CVE-2020-26270 : What You Need to Know
Learn about CVE-2020-26270 affecting TensorFlow versions < 1.15.5, >= 2.0.0 and < 2.0.4, >= 2.1.0 and < 2.1.3, >= 2.2.0 and < 2.2.2, and >= 2.3.0 and < 2.3.2. Find out the impact, technical details, and mitigation steps for this vulnerability.
In affected versions of TensorFlow, a vulnerability exists that can lead to a denial of service attack when using LSTM/GRU models with zero-length input. This CVE has a CVSS base score of 4.4.
Understanding CVE-2020-26270
This CVE affects TensorFlow versions < 1.15.5, >= 2.0.0 and < 2.0.4, >= 2.1.0 and < 2.1.3, >= 2.2.0 and < 2.2.2, and >= 2.3.0 and < 2.3.2.
What is CVE-2020-26270?
In affected versions of TensorFlow, running an LSTM/GRU model with zero-length input can trigger a CHECK failure, potentially leading to a denial of service vulnerability.
The Impact of CVE-2020-26270
- CVSS Base Score: 4.4 (Medium)
- Attack Complexity: Low
- Attack Vector: Local
- Availability Impact: Low
- Integrity Impact: Low
- Privileges Required: Low
- User Interaction: None
- Scope: Unchanged
- Vector String: CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:L/A:L
Technical Details of CVE-2020-26270
This section provides detailed technical information about the vulnerability.
Vulnerability Description
The vulnerability occurs in LSTM/GRU models in TensorFlow when the input to the layer is zero-length, resulting in a CHECK failure with the CUDA backend.
Affected Systems and Versions
The following TensorFlow versions are affected:
- < 1.15.5
= 2.0.0, < 2.0.4
= 2.1.0, < 2.1.3
= 2.2.0, < 2.2.2
= 2.3.0, < 2.3.2
Exploitation Mechanism
The vulnerability can be exploited by providing zero-length input to an LSTM/GRU layer in TensorFlow, potentially leading to a denial of service attack.
Mitigation and Prevention
It is crucial to take immediate steps to address and prevent the exploitation of this vulnerability.
Immediate Steps to Take
- Update TensorFlow to versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2, or later to mitigate the vulnerability.
- Avoid providing zero-length inputs to LSTM/GRU layers in TensorFlow.
Long-Term Security Practices
- Regularly update TensorFlow to the latest versions to ensure security patches are applied.
- Implement proper input validation mechanisms to prevent similar vulnerabilities.
Patching and Updates
- Apply patches provided by TensorFlow to address the vulnerability and enhance system security.