# CVE-2023-27579: TensorFlow has Floating Point Exception in TFLite in conv kernel

> An attacker can cause a floating point exception and crash the application by providing a specific invalid parameter in a TFLite model.

- **Severity:** High 7.5 (`CVSS:3.1/AV:N/AC:L/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:** 24 Mar 2023
- **Updated:** 19 Feb 2025
- **Also known as:** AZL-31208, AZL-35323, BIT-tensorflow-2023-27579, GHSA-5w96-866f-6rm8, PYSEC-2026-3111, PYSEC-2026-3279, PYSEC-2026-954

## What is CVE-2023-27579?

CVE-2023-27579 is a high-severity denial of service vulnerability in Google Tensorflow, published on 24 Mar 2023. An attacker can exploit it over the internet without logging in, with no action from the victim.

### Official description

TensorFlow is an end-to-end open source platform for machine learning. Constructing a tflite model with a paramater `filter_input_channel` of less than 1 gives a FPE. This issue has been patched in version 2.12. TensorFlow will also cherrypick the fix commit on TensorFlow 2.11.1.

Weakness: CWE-697 (Incorrect Comparison)

## Which versions are affected by CVE-2023-27579?

10 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 | 2.12.0 |
| `tensorflow` | PyPI | All versions | 2.11.1 |
| `tensorflow` | PyPI | All versions | 2.11.1 |
| `tensorflow` | PyPI | All versions | 2.11.1 |
| `tensorflow-cpu` | PyPI | All versions | 2.11.1 |
| `tensorflow-cpu` | PyPI | All versions | 2.11.1 |
| `tensorflow-cpu` | PyPI | All versions | 2.11.1 |
| `tensorflow-gpu` | PyPI | All versions | 2.11.1 |
| `tensorflow-gpu` | PyPI | All versions | 2.11.1 |
| `tensorflow-gpu` | PyPI | All versions | 2.11.1 |

Products: Google Tensorflow.

## How do I fix CVE-2023-27579?

Upgrade tensorflow to a fixed version for your release line: 2.12.0 or 2.11.1.

## Is CVE-2023-27579 being exploited?

There is no known exploitation of CVE-2023-27579 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-2023-27579 and fix it if it is affected.

What it is: CVE-2023-27579 is a high-severity denial of service vulnerability in Google Tensorflow, published on 24 Mar 2023. 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-2023-27579 in the wild and no public exploit code.

Affected packages and fixed versions:
- tensorflow (Bitnami): affected all versions; fixed in 2.12.0
- tensorflow (PyPI): affected all versions; fixed in 2.11.1
- tensorflow (PyPI): affected all versions; fixed in 2.11.1
- tensorflow (PyPI): affected all versions; fixed in 2.11.1
- tensorflow-cpu (PyPI): affected all versions; fixed in 2.11.1
- tensorflow-cpu (PyPI): affected all versions; fixed in 2.11.1
- tensorflow-cpu (PyPI): affected all versions; fixed in 2.11.1
- tensorflow-gpu (PyPI): affected all versions; fixed in 2.11.1
- tensorflow-gpu (PyPI): affected all versions; fixed in 2.11.1
- tensorflow-gpu (PyPI): affected all versions; fixed in 2.11.1

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-2023-27579/ (Markdown: https://insights.sbomify.com/CVE-2023-27579/index.md)
```

## Sources

- CVE record: https://www.cve.org/CVERecord?id=CVE-2023-27579
- NVD: https://nvd.nist.gov/vuln/detail/CVE-2023-27579
- OSV: https://osv.dev/vulnerability/CVE-2023-27579

---
From sbomify Insights: https://insights.sbomify.com/CVE-2023-27579/
Summaries are generated and may contain mistakes; the official description above is the record.