Use after free in boosted trees creation 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- Where the record stands: published, rejected by its publisher, or superseded by another id.What do these mean?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 2.5.1, 2.4.3, 2.3.4
- 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
TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation for `tf.raw_ops.BoostedTreesCreateEnsemble` can result in a use after free error if an attacker supplies specially crafted arguments. The implementation uses a reference counted resource and decrements the refcount if the initialization fails, as it should. However, when the code was written, the resource was represented as a naked pointer but later refactoring has changed it to be a smart pointer. Thus, when the pointer leaves the scope, a subsequent `free`-ing of the resource occurs, but this fails to take into account that the refcount has already reached 0, thus the resource has been already freed. During this double-free process, members of the resource object are accessed for cleanup but they are invalid as the entire resource has been freed. We have patched the issue in GitHub commit 5ecec9c6fbdbc6be03295685190a45e7eee726ab. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.
Timeline
- CVE published
- Record last updated
Affected versions
- tensorflow tensorflow
From 2.5.0 · fixed in 2.5.1; From 2.4.0 · fixed in 2.4.3; fixed in 2.3.4
- tensorflowPyPI
fixed in 2.3.4
- tensorflowPyPI
From 2.4.0 · fixed in 2.4.3
- tensorflowPyPI
From 2.5.0 · fixed in 2.5.1
- tensorflow-cpuPyPI
fixed in 2.3.4
- tensorflow-cpuPyPI
From 2.4.0 · fixed in 2.4.3
- tensorflow-cpuPyPI
From 2.5.0 · fixed in 2.5.1
- tensorflow-gpuPyPI
fixed in 2.3.4
- tensorflow-gpuPyPI
From 2.4.0 · fixed in 2.4.3
- tensorflow-gpuPyPI
From 2.5.0 · fixed in 2.5.1
- tensorflow-cpuPyPI
From 2.3.0 · fixed in 2.3.4; From 2.4.0 · fixed in 2.4.3
- tensorflow-gpuPyPI
From 2.3.0 · fixed in 2.3.4; From 2.4.0 · fixed in 2.4.3
- tensorflowPyPI
From 2.3.0 · fixed in 2.3.4; From 2.4.0 · fixed in 2.4.3