778

This section provides detailed technical information about the vulnerability.

Vulnerability Description

The vulnerability arises due to a conversion issue from int64 to int32 in the data_size and num_segments fields, potentially resulting in negative numbers and accessing out-of-bounds heap memory.

Affected Systems and Versions

        Product: TensorFlow
        Vendor: TensorFlow
        Versions Affected: < 1.15

Exploitation Mechanism

        Attack Complexity: High
        Attack Vector: Network
        Privileges Required: Low
        User Interaction: Required
        Scope: Unchanged
        Vector String: CVSS:3.1/AV:N/AC:H/PR:L/UI:R/S:U/C:N/I:N/A:L

Mitigation and Prevention

To address CVE-2019-16778, follow these mitigation strategies:

Immediate Steps to Take

        Update TensorFlow to version 1.15 or higher to mitigate the vulnerability.
        Monitor TensorFlow security advisories for any future updates or patches.

Long-Term Security Practices

        Regularly update software and libraries to the latest versions.
        Implement secure coding practices to prevent buffer overflows and memory access issues.

Patching and Updates

        Apply patches and updates provided by TensorFlow promptly to address security vulnerabilities.

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