Memory exhaustion 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- Where the record stands: published, rejected by its publisher, or superseded by another id.What do these mean?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 2.5.3, 2.6.3, 2.7.1
- 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
- Exploitation: none
- Automatable: no
- Technical impact: partial
Summary
Tensorflow is an Open Source Machine Learning Framework. The implementation of `ThreadPoolHandle` can be used to trigger a denial of service attack by allocating too much memory. This is because the `num_threads` argument is only checked to not be negative, but there is no upper bound on its value. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.
Timeline
- CVE published
- CISA SSVC assessment
Exploitation: none · Automatable: no · Technical impact: partial
- Record last updated
Affected versions
- n/a n/a
Version range not reported
- tensorflowPyPI
fixed in 2.5.3; From 2.6.0 · fixed in 2.6.3; From 2.7.0 · fixed in 2.7.1
- tensorflowPyPI
fixed in 2.5.3
- tensorflowPyPI
From 2.6.0 · fixed in 2.6.3
- tensorflowPyPI
From 2.7.0 · fixed in 2.7.1
- tensorflow-cpuPyPI
fixed in 2.5.3
- tensorflow-cpuPyPI
From 2.6.0 · fixed in 2.6.3
- tensorflow-cpuPyPI
From 2.7.0 · fixed in 2.7.1
- tensorflow-gpuPyPI
fixed in 2.5.3
- tensorflow-gpuPyPI
From 2.6.0 · fixed in 2.6.3
- tensorflow-gpuPyPI
From 2.7.0 · fixed in 2.7.1
- tensorflow-gpuPyPI
fixed in 2.5.3; From 2.6.0 · fixed in 2.6.3
- tensorflow-cpuPyPI
fixed in 2.5.3; From 2.6.0 · fixed in 2.6.3