Denial of Service in Tensorflow
- Published
- Last modified
- Where the record stands: published, rejected by its publisher, or superseded by another id.What do these mean? →
- published
- Whether CISA lists it in its Known Exploited Vulnerabilities catalog: proof that attackers are using it.What do these mean? →
- Not listed in CISA KEV
- Fix
- Fixed in 2.3.1
- Whether working attack code is public (Exploit-DB, Metasploit, Nuclei or GitHub). Public code makes attacks easier.What do these mean? →
- None on Exploit-DB, Metasploit or GitHub
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 rank 2. This tensor must be a matrix because code assumes its elements are accessed as elements of a matrix. However, malicious users can pass in tensors of different rank, resulting in a `CHECK` assertion failure and a crash. This can be used to cause denial of service in serving installations, if users are allowed to control the components of the input sparse tensor. The issue is patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and is released in TensorFlow version 2.3.1.
Timeline
- CVE published
- Record last updated
Affected versions
- tensorflow tensorflow
2.3.0
- tensorflowPyPI
fixed in 2.3.1
- tensorflow-cpuPyPI
fixed in 2.3.1
- tensorflowPyPI
From 2.3.0 · fixed in 2.3.1
- tensorflow-cpuPyPI
From 2.3.0 · fixed in 2.3.1
- tensorflow-gpuPyPI
From 2.3.0 · fixed in 2.3.1
- tensorflow-gpuPyPI
fixed in 2.3.1