# CVE-2022-41894: Buffer overflow in `CONV_3D_TRANSPOSE` on TFLite

> An attacker can trigger a buffer overflow in TensorFlow Lite's CONV_3D_TRANSPOSE operator by crafting a malicious model, potentially leading to arbitrary code execution.

- **Severity:** High 7.1 (`CVSS:3.1/AV:N/AC:H/PR:L/UI:R/S:U/C:H/I:H/A:H`)
- **Exploited in the wild:** Not known
- **Login needed:** Any signed-in user
- **Attackable:** Over the internet
- **Victim action needed:** A user has to click or open something
- **Fix available:** Yes
- **Published:** 18 Nov 2022
- **Updated:** 22 Apr 2025
- **Also known as:** AZL-11532, BIT-tensorflow-2022-41894, GHSA-h6q3-vv32-2cq5, PYSEC-2026-936

## What is CVE-2022-41894?

CVE-2022-41894 is a high-severity memory corruption vulnerability in Google Tensorflow, published on 18 Nov 2022. Any signed-in user can exploit it over the network, and only if a user opens a file or follows a link.

### Official description

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.

Weakness: CWE-120 (Buffer Copy without Checking Size of Input)

## Which versions are affected by CVE-2022-41894?

8 open source packages ship an affected version, including tensorflow. It affects 1 product.


| Package | Ecosystem | Affected | Fixed in |
| --- | --- | --- | --- |
| `tensorflow` | Bitnami | All versions | 2.8.4, 2.9.3, 2.10.1 |
| `tensorflow` | PyPI | All versions | 2.8.4, 2.9.3, 2.10.1 |
| `tensorflow` | PyPI | All versions | 2.8.4 |
| `tensorflow` | PyPI | All versions | 2.8.4 |
| `tensorflow` | PyPI | From 2.10.0 | 2.10.1 |
| `tensorflow` | PyPI | From 2.10.0 | 2.10.1 |
| `tensorflow` | PyPI | From 2.9.0 | 2.9.3 |
| `tensorflow` | PyPI | From 2.9.0 | 2.9.3 |

Products: Google Tensorflow.

## How do I fix CVE-2022-41894?

Upgrade tensorflow to a fixed version for your release line: 2.8.4, 2.9.3 or 2.10.1.

## Is CVE-2022-41894 being exploited?

It is not confirmed as exploited in the wild, but public exploit code for CVE-2022-41894 exists.

## Other vulnerabilities in Google Tensorflow

- [CVE-2023-25668](https://insights.sbomify.com/CVE-2023-25668/): TensorFlow vulnerable to heap out-of-buffer read in the QuantizeAndDequantize operation (critical 9.8)
- [CVE-2018-7575](https://insights.sbomify.com/CVE-2018-7575/): Integer Overflow or Wraparound in Google TensorFlow (critical 9.8)
- [CVE-2021-37678](https://insights.sbomify.com/CVE-2021-37678/): Arbitrary code execution due to YAML deserialization (critical 9.3)
- [CVE-2021-35958](https://insights.sbomify.com/CVE-2021-35958/): Path traversal in n/a (critical 9.1)
- [CVE-2020-15202](https://insights.sbomify.com/CVE-2020-15202/): Integer truncation in Shard API usage (critical 9.0)

## Fix prompt for AI coding agents

```text
Check this codebase for CVE-2022-41894 and fix it if it is affected.

What it is: CVE-2022-41894 is a high-severity memory corruption vulnerability in Google Tensorflow, published on 18 Nov 2022. Any signed-in user can exploit it over the network, and only if a user opens a file or follows a link.
Exploitation: It is not confirmed as exploited in the wild, but public exploit code for CVE-2022-41894 exists.

Affected packages and fixed versions:
- tensorflow (Bitnami): affected all versions; fixed in 2.8.4, 2.9.3, 2.10.1
- tensorflow (PyPI): affected all versions; fixed in 2.8.4, 2.9.3, 2.10.1
- tensorflow (PyPI): affected all versions; fixed in 2.8.4
- tensorflow (PyPI): affected all versions; fixed in 2.8.4
- tensorflow (PyPI): affected from 2.10.0; fixed in 2.10.1
- tensorflow (PyPI): affected from 2.10.0; fixed in 2.10.1
- tensorflow (PyPI): affected from 2.9.0; fixed in 2.9.3
- tensorflow (PyPI): affected from 2.9.0; fixed in 2.9.3

Task:
1. Search the dependency manifests and lockfiles (for example package.json, package-lock.json, requirements.txt, poetry.lock, uv.lock, pom.xml, build.gradle, go.mod, Cargo.lock, Gemfile.lock) and any Dockerfiles for the affected packages, including transitive dependencies.
2. If an affected version is in use, upgrade it to the fixed version for its release line with the project's package manager, so the lockfile is regenerated. Prefer the smallest upgrade that fixes it.
3. If there is no fix, or the upgrade would break the build, do not guess: explain the risk and the safest workaround instead.
4. Run the tests and summarise what changed and why.
5. If nothing in the project is affected, say so and change nothing.

Reference: https://insights.sbomify.com/CVE-2022-41894/ (Markdown: https://insights.sbomify.com/CVE-2022-41894/index.md)
```

## Sources

- CVE record: https://www.cve.org/CVERecord?id=CVE-2022-41894
- NVD: https://nvd.nist.gov/vuln/detail/CVE-2022-41894
- OSV: https://osv.dev/vulnerability/CVE-2022-41894

---
From sbomify Insights: https://insights.sbomify.com/CVE-2022-41894/
Summaries are generated and may contain mistakes; the official description above is the record.