What is the CVSS score of CVE-2020-15213?

CVE-2020-15213 has a CVSS 3.1 base score of 4.0 (Medium). CVSS vector: CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:C/C:N/I:N/A:L. EPSS exploitation probability: 0.6%.

Is there a public PoC exploit available for CVE-2020-15213?

Yes, a public proof-of-concept exploit is available for CVE-2020-15213. Prioritise patching immediately. EPSS: 0.6%.

Is there a patch available for CVE-2020-15213?

Yes, a patch is available for CVE-2020-15213. Update the affected software to the latest version immediately.

Tensorflow CVE-2020-15213

MEDIUM

Buffer Overflow (CWE-119)

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

Buffer Overflow Denial Of Service Tensorflow

4.0

CVSS 3.1 · NVD

Severity by source

NVD PRIMARY

4.0 MEDIUM

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

Primary rating from NVD · only source for this CVE.

CVSS VectorNVD

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

Attack Vector

Network

Attack Complexity

High

Privileges Required

None

User Interaction

None

Scope

Changed

Confidentiality

None

Integrity

None

Availability

Low

Lifecycle Timeline

CVE Published

Sep 25, 2020 - 19:15 nvd

MEDIUM 4.0

Blast Radius

ecosystem impact

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

83 pypi packages depend on tensorflow (82 direct, 1 indirect)
1 pypi packages depend on tensorflow-cpu (1 direct, 0 indirect)
2 pypi packages depend on tensorflow-gpu (2 direct, 0 indirect)

Ecosystem-wide dependent count for version 2.2.0 and other introduced versions.

DescriptionNVD

In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger a denial of service by causing an out of memory allocation in the implementation of segment sum. Since code uses the last element of the tensor holding them to determine the dimensionality of output tensor, attackers can use a very large value to trigger a large allocation. The issue is patched in commit 204945b19e44b57906c9344c0d00120eeeae178a and is released in TensorFlow versions 2.2.1, or 2.3.1. A potential workaround would be to add a custom Verifier to limit the maximum value in the segment ids tensor. This only handles the case when the segment ids are stored statically in the model, but a similar validation could be done if the segment ids are generated at runtime, between inference steps. However, if the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code.

AnalysisAI

In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger a denial of service by causing an out of memory allocation in the implementation of segment sum. Rated medium severity (CVSS 4.0), this vulnerability is remotely exploitable, no authentication required. 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 Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger a denial of service by causing an out of memory allocation in the implementation of segment sum. Since code uses the last element of the tensor holding them to determine the dimensionality of output tensor, attackers can use a very large value to trigger a large allocation. The issue is patched in commit 204945b19e44b57906c9344c0d00120eeeae178a and is released in TensorFlow versions 2.2.1, or 2.3.1. A potential workaround would be to add a custom Verifier to limit the maximum value in the segment ids tensor. This only handles the case when the segment ids are stored statically in the model, but a similar validation could be done if the segment ids are generated at runtime, between inference steps. However, if the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code. Affected products include: Google Tensorflow.

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.