Heap buffer overflow in `BandedTriangularSolve`
- 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- Where the record stands: published, rejected by its publisher, or superseded by another id.What do these mean?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 2.1.4, 2.2.3, 2.3.3, 2.4.2
- 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
TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a heap buffer overflow in Eigen implementation of `tf.raw_ops.BandedTriangularSolve`. The implementation(https://github.com/tensorflow/tensorflow/blob/eccb7ec454e6617738554a255d77f08e60ee0808/tensorflow/core/kernels/linalg/banded_triangular_solve_op.cc#L269-L278) calls `ValidateInputTensors` for input validation but fails to validate that the two tensors are not empty. Furthermore, since `OP_REQUIRES` macro only stops execution of current function after setting `ctx->status()` to a non-OK value, callers of helper functions that use `OP_REQUIRES` must check value of `ctx->status()` before continuing. This doesn't happen in this op's implementation(https://github.com/tensorflow/tensorflow/blob/eccb7ec454e6617738554a255d77f08e60ee0808/tensorflow/core/kernels/linalg/banded_triangular_solve_op.cc#L219), hence the validation that is present is also not effective. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.
Timeline
- CVE published
- Record last updated
Affected versions
- tensorflow tensorflow
fixed in 2.1.4; From 2.2.0 · fixed in 2.2.3; From 2.3.0 · fixed in 2.3.3; From 2.4.0 · fixed in 2.4.2
- tensorflowPyPI
fixed in 2.1.4; From 2.2.0 · fixed in 2.2.3; From 2.3.0 · fixed in 2.3.3; From 2.4.0 · fixed in 2.4.2
- tensorflow-cpuPyPI
fixed in 2.1.4; From 2.2.0 · fixed in 2.2.3; From 2.3.0 · fixed in 2.3.3; From 2.4.0 · fixed in 2.4.2
- tensorflow-gpuPyPI
fixed in 2.1.4; From 2.2.0 · fixed in 2.2.3; From 2.3.0 · fixed in 2.3.3; From 2.4.0 · fixed in 2.4.2
- tensorflowPyPI
fixed in 2.1.4
- tensorflowPyPI
From 2.2.0 · fixed in 2.2.3
- tensorflowPyPI
From 2.3.0 · fixed in 2.3.3
- tensorflowPyPI
From 2.4.0 · fixed in 2.4.2
- tensorflow-cpuPyPI
fixed in 2.1.4
- tensorflow-cpuPyPI
From 2.2.0 · fixed in 2.2.3
- tensorflow-cpuPyPI
From 2.3.0 · fixed in 2.3.3
- tensorflow-cpuPyPI
From 2.4.0 · fixed in 2.4.2
- tensorflow-gpuPyPI
fixed in 2.1.4
- tensorflow-gpuPyPI
From 2.2.0 · fixed in 2.2.3
- tensorflow-gpuPyPI
From 2.3.0 · fixed in 2.3.3
- tensorflow-gpuPyPI
From 2.4.0 · fixed in 2.4.2