What is the CVSS score of CVE-2020-15196?

CVE-2020-15196 has a CVSS 3.1 base score of 9.9 (Critical). CVSS vector: CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:C/C:H/I:H/A:H. EPSS exploitation probability: 0.9%.

Is there a public PoC exploit available for CVE-2020-15196?

Yes, a public proof-of-concept exploit is available for CVE-2020-15196. Prioritise patching immediately. EPSS: 0.9%.

Is there a patch available for CVE-2020-15196?

Yes, a patch is available for CVE-2020-15196. Update the affected software to the latest version immediately.

Tensorflow CVE-2020-15196

Q: Which versions of Tensorflow are affected by CVE-2020-15196?

In Tensorflow version 2.3.0, the `SparseCountSparseOutput` and `RaggedCountSparseOutput` implementations don't validate that the `weights` tensor has the same shape as the data.. A patch is available.

CRITICAL

Buffer Overflow (CWE-119)

2020-09-25 security-advisories@github.com

Buffer Overflow Tensorflow

9.9

CVSS 3.1 · NVD

Severity by source

NVD PRIMARY

9.9 CRITICAL

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

Primary rating from NVD · only source for this CVE.

CVSS VectorNVD

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

Attack Vector

Network

Attack Complexity

Low

Privileges Required

Low

User Interaction

None

Scope

Changed

Confidentiality

High

Integrity

High

Availability

High

Lifecycle Timeline

CVE Published

Sep 25, 2020 - 19:15 nvd

CRITICAL 9.9

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-cpu (1 direct, 0 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

In Tensorflow version 2.3.0, the SparseCountSparseOutput and RaggedCountSparseOutput implementations don't validate that the weights tensor has the same shape as the data. The check exists for DenseCountSparseOutput, where both tensors are fully specified. In the sparse and ragged count weights are still accessed in parallel with the data. But, since there is no validation, a user passing fewer weights than the values for the tensors can generate a read from outside the bounds of the heap buffer allocated for the weights. The issue is patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and is released in TensorFlow version 2.3.1.

AnalysisAI

In Tensorflow version 2.3.0, the SparseCountSparseOutput and RaggedCountSparseOutput implementations don't validate that the weights tensor has the same shape as the data. Rated critical severity (CVSS 9.9), this vulnerability is remotely exploitable, low attack complexity. Public exploit code available.

Technical ContextAI

This vulnerability is classified as Buffer Overflow (CWE-119), which allows attackers to corrupt memory to execute arbitrary code or crash the application. In Tensorflow version 2.3.0, the SparseCountSparseOutput and RaggedCountSparseOutput implementations don't validate that the weights tensor has the same shape as the data. The check exists for DenseCountSparseOutput, where both tensors are fully specified. In the sparse and ragged count weights are still accessed in parallel with the data. But, since there is no validation, a user passing fewer weights than the values for the tensors can generate a read from outside the bounds of the heap buffer allocated for the weights. The issue is patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and is released in TensorFlow version 2.3.1. Affected products include: Google Tensorflow. Version information: version 2.3.0.

RemediationAI

A vendor patch is available. Apply the latest security update as soon as possible. Use memory-safe languages or bounds-checking. Enable ASLR, DEP/NX, stack canaries. Use safe string functions.