What is the CVSS score of CVE-2021-29521?

CVE-2021-29521 has a CVSS 3.1 base score of 5.5 (Medium). CVSS vector: CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H. EPSS exploitation probability: 0.2%.

Is there a public PoC exploit available for CVE-2021-29521?

Yes, a public proof-of-concept exploit is available for CVE-2021-29521. Prioritise patching immediately. EPSS: 0.2%.

Is there a patch available for CVE-2021-29521?

Yes, a patch is available for CVE-2021-29521. Update the affected software to the latest version immediately.

Tensorflow CVE-2021-29521

MEDIUM

Incorrect Calculation of Buffer Size (CWE-131)

2021-05-14 security-advisories@github.com

Information Disclosure Tensorflow

5.5

CVSS 3.1 · NVD

Severity by source

NVD PRIMARY

5.5 MEDIUM

AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H

Primary rating from NVD · only source for this CVE.

CVSS VectorNVD

CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H

Attack Vector

Local

Attack Complexity

Low

Privileges Required

Low

User Interaction

None

Scope

Unchanged

Confidentiality

None

Integrity

None

Availability

High

Lifecycle Timeline

CVE Published

May 14, 2021 - 20:15 nvd

MEDIUM 5.5

Blast Radius

ecosystem impact

† from your stack dependencies † transitive graph · vuln.today resolves 4-path depth

64 pypi packages depend on tensorflow (62 direct, 2 indirect)
1 pypi packages depend on tensorflow-gpu (1 direct, 0 indirect)

Ecosystem-wide dependent count for version 2.3.0 and other introduced versions.

DescriptionNVD

TensorFlow is an end-to-end open source platform for machine learning. Specifying a negative dense shape in tf.raw_ops.SparseCountSparseOutput results in a segmentation fault being thrown out from the standard library as std::vector invariants are broken. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/8f7b60ee8c0206a2c99802e3a4d1bb55d2bc0624/tensorflow/core/kernels/count_ops.cc#L199-L213) assumes the first element of the dense shape is always positive and uses it to initialize a BatchedMap<T> (i.e., std::vector<absl::flat_hash_map<int64,T>>(https://github.com/tensorflow/tensorflow/blob/8f7b60ee8c0206a2c99802e3a4d1bb55d2bc0624/tensorflow/core/kernels/count_ops.cc#L27)) data structure. If the shape tensor has more than one element, num_batches is the first value in shape. Ensuring that the dense_shape argument is a valid tensor shape (that is, all elements are non-negative) solves this issue. 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.

AnalysisAI

TensorFlow is an end-to-end open source platform for machine learning. Rated medium severity (CVSS 5.5), this vulnerability is low attack complexity. Public exploit code available.

Technical ContextAI

This vulnerability is classified under CWE-131. TensorFlow is an end-to-end open source platform for machine learning. Specifying a negative dense shape in tf.raw_ops.SparseCountSparseOutput results in a segmentation fault being thrown out from the standard library as std::vector invariants are broken. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/8f7b60ee8c0206a2c99802e3a4d1bb55d2bc0624/tensorflow/core/kernels/count_ops.cc#L199-L213) assumes the first element of the dense shape is always positive and uses it to initialize a BatchedMap<T> (i.e., std::vector<absl::flat_hash_map<int64,T>>(https://github.com/tensorflow/tensorflow/blob/8f7b60ee8c0206a2c99802e3a4d1bb55d2bc0624/tensorflow/core/kernels/count_ops.cc#L27)) data structure. If the shape tensor has more than one element, num_batches is the first value in shape. Ensuring that the dense_shape argument is a valid tensor shape (that is, all elements are non-negative) solves this issue. 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. Affected products include: Google Tensorflow.

RemediationAI

A vendor patch is available. Apply the latest security update as soon as possible. Apply vendor patches when available. Implement network segmentation and monitoring as interim mitigations.