# CVE-2022-23560: Read and Write outside of bounds in TFLite > Attackers can craft malicious TFLite models to trigger out-of-bounds reads and writes, potentially leading to code execution or data exposure. - **Severity:** High 8.8 (`CVSS:3.1/AV:N/AC:L/PR:L/UI:N/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:** Not needed - **Fix available:** Yes - **Published:** 4 Feb 2022 - **Updated:** 22 Apr 2025 - **Also known as:** BIT-tensorflow-2022-23560, GHSA-4hvf-hxvg-f67v, PYSEC-2022-124, PYSEC-2022-69, PYSEC-2026-3099 ## What is CVE-2022-23560? CVE-2022-23560 is a high-severity memory corruption vulnerability in Google Tensorflow, published on 4 Feb 2022. Any signed-in user can exploit it over the network, with no action from the victim. ### Official description Tensorflow is an Open Source Machine Learning Framework. An attacker can craft a TFLite model that would allow limited reads and writes outside of arrays in TFLite. This exploits missing validation in the conversion from sparse tensors to dense tensors. The fix is included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range. Users are advised to upgrade as soon as possible. Weakness: CWE-787 (Out-of-bounds Write), CWE-125 (Out-of-bounds Read) ## Which versions are affected by CVE-2022-23560? 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 | 2.5.3, 2.6.3, 2.7.1 | | `tensorflow` | PyPI | All versions | 2.5.3, 2.6.3, 2.7.1 | | `tensorflow` | PyPI | All versions | 2.5.3 | | `tensorflow` | PyPI | All versions | 2.5.3 | | `tensorflow` | PyPI | From 2.6.0 | 2.6.3 | | `tensorflow` | PyPI | From 2.6.0 | 2.6.3 | | `tensorflow` | PyPI | From 2.7.0 | 2.7.1 | | `tensorflow` | PyPI | From 2.7.0 | 2.7.1 | | `tensorflow-cpu` | PyPI | All versions | 2.5.3, 2.6.3 | | `tensorflow-cpu` | PyPI | All versions | 2.5.3 | | `tensorflow-cpu` | PyPI | All versions | 2.5.3 | | `tensorflow-cpu` | PyPI | From 2.6.0 | 2.6.3 | | `tensorflow-cpu` | PyPI | From 2.6.0 | 2.6.3 | | `tensorflow-cpu` | PyPI | From 2.7.0 | 2.7.1 | | `tensorflow-cpu` | PyPI | From 2.7.0 | 2.7.1 | | `tensorflow-gpu` | PyPI | All versions | 2.5.3, 2.6.3 | | `tensorflow-gpu` | PyPI | All versions | 2.5.3 | | `tensorflow-gpu` | PyPI | All versions | 2.5.3 | | `tensorflow-gpu` | PyPI | From 2.6.0 | 2.6.3 | | `tensorflow-gpu` | PyPI | From 2.6.0 | 2.6.3 | | `tensorflow-gpu` | PyPI | From 2.7.0 | 2.7.1 | | `tensorflow-gpu` | PyPI | From 2.7.0 | 2.7.1 | Products: Google Tensorflow. ## How do I fix CVE-2022-23560? Upgrade tensorflow to a fixed version for your release line: 2.5.3, 2.6.3 or 2.7.1. ## Is CVE-2022-23560 being exploited? It is not confirmed as exploited in the wild, but public exploit code for CVE-2022-23560 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-2022-23560 and fix it if it is affected. What it is: CVE-2022-23560 is a high-severity memory corruption vulnerability in Google Tensorflow, published on 4 Feb 2022. Any signed-in user can exploit it over the network, with no action from the victim. Exploitation: It is not confirmed as exploited in the wild, but public exploit code for CVE-2022-23560 exists. Affected packages and fixed versions: - tensorflow (Bitnami): affected all versions; fixed in 2.5.3, 2.6.3, 2.7.1 - tensorflow (PyPI): affected all versions; fixed in 2.5.3, 2.6.3, 2.7.1 - tensorflow (PyPI): affected all versions; fixed in 2.5.3 - tensorflow (PyPI): affected all versions; fixed in 2.5.3 - tensorflow (PyPI): affected from 2.6.0; fixed in 2.6.3 - tensorflow (PyPI): affected from 2.6.0; fixed in 2.6.3 - tensorflow (PyPI): affected from 2.7.0; fixed in 2.7.1 - tensorflow (PyPI): affected from 2.7.0; fixed in 2.7.1 - tensorflow-cpu (PyPI): affected all versions; fixed in 2.5.3, 2.6.3 - tensorflow-cpu (PyPI): affected all versions; fixed in 2.5.3 - tensorflow-cpu (PyPI): affected all versions; fixed in 2.5.3 - tensorflow-cpu (PyPI): affected from 2.6.0; fixed in 2.6.3 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-2022-23560/ (Markdown: https://insights.sbomify.com/CVE-2022-23560/index.md) ``` ## Sources - CVE record: https://www.cve.org/CVERecord?id=CVE-2022-23560 - NVD: https://nvd.nist.gov/vuln/detail/CVE-2022-23560 - OSV: https://osv.dev/vulnerability/CVE-2022-23560 --- From sbomify Insights: https://insights.sbomify.com/CVE-2022-23560/ Summaries are generated and may contain mistakes; the official description above is the record.