Vulnerabilities > CVE-2020-15198 - Improper Restriction of Operations within the Bounds of a Memory Buffer vulnerability in Google Tensorflow 2.3.0
Summary
In Tensorflow before version 2.3.1, the `SparseCountSparseOutput` implementation does not validate that the input arguments form a valid sparse tensor. In particular, there is no validation that the `indices` tensor has the same shape as the `values` one. The values in these tensors are always accessed in parallel. Thus, a shape mismatch can result in accesses outside the bounds of heap allocated buffers. The issue is patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and is released in TensorFlow version 2.3.1.
Vulnerable Configurations
Part | Description | Count |
---|---|---|
Application | 4 |
Common Weakness Enumeration (CWE)
Common Attack Pattern Enumeration and Classification (CAPEC)
- Buffer Overflow via Environment Variables This attack pattern involves causing a buffer overflow through manipulation of environment variables. Once the attacker finds that they can modify an environment variable, they may try to overflow associated buffers. This attack leverages implicit trust often placed in environment variables.
- Overflow Buffers Buffer Overflow attacks target improper or missing bounds checking on buffer operations, typically triggered by input injected by an attacker. As a consequence, an attacker is able to write past the boundaries of allocated buffer regions in memory, causing a program crash or potentially redirection of execution as per the attackers' choice.
- Client-side Injection-induced Buffer Overflow This type of attack exploits a buffer overflow vulnerability in targeted client software through injection of malicious content from a custom-built hostile service.
- Filter Failure through Buffer Overflow In this attack, the idea is to cause an active filter to fail by causing an oversized transaction. An attacker may try to feed overly long input strings to the program in an attempt to overwhelm the filter (by causing a buffer overflow) and hoping that the filter does not fail securely (i.e. the user input is let into the system unfiltered).
- MIME Conversion An attacker exploits a weakness in the MIME conversion routine to cause a buffer overflow and gain control over the mail server machine. The MIME system is designed to allow various different information formats to be interpreted and sent via e-mail. Attack points exist when data are converted to MIME compatible format and back.
References
- https://github.com/tensorflow/tensorflow/commit/3cbb917b4714766030b28eba9fb41bb97ce9ee02
- https://github.com/tensorflow/tensorflow/commit/3cbb917b4714766030b28eba9fb41bb97ce9ee02
- https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1
- https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-jc87-6vpp-7ff3
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-jc87-6vpp-7ff3