# CVE-2022-36002: `CHECK` fail in `Unbatch` in TensorFlow > An attacker can cause a denial of service in TensorFlow by providing a nonscalar input to the Unbatch operation, triggering a CHECK failure. - **Severity:** Medium 5.9 (`CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:H`) - **Exploited in the wild:** Not known - **Login needed:** Not needed - **Attackable:** Over the internet - **Victim action needed:** Not needed - **Fix available:** Yes - **Published:** 16 Sep 2022 - **Updated:** 23 Apr 2025 - **Also known as:** BIT-tensorflow-2022-36002, GHSA-mh3m-62v7-68xg, PYSEC-2026-1020, PYSEC-2026-3216, PYSEC-2026-3352 ## What is CVE-2022-36002? CVE-2022-36002 is a medium-severity denial of service vulnerability in Google Tensorflow, published on 16 Sep 2022. An attacker can exploit it over the internet without logging in, with no action from the victim. ### Official description TensorFlow is an open source platform for machine learning. When `Unbatch` receives a nonscalar input `id`, it gives a `CHECK` fail that can trigger a denial of service attack. We have patched the issue in GitHub commit 4419d10d576adefa36b0e0a9425d2569f7c0189f. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue. Weakness: CWE-617 (Reachable Assertion) ## Which versions are affected by CVE-2022-36002? 22 open source packages ship an affected version, including tensorflow, tensorflow-cpu and tensorflow-gpu. It affects 1 product. | Package | Ecosystem | Affected | Fixed in | | --- | --- | --- | --- | | `tensorflow` | Bitnami | All versions | 2.7.2, 2.8.1, 2.9.1 | | `tensorflow` | PyPI | All versions | 2.7.2, 2.8.1, 2.9.1 | | `tensorflow` | PyPI | All versions | 2.7.2 | | `tensorflow` | PyPI | All versions | 2.7.2 | | `tensorflow` | PyPI | From 2.8.0 | 2.8.1 | | `tensorflow` | PyPI | From 2.8.0 | 2.8.1 | | `tensorflow` | PyPI | From 2.9.0 | 2.9.1 | | `tensorflow` | PyPI | From 2.9.0 | 2.9.1 | | `tensorflow-cpu` | PyPI | All versions | 2.7.2, 2.8.1, 2.9.1 | | `tensorflow-cpu` | PyPI | All versions | 2.7.2 | | `tensorflow-cpu` | PyPI | All versions | 2.7.2 | | `tensorflow-cpu` | PyPI | From 2.8.0 | 2.8.1 | | `tensorflow-cpu` | PyPI | From 2.8.0 | 2.8.1 | | `tensorflow-cpu` | PyPI | From 2.9.0 | 2.9.1 | | `tensorflow-cpu` | PyPI | From 2.9.0 | 2.9.1 | | `tensorflow-gpu` | PyPI | All versions | 2.7.2, 2.8.1, 2.9.1 | | `tensorflow-gpu` | PyPI | All versions | 2.7.2 | | `tensorflow-gpu` | PyPI | All versions | 2.7.2 | | `tensorflow-gpu` | PyPI | From 2.8.0 | 2.8.1 | | `tensorflow-gpu` | PyPI | From 2.8.0 | 2.8.1 | | `tensorflow-gpu` | PyPI | From 2.9.0 | 2.9.1 | | `tensorflow-gpu` | PyPI | From 2.9.0 | 2.9.1 | Products: Google Tensorflow. ## How do I fix CVE-2022-36002? Upgrade tensorflow to a fixed version for your release line: 2.7.2, 2.8.1 or 2.9.1. ## Is CVE-2022-36002 being exploited? There is no known exploitation of CVE-2022-36002 in the wild and no public exploit code. ## 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-2022-36002 and fix it if it is affected. What it is: CVE-2022-36002 is a medium-severity denial of service vulnerability in Google Tensorflow, published on 16 Sep 2022. An attacker can exploit it over the internet without logging in, with no action from the victim. Exploitation: There is no known exploitation of CVE-2022-36002 in the wild and no public exploit code. Affected packages and fixed versions: - tensorflow (Bitnami): affected all versions; fixed in 2.7.2, 2.8.1, 2.9.1 - tensorflow (PyPI): affected all versions; fixed in 2.7.2, 2.8.1, 2.9.1 - tensorflow (PyPI): affected all versions; fixed in 2.7.2 - tensorflow (PyPI): affected all versions; fixed in 2.7.2 - tensorflow (PyPI): affected from 2.8.0; fixed in 2.8.1 - tensorflow (PyPI): affected from 2.8.0; fixed in 2.8.1 - tensorflow (PyPI): affected from 2.9.0; fixed in 2.9.1 - tensorflow (PyPI): affected from 2.9.0; fixed in 2.9.1 - tensorflow-cpu (PyPI): affected all versions; fixed in 2.7.2, 2.8.1, 2.9.1 - tensorflow-cpu (PyPI): affected all versions; fixed in 2.7.2 - tensorflow-cpu (PyPI): affected all versions; fixed in 2.7.2 - tensorflow-cpu (PyPI): affected from 2.8.0; fixed in 2.8.1 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-2022-36002/ (Markdown: https://insights.sbomify.com/CVE-2022-36002/index.md) ``` ## Sources - CVE record: https://www.cve.org/CVERecord?id=CVE-2022-36002 - NVD: https://nvd.nist.gov/vuln/detail/CVE-2022-36002 - OSV: https://osv.dev/vulnerability/CVE-2022-36002 --- From sbomify Insights: https://insights.sbomify.com/CVE-2022-36002/ Summaries are generated and may contain mistakes; the official description above is the record.