# CVE-2020-26268: Write to immutable memory region in TensorFlow > An attacker can cause a segmentation fault and crash the Python interpreter by using tf.raw_ops.ImmutableConst with non-integral types on memory-mapped files. - **Severity:** Medium 4.4 (`CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:L/A:L`) - **Exploited in the wild:** Not known - **Login needed:** Any signed-in user - **Attackable:** Local or same network only - **Victim action needed:** Not needed - **Fix available:** Yes - **Published:** 10 Dec 2020 - **Updated:** 4 Aug 2024 - **Also known as:** BIT-tensorflow-2020-26268, GHSA-hhvc-g5hv-48c6, PYSEC-2020-255, PYSEC-2020-299, PYSEC-2020-334 ## What is CVE-2020-26268? CVE-2020-26268 is a medium-severity memory corruption vulnerability in Google Tensorflow, published on 10 Dec 2020. It can only be exploited from the same machine or local network, with no action from the victim. ### Official description In affected versions of TensorFlow the tf.raw_ops.ImmutableConst operation returns a constant tensor created from a memory mapped file which is assumed immutable. However, if the type of the tensor is not an integral type, the operation crashes the Python interpreter as it tries to write to the memory area. If the file is too small, TensorFlow properly returns an error as the memory area has fewer bytes than what is needed for the tensor it creates. However, as soon as there are enough bytes, the above snippet causes a segmentation fault. This is because the allocator used to return the buffer data is not marked as returning an opaque handle since the needed virtual method is not overridden. This is fixed in versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2, and 2.4.0. Weakness: CWE-471 (Modification of Assumed-Immutable Data (MAID)) ## Which versions are affected by CVE-2020-26268? 34 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 | 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2 | | `tensorflow` | PyPI | All versions | 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2 | | `tensorflow` | PyPI | All versions | 1.15.5 | | `tensorflow` | PyPI | All versions | 1.15.5 | | `tensorflow` | PyPI | From 2.0.0 | 2.0.4 | | `tensorflow` | PyPI | From 2.0.0 | 2.0.4 | | `tensorflow` | PyPI | From 2.1.0 | 2.1.3 | | `tensorflow` | PyPI | From 2.1.0 | 2.1.3 | | `tensorflow` | PyPI | From 2.2.0 | 2.2.2 | | `tensorflow` | PyPI | From 2.2.0 | 2.2.2 | | `tensorflow` | PyPI | From 2.3.0 | 2.3.2 | | `tensorflow` | PyPI | From 2.3.0 | 2.3.2 | | `tensorflow-cpu` | PyPI | All versions | 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2 | | `tensorflow-cpu` | PyPI | All versions | 1.15.5 | | `tensorflow-cpu` | PyPI | All versions | 1.15.5 | | `tensorflow-cpu` | PyPI | From 2.0.0 | 2.0.4 | | `tensorflow-cpu` | PyPI | From 2.0.0 | 2.0.4 | | `tensorflow-cpu` | PyPI | From 2.1.0 | 2.1.3 | | `tensorflow-cpu` | PyPI | From 2.1.0 | 2.1.3 | | `tensorflow-cpu` | PyPI | From 2.2.0 | 2.2.2 | | `tensorflow-cpu` | PyPI | From 2.2.0 | 2.2.2 | | `tensorflow-cpu` | PyPI | From 2.3.0 | 2.3.2 | | `tensorflow-cpu` | PyPI | From 2.3.0 | 2.3.2 | | `tensorflow-gpu` | PyPI | All versions | 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2 | | `tensorflow-gpu` | PyPI | All versions | 1.15.5 | | `tensorflow-gpu` | PyPI | All versions | 1.15.5 | | `tensorflow-gpu` | PyPI | From 2.0.0 | 2.0.4 | | `tensorflow-gpu` | PyPI | From 2.0.0 | 2.0.4 | | `tensorflow-gpu` | PyPI | From 2.1.0 | 2.1.3 | | `tensorflow-gpu` | PyPI | From 2.1.0 | 2.1.3 | | `tensorflow-gpu` | PyPI | From 2.2.0 | 2.2.2 | | `tensorflow-gpu` | PyPI | From 2.2.0 | 2.2.2 | | `tensorflow-gpu` | PyPI | From 2.3.0 | 2.3.2 | | `tensorflow-gpu` | PyPI | From 2.3.0 | 2.3.2 | Products: Google Tensorflow. ## How do I fix CVE-2020-26268? Upgrade tensorflow to a fixed version for your release line: 1.15.5, 2.0.4, 2.1.3 or 2.2.2. ## Is CVE-2020-26268 being exploited? There is no known exploitation of CVE-2020-26268 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-2020-26268 and fix it if it is affected. What it is: CVE-2020-26268 is a medium-severity memory corruption vulnerability in Google Tensorflow, published on 10 Dec 2020. It can only be exploited from the same machine or local network, with no action from the victim. Exploitation: There is no known exploitation of CVE-2020-26268 in the wild and no public exploit code. Affected packages and fixed versions: - tensorflow (Bitnami): affected all versions; fixed in 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2 - tensorflow (PyPI): affected all versions; fixed in 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2 - tensorflow (PyPI): affected all versions; fixed in 1.15.5 - tensorflow (PyPI): affected all versions; fixed in 1.15.5 - tensorflow (PyPI): affected from 2.0.0; fixed in 2.0.4 - tensorflow (PyPI): affected from 2.0.0; fixed in 2.0.4 - tensorflow (PyPI): affected from 2.1.0; fixed in 2.1.3 - tensorflow (PyPI): affected from 2.1.0; fixed in 2.1.3 - tensorflow (PyPI): affected from 2.2.0; fixed in 2.2.2 - tensorflow (PyPI): affected from 2.2.0; fixed in 2.2.2 - tensorflow (PyPI): affected from 2.3.0; fixed in 2.3.2 - tensorflow (PyPI): affected from 2.3.0; fixed in 2.3.2 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-2020-26268/ (Markdown: https://insights.sbomify.com/CVE-2020-26268/index.md) ``` ## Sources - CVE record: https://www.cve.org/CVERecord?id=CVE-2020-26268 - NVD: https://nvd.nist.gov/vuln/detail/CVE-2020-26268 - OSV: https://osv.dev/vulnerability/CVE-2020-26268 --- From sbomify Insights: https://insights.sbomify.com/CVE-2020-26268/ Summaries are generated and may contain mistakes; the official description above is the record.