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Tensorflow CVE-2022-41894

HIGH
Classic Buffer Overflow (CWE-120)
2022-11-18 security-advisories@github.com
8.1
CVSS 3.1 · NVD
View

Vulnerability Assessment

Weakness
CWE
ASVS 5.0
V15 - Secure Coding and Architecture Buffer copy size validation
MITRE ATT&CK

1 technique across 1 tactic: Execution

The technique list with ATT&CK links and the kill-chain view is part of the Basic plan and up.

Impact

CVSS score of 8.1 (HIGH). Attack characteristics: network attack vector, high complexity, no privileges required, no user interaction not required. Public proof-of-concept exploit code is available.

Exploit ScenarioDerived

A remote attacker could overflow a buffer to corrupt adjacent memory by sending crafted requests to the affected application. No authentication is required to exploit this vulnerability.

This Week Schedule remediation this week. Why: HIGH severity (CVSS 7.0-8.9).
Patch available 0.5% exploitation probability (EPSS)

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AnalysisAI

TensorFlow is an open source platform for machine learning. Rated high severity (CVSS 8.1), this vulnerability is remotely exploitable, no authentication required. Public exploit code available.

RemediationAI

A vendor patch is available. Apply the latest security update as soon as possible. Always validate buffer sizes before copy operations. Use bounded functions (strncpy, snprintf). Enable compiler protections.

Affected Packages

pypi tensorflow fix: 2.8.4, 2.9.3, 2.10.1

Source: OSV (Open Source Vulnerabilities)

Affects your stack

Affects 1 package in pypi

Checking this CVE against your own stacks and servers is part of the Basic plan and up.

Technical ContextAI · show details

This vulnerability is classified as Buffer Copy without Size Check (CWE-120), which allows attackers to overflow a buffer to corrupt adjacent memory. TensorFlow is an open source platform for machine learning. The reference kernel of the CONV_3D_TRANSPOSE TensorFlow Lite operator wrongly increments the data_ptr when adding the bias to the result. Instead of data_ptr += num_channels; it should be data_ptr += output_num_channels; as if the number of input channels is different than the number of output channels, the wrong result will be returned and a buffer overflow will occur if num_channels > output_num_channels. An attacker can craft a model with a specific number of input channels. It is then possible to write specific values through the bias of the layer outside the bounds of the buffer. This attack only works if the reference kernel resolver is used in the interpreter. We have patched the issue in GitHub commit 72c0bdcb25305b0b36842d746cc61d72658d2941. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range. Affected products include: Google Tensorflow.

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Severity by source

NVD PRIMARY 8.1 HIGH
AV:N/AC:H/PR:N/UI:N/S:U/C:H/I:H/A:H

Primary rating from NVD · only source for this CVE.

CVSS VectorNVD

Attack Vector
Network
Attack Complexity
High
Privileges Required
None
User Interaction
None
Scope
Unchanged
Confidentiality
High
Integrity
High
Availability
High

DescriptionNVD

TensorFlow is an open source platform for machine learning. The reference kernel of the CONV_3D_TRANSPOSE TensorFlow Lite operator wrongly increments the data_ptr when adding the bias to the result. Instead of data_ptr += num_channels; it should be data_ptr += output_num_channels; as if the number of input channels is different than the number of output channels, the wrong result will be returned and a buffer overflow will occur if num_channels > output_num_channels. An attacker can craft a model with a specific number of input channels. It is then possible to write specific values through the bias of the layer outside the bounds of the buffer. This attack only works if the reference kernel resolver is used in the interpreter. We have patched the issue in GitHub commit 72c0bdcb25305b0b36842d746cc61d72658d2941. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.

Threat Intelligence Overview

EPSS Score
0.5%
Top 59% exploitable
Exploit
POC
Public exploit code exists
Data Quality
3 sources
Confidence: Low

How this decision was reached

The engine reads each signal below and applies a fixed list of rules from top to bottom. The first rule that matches sets the recommended action – like a statement, every line is shown.

SignalValue
Severity HIGH
CISA KEV No
Public POC Yes
EPSS 0.5%
Patch available Yes
Active threat No
Deciding rule
HIGH severity (CVSS 7.0-8.9)
What every action level means
Emergency Immediate action required. Criteria: CRITICAL + in CISA KEV (actively exploited) without patch, OR CRITICAL + EPSS > 30% without patch, OR CRITICAL + active threat signals without patch.
Act Now Prioritize within 24-48h. Criteria: in KEV with patch available, OR CRITICAL + public POC, OR HIGH + EPSS > 10%, OR CRITICAL + active threat, OR any CRITICAL severity.
This Week Schedule remediation this week. Criteria: HIGH severity (CVSS 7.0-8.9). Patch within normal change window.
This Month Plan within 30 days. Criteria: MEDIUM severity (CVSS 4.0-6.9), lower exploitation probability. Include in regular patch cycle.
Monitor Track and reassess. Criteria: LOW severity (CVSS 0.1-3.9), minimal exploitation risk. No immediate action needed.

SSVC AssessmentENISA

SSVC Decision· derived
Scheduled
Exploitation· derived
Poc
System Exposure· derived
Open
Automatable· derived
No
Human Impact· derived
Medium

Priority Score

61
Low Medium High Critical
KEV: 0
EPSS: +1
CVSS: +40
PoC: +20
Weaponized: 0
Threat intel: 0
Gov advisory: 0

Attack Technique

A buffer overflow occurs when a program writes more data to a memory buffer than it was allocated to hold, causing the excess data to spill into adjacent memory regions.

Affected Products (CPE)NVD

cpe:2.3:a:google:tensorflow:*:*:*:*:*:*:*:*
cpe:2.3:a:google:tensorflow:*:*:*:*:*:*:*:*
cpe:2.3:a:google:tensorflow:*:*:*:*:*:*:*:*
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