Heap out of bounds access in MakeEdge 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- Where the record stands: published, rejected by its publisher, or superseded by another id.What do these mean?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 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2
- 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 affected versions of TensorFlow under certain cases, loading a saved model can result in accessing uninitialized memory while building the computation graph. The MakeEdge function creates an edge between one output tensor of the src node (given by output_index) and the input slot of the dst node (given by input_index). This is only possible if the types of the tensors on both sides coincide, so the function begins by obtaining the corresponding DataType values and comparing these for equality. However, there is no check that the indices point to inside of the arrays they index into. Thus, this can result in accessing data out of bounds of the corresponding heap allocated arrays. In most scenarios, this can manifest as unitialized data access, but if the index points far away from the boundaries of the arrays this can be used to leak addresses from the library. This is fixed in versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2, and 2.4.0.
Timeline
- CVE published
- Record last updated
Affected versions
- tensorflow tensorflow
fixed in 1.15.5; From 2.0.0 · fixed in 2.0.4; From 2.1.0 · fixed in 2.1.3; From 2.2.0 · fixed in 2.2.2; From 2.3.0 · fixed in 2.3.2
- tensorflow-cpuPyPI
fixed in 1.15.5; From 2.0.0 · fixed in 2.0.4; From 2.1.0 · fixed in 2.1.3; From 2.2.0 · fixed in 2.2.2; From 2.3.0 · fixed in 2.3.2
- tensorflow-gpuPyPI
fixed in 1.15.5; From 2.0.0 · fixed in 2.0.4; From 2.1.0 · fixed in 2.1.3; From 2.2.0 · fixed in 2.2.2; From 2.3.0 · fixed in 2.3.2
- tensorflowPyPI
fixed in 1.15.5; From 2.0.0 · fixed in 2.0.4; From 2.1.0 · fixed in 2.1.3; From 2.2.0 · fixed in 2.2.2; From 2.3.0 · fixed in 2.3.2
- tensorflowPyPI
fixed in 1.15.5
- tensorflowPyPI
From 2.0.0 · fixed in 2.0.4
- tensorflowPyPI
From 2.1.0 · fixed in 2.1.3
- tensorflowPyPI
From 2.2.0 · fixed in 2.2.2
- tensorflowPyPI
From 2.3.0 · fixed in 2.3.2
- tensorflow-cpuPyPI
fixed in 1.15.5
- tensorflow-cpuPyPI
From 2.0.0 · fixed in 2.0.4
- tensorflow-cpuPyPI
From 2.1.0 · fixed in 2.1.3
- tensorflow-cpuPyPI
From 2.2.0 · fixed in 2.2.2
- tensorflow-cpuPyPI
From 2.3.0 · fixed in 2.3.2
- tensorflow-gpuPyPI
fixed in 1.15.5
- tensorflow-gpuPyPI
From 2.0.0 · fixed in 2.0.4
- tensorflow-gpuPyPI
From 2.1.0 · fixed in 2.1.3
- tensorflow-gpuPyPI
From 2.2.0 · fixed in 2.2.2
- tensorflow-gpuPyPI
From 2.3.0 · fixed in 2.3.2