What is the CVSS score of CVE-2020-15265?

CVE-2020-15265 has a CVSS 3.1 base score of 7.5 (High). CVSS vector: CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H. EPSS exploitation probability: 0.9%.

Is there a public PoC exploit available for CVE-2020-15265?

Yes, a public proof-of-concept exploit is available for CVE-2020-15265. Prioritise patching immediately. EPSS: 0.9%.

Is there a patch available for CVE-2020-15265?

Yes, a patch is available for CVE-2020-15265. Update the affected software to the latest version immediately.

Tensorflow CVE-2020-15265

HIGH

Out-of-bounds Read (CWE-125)

2020-10-21 security-advisories@github.com

Buffer Overflow Information Disclosure Tensorflow

7.5

CVSS 3.1 · NVD

Severity by source

NVD PRIMARY

7.5 HIGH

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

Attack Vector

Network

Attack Complexity

Low

Privileges Required

None

User Interaction

None

Scope

Unchanged

Confidentiality

None

Integrity

None

Availability

High

Lifecycle Timeline

CVE Published

Oct 21, 2020 - 21:15 nvd

HIGH 7.5

Blast Radius

ecosystem impact

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

50 pypi packages depend on tensorflow (48 direct, 2 indirect)
3 pypi packages depend on tensorflow-gpu (3 direct, 0 indirect)

Ecosystem-wide dependent count for version 2.4.0 and other introduced versions.

DescriptionNVD

In Tensorflow before version 2.4.0, an attacker can pass an invalid axis value to tf.quantization.quantize_and_dequantize. This results in accessing a dimension outside the rank of the input tensor in the C++ kernel implementation. However, dim_size only does a DCHECK to validate the argument and then uses it to access the corresponding element of an array. Since in normal builds, DCHECK-like macros are no-ops, this results in segfault and access out of bounds of the array. The issue is patched in eccb7ec454e6617738554a255d77f08e60ee0808 and TensorFlow 2.4.0 will be released containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved.

AnalysisAI

In Tensorflow before version 2.4.0, an attacker can pass an invalid axis value to tf.quantization.quantize_and_dequantize. Rated high severity (CVSS 7.5), 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 Read (CWE-125), which allows attackers to read data from memory outside the intended buffer boundaries. In Tensorflow before version 2.4.0, an attacker can pass an invalid axis value to tf.quantization.quantize_and_dequantize. This results in accessing a dimension outside the rank of the input tensor in the C++ kernel implementation. However, dim_size only does a DCHECK to validate the argument and then uses it to access the corresponding element of an array. Since in normal builds, DCHECK-like macros are no-ops, this results in segfault and access out of bounds of the array. The issue is patched in eccb7ec454e6617738554a255d77f08e60ee0808 and TensorFlow 2.4.0 will be released containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved. Affected products include: Google Tensorflow. Version information: version 2.4.0.

RemediationAI

A vendor patch is available. Apply the latest security update as soon as possible. Validate array indices and buffer lengths. Use memory-safe languages. Enable AddressSanitizer during testing.