CVE-2021-29512

Heap buffer overflow in `RaggedBinCount`

Published May 14, 2021 CVSS 3.1 · 2.5 Low EPSS < 1% KEV · Not listed

Description

TensorFlow is an end-to-end open source platform for machine learning. If the `splits` argument of `RaggedBincount` does not specify a valid `SparseTensor`(https://www.tensorflow.org/api_docs/python/tf/sparse/SparseTensor), then an attacker can trigger a heap buffer overflow. This will cause a read from outside the bounds of the `splits` tensor buffer in the implementation of the `RaggedBincount` op(https://github.com/tensorflow/tensorflow/blob/8b677d79167799f71c42fd3fa074476e0295413a/tensorflow/core/kernels/bincount_op.cc#L430-L433). Before the `for` loop, `batch_idx` is set to 0. The user controls the `splits` array, making it contain only one element, 0. Thus, the code in the `while` loop would increment `batch_idx` and then try to read `splits(1)`, which is outside of bounds. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2 and TensorFlow 2.3.3, as these are also affected.

Affected Products

Default status is the baseline for the product, each version can override it (e.g. patched versions marked unaffected).

Vendor Product Default status Versions

tensorflow

affected

Version	Status	Constraints
`>=2.3.0, < 2.3.3`	affected	—
`>= 2.4.0, < 2.4.2`	affected	—

Configuration 1 [-]

OR	cpe:2.3:a:google:tensorflow::::::::
	cpe:2.3:a:google:tensorflow::::::::

No data.

No data available yet.

Remediation

No solution or workaround provided in the CVE record

Tracking

Advisories

Source	ID	Title
EUVD	EUVD-2021-0253	TensorFlow is an end-to-end open source platform for machine learning. If the `splits` argument of `RaggedBincount` does not specify a valid `SparseTensor`(https://www.tensorflow.org/api_docs/python/tf/sparse/SparseTensor), then an attacker can trigger a heap buffer overflow. This will cause a read from outside the bounds of the `splits` tensor buffer in the implementation of the `RaggedBincount` op(https://github.com/tensorflow/tensorflow/blob/8b677d79167799f71c42fd3fa074476e0295413a/tensorflow/core/kernels/bincount_op.cc#L430-L433). Before the `for` loop, `batch_idx` is set to 0. The user controls the `splits` array, making it contain only one element, 0. Thus, the code in the `while` loop would increment `batch_idx` and then try to read `splits(1)`, which is outside of bounds. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2 and TensorFlow 2.3.3, as these are also affected.
Github GHSA	GHSA-4278-2v5v-65r4	Heap buffer overflow in `RaggedBinCount`

Metrics

No CVSS v4.0

Attack Vector Local

Attack Complexity High

Privileges Required Low

Scope Unchanged

Confidentiality Impact None

Integrity Impact None

Availability Impact Low

User Interaction None

No CVSS v3.0

Access Vector Local

Access Complexity Low

Authentication None

Confidentiality Impact Partial

Integrity Impact Partial

Availability Impact Partial

This CVE is not in the KEV list.

Exploit prediction score: < 1% (0.211% probability within 30 days)

Key SSVC decision points have not yet been added.

References

Link	Providers
https://github.com/tensorflow/tensorflow/commit/eebb96c2830d48597d055d247c0e9aebaea94cd5
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4278-2v5v-65r4