# CVE-2020-15209: Null pointer dereference in tensorflow-lite > A crafted TFLite model causes a null pointer dereference, leading to denial of service in applications using the TensorFlow Lite library. - **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:** 25 Sep 2020 - **Updated:** 4 Aug 2024 - **Also known as:** BIT-tensorflow-2020-15209, GHSA-qh32-6jjc-qprm, PYSEC-2020-132, PYSEC-2020-289, PYSEC-2020-324 ## What is CVE-2020-15209? CVE-2020-15209 is a medium-severity 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, a crafted TFLite model can force a node to have as input a tensor backed by a `nullptr` buffer. This can be achieved by changing a buffer index in the flatbuffer serialization to convert a read-only tensor to a read-write one. The runtime assumes that these buffers are written to before a possible read, hence they are initialized with `nullptr`. However, by changing the buffer index for a tensor and implicitly converting that tensor to be a read-write one, as there is nothing in the model that writes to it, we get a null pointer dereference. The issue is patched in commit 0b5662bc, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. Weakness: CWE-476 (NULL Pointer Dereference) ## Which versions are affected by CVE-2020-15209? 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-2020-15209? 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-2020-15209 being exploited? There is no known exploitation of CVE-2020-15209 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-2020-15209 and fix it if it is affected. What it is: CVE-2020-15209 is a medium-severity 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-2020-15209 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-2020-15209/ (Markdown: https://insights.sbomify.com/CVE-2020-15209/index.md) ``` ## Sources - CVE record: https://www.cve.org/CVERecord?id=CVE-2020-15209 - NVD: https://nvd.nist.gov/vuln/detail/CVE-2020-15209 - OSV: https://osv.dev/vulnerability/CVE-2020-15209 --- From sbomify Insights: https://insights.sbomify.com/CVE-2020-15209/ Summaries are generated and may contain mistakes; the official description above is the record.