# CVE-2020-15202: Integer truncation in Shard API usage > Integer truncation in TensorFlow's Shard API can lead to memory corruption, crashes, or potential code execution when processing large parallel workloads. - **Severity:** Critical 9.0 (`CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:C/C:H/I:H/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:** 25 Sep 2020 - **Updated:** 4 Aug 2024 - **Also known as:** BIT-tensorflow-2020-15202, GHSA-h6fg-mjxg-hqq4, PYSEC-2020-125, PYSEC-2020-282, PYSEC-2020-317 ## What is CVE-2020-15202? CVE-2020-15202 is a critical 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 before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the `Shard` API in TensorFlow expects the last argument to be a function taking two `int64` (i.e., `long long`) arguments. However, there are several places in TensorFlow where a lambda taking `int` or `int32` arguments is being used. In these cases, if the amount of work to be parallelized is large enough, integer truncation occurs. Depending on how the two arguments of the lambda are used, this can result in segfaults, read/write outside of heap allocated arrays, stack overflows, or data corruption. The issue is patched in commits 27b417360cbd671ef55915e4bb6bb06af8b8a832 and ca8c013b5e97b1373b3bb1c97ea655e69f31a575, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. Weakness: CWE-754 (Improper Check for Unusual or Exceptional Conditions), CWE-197 (Numeric Truncation Error) ## Which versions are affected by CVE-2020-15202? 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-2020-15202? 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-2020-15202 being exploited? There is no known exploitation of CVE-2020-15202 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-2020-15205](https://insights.sbomify.com/CVE-2020-15205/): Data leak in Tensorflow (critical 9.0) ## Fix prompt for AI coding agents ```text Check this codebase for CVE-2020-15202 and fix it if it is affected. What it is: CVE-2020-15202 is a critical 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-2020-15202 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-2020-15202/ (Markdown: https://insights.sbomify.com/CVE-2020-15202/index.md) ``` ## Sources - CVE record: https://www.cve.org/CVERecord?id=CVE-2020-15202 - NVD: https://nvd.nist.gov/vuln/detail/CVE-2020-15202 - OSV: https://osv.dev/vulnerability/CVE-2020-15202 --- From sbomify Insights: https://insights.sbomify.com/CVE-2020-15202/ Summaries are generated and may contain mistakes; the official description above is the record.