What is the CVSS score of CVE-2020-15212?

CVE-2020-15212 has a CVSS 3.1 base score of 8.6 (High). CVSS vector: CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:L/A:H. EPSS exploitation probability: 0.6%.

Is there a public PoC exploit available for CVE-2020-15212?

Yes, a public proof-of-concept exploit is available for CVE-2020-15212. Prioritise patching immediately. EPSS: 0.6%.

Is there a patch available for CVE-2020-15212?

Yes, a patch is available for CVE-2020-15212. Update the affected software to the latest version immediately.

Tensorflow CVE-2020-15212

HIGH

Out-of-bounds Write (CWE-787)

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

Buffer Overflow Memory Corruption Tensorflow

8.6

CVSS 3.1 · NVD

Severity by source

NVD PRIMARY

8.6 HIGH

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

Primary rating from NVD · only source for this CVE.

CVSS VectorNVD

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

Attack Vector

Network

Attack Complexity

Low

Privileges Required

None

User Interaction

None

Scope

Unchanged

Confidentiality

Low

Integrity

Low

Availability

High

Lifecycle Timeline

CVE Published

Sep 25, 2020 - 19:15 nvd

HIGH 8.6

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)
1 pypi packages depend on tensorflow-gpu (1 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 writes outside of bounds of heap allocated buffers by inserting negative elements in the segment ids tensor. Users having access to segment_ids_data can alter output_index and then write to outside of output_data buffer. This might result in a segmentation fault but it can also be used to further corrupt the memory and can be chained with other vulnerabilities to create more advanced exploits. 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 the model loading code to ensure that the segment ids are all positive, although this only handles the case when the segment ids are stored statically in the model. A similar validation could be done if the segment ids are generated at runtime between inference steps. 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 writes outside of bounds of heap allocated buffers by inserting negative elements in the segment ids tensor. Rated high severity (CVSS 8.6), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available.

Technical ContextAI

This vulnerability is classified as Out-of-bounds Write (CWE-787), which allows attackers to write data beyond allocated buffer boundaries leading to code execution or crashes. In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger writes outside of bounds of heap allocated buffers by inserting negative elements in the segment ids tensor. Users having access to segment_ids_data can alter output_index and then write to outside of output_data buffer. This might result in a segmentation fault but it can also be used to further corrupt the memory and can be chained with other vulnerabilities to create more advanced exploits. 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 the model loading code to ensure that the segment ids are all positive, although this only handles the case when the segment ids are stored statically in the model. A similar validation could be done if the segment ids are generated at runtime between inference steps. 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. Validate write boundaries, use memory-safe languages, enable compiler protections (ASLR, stack canaries).