# CVE-2020-15208: Data corruption in tensorflow-lite > Attackers can trigger out-of-bounds reads and writes in TensorFlow Lite by crafting tensors with mismatched dimensions, potentially leading to code execution or crashes. - **Severity:** High 7.4 (`CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:H/I:H/A:N`) - **Exploited in the wild:** Not known - **Login needed:** Not needed - **Attackable:** Over the internet - **Victim action needed:** Not needed - **Fix available:** Yes - **Published:** 25 Sep 2020 - **Updated:** 4 Aug 2024 - **Also known as:** BIT-tensorflow-2020-15208, GHSA-mxjj-953w-2c2v, PYSEC-2020-131, PYSEC-2020-288, PYSEC-2020-323 ## What is CVE-2020-15208? CVE-2020-15208 is a high-severity memory corruption vulnerability in Google Tensorflow, published on 25 Sep 2020. An attacker can exploit it over the internet without logging in, with no action from the victim. ### Official description In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, when determining the common dimension size of two tensors, TFLite uses a `DCHECK` which is no-op outside of debug compilation modes. Since the function always returns the dimension of the first tensor, malicious attackers can craft cases where this is larger than that of the second tensor. In turn, this would result in reads/writes outside of bounds since the interpreter will wrongly assume that there is enough data in both tensors. The issue is patched in commit 8ee24e7949a203d234489f9da2c5bf45a7d5157d, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. Weakness: CWE-787 (Out-of-bounds Write), CWE-125 (Out-of-bounds Read) ## Which versions are affected by CVE-2020-15208? 34 open source packages ship an affected version, including tensorflow, tensorflow-cpu and tensorflow-gpu. It affects 2 products. | Package | Ecosystem | Affected | Fixed in | | --- | --- | --- | --- | | `tensorflow` | Bitnami | All versions | 1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1 | | `tensorflow` | PyPI | All versions | 1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1 | | `tensorflow` | PyPI | All versions | 1.15.4 | | `tensorflow` | PyPI | All versions | 1.15.4 | | `tensorflow` | PyPI | From 2.0.0 | 2.0.3 | | `tensorflow` | PyPI | From 2.0.0 | 2.0.3 | | `tensorflow` | PyPI | From 2.1.0 | 2.1.2 | | `tensorflow` | PyPI | From 2.1.0 | 2.1.2 | | `tensorflow` | PyPI | From 2.2.0 | 2.2.1 | | `tensorflow` | PyPI | From 2.2.0 | 2.2.1 | | `tensorflow` | PyPI | From 2.3.0 | 2.3.1 | | `tensorflow` | PyPI | From 2.3.0 | 2.3.1 | | `tensorflow-cpu` | PyPI | All versions | 1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1 | | `tensorflow-cpu` | PyPI | All versions | 1.15.4 | | `tensorflow-cpu` | PyPI | All versions | 1.15.4 | | `tensorflow-cpu` | PyPI | From 2.0.0 | 2.0.3 | | `tensorflow-cpu` | PyPI | From 2.0.0 | 2.0.3 | | `tensorflow-cpu` | PyPI | From 2.1.0 | 2.1.2 | | `tensorflow-cpu` | PyPI | From 2.1.0 | 2.1.2 | | `tensorflow-cpu` | PyPI | From 2.2.0 | 2.2.1 | | `tensorflow-cpu` | PyPI | From 2.2.0 | 2.2.1 | | `tensorflow-cpu` | PyPI | From 2.3.0 | 2.3.1 | | `tensorflow-cpu` | PyPI | From 2.3.0 | 2.3.1 | | `tensorflow-gpu` | PyPI | All versions | 1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1 | | `tensorflow-gpu` | PyPI | All versions | 1.15.4 | | `tensorflow-gpu` | PyPI | All versions | 1.15.4 | | `tensorflow-gpu` | PyPI | From 2.0.0 | 2.0.3 | | `tensorflow-gpu` | PyPI | From 2.0.0 | 2.0.3 | | `tensorflow-gpu` | PyPI | From 2.1.0 | 2.1.2 | | `tensorflow-gpu` | PyPI | From 2.1.0 | 2.1.2 | | `tensorflow-gpu` | PyPI | From 2.2.0 | 2.2.1 | | `tensorflow-gpu` | PyPI | From 2.2.0 | 2.2.1 | | `tensorflow-gpu` | PyPI | From 2.3.0 | 2.3.1 | | `tensorflow-gpu` | PyPI | From 2.3.0 | 2.3.1 | Products: Google Tensorflow, Opensuse Leap. ## How do I fix CVE-2020-15208? Upgrade tensorflow to a fixed version for your release line: 1.15.4, 2.0.3, 2.1.2 or 2.2.1. ## Is CVE-2020-15208 being exploited? There is no known exploitation of CVE-2020-15208 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-2020-15208 and fix it if it is affected. What it is: CVE-2020-15208 is a high-severity memory corruption vulnerability in Google Tensorflow, published on 25 Sep 2020. 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-2020-15208 in the wild and no public exploit code. Affected packages and fixed versions: - tensorflow (Bitnami): affected all versions; fixed in 1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1 - tensorflow (PyPI): affected all versions; fixed in 1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1 - tensorflow (PyPI): affected all versions; fixed in 1.15.4 - tensorflow (PyPI): affected all versions; fixed in 1.15.4 - tensorflow (PyPI): affected from 2.0.0; fixed in 2.0.3 - tensorflow (PyPI): affected from 2.0.0; fixed in 2.0.3 - tensorflow (PyPI): affected from 2.1.0; fixed in 2.1.2 - tensorflow (PyPI): affected from 2.1.0; fixed in 2.1.2 - tensorflow (PyPI): affected from 2.2.0; fixed in 2.2.1 - tensorflow (PyPI): affected from 2.2.0; fixed in 2.2.1 - tensorflow (PyPI): affected from 2.3.0; fixed in 2.3.1 - tensorflow (PyPI): affected from 2.3.0; fixed in 2.3.1 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-2020-15208/ (Markdown: https://insights.sbomify.com/CVE-2020-15208/index.md) ``` ## Sources - CVE record: https://www.cve.org/CVERecord?id=CVE-2020-15208 - NVD: https://nvd.nist.gov/vuln/detail/CVE-2020-15208 - OSV: https://osv.dev/vulnerability/CVE-2020-15208 --- From sbomify Insights: https://insights.sbomify.com/CVE-2020-15208/ Summaries are generated and may contain mistakes; the official description above is the record.