Vulnerability in tensorflow (CVE-2020-15193)
Summary
Vulnerability in tensorflow (CVE-2020-15193). Data can be tampered with by attackers. Exploitable via dlpack.to_dlpack. Mitigation: upgrade to 2.3.1 or later.
Original description
The description as published by the source.
In Tensorflow before versions 2.2.1 and 2.3.1, the implementation of dlpack.to_dlpack can be made to use uninitialized memory resulting in further memory corruption. This is because the pybind11 glue code assumes that the argument is a tensor. However, there is nothing stopping users from passing in a Python object instead of a tensor. The uninitialized memory address is due to a reinterpret_cast Since the PyObject is a Python object, not a TensorFlow Tensor, the cast to EagerTensor fails. The issue is patched in commit 22e07fb204386768e5bcbea563641ea11f96ceb8 and is released in TensorFlow versions 2.2.1, or 2.3.1.
Source: OSV.dev (Open Source Vulnerabilities) NVD GitHub Advisory OSV
Auto-generated summary
Assembled by SecPulse from the source data (CVSS, CWE, affected versions) using templates — no AI involved. Check the original description and references below for exact details.
A vulnerability tracked as CVE-2020-15193 has been found in tensorflow.
Attackers can target a specific entry point like dlpack.to_dlpack over the network to misuse the product.
Data can be tampered with by attackers.
What to do: upgrade tensorflow to 2.3.1 or later.
If unsure, ask your IT team or search "tensorflow CVE-2020-15193" on the vendor's site.
CVE-2020-15193 (tensorflow) — CWE-908
Attack vector: remote (network-reachable) / no user interaction
Attack surface: dlpack.to_dlpack / reinterpret_cast / PyObject / EagerTensor
Patched: 2.3.1 — apply immediately
Plan: 1) Audit SBOM/dependencies, 2) Stage→prod upgrade, 3) Add WAF/proxy monitoring on affected endpoints, 4) Hunt IOCs in logs.
Refs: see the GHSA / vendor advisory / patched release linked on this page.
A vulnerability (CWE-908) exists in tensorflow. Attackers reach the vulnerable code path via dlpack.to_dlpack.
Check the vendor advisory for the affected versions of tensorflow. Attack surface: dlpack.to_dlpack / reinterpret_cast / PyObject / EagerTensor.
Severity: ?. Data can be tampered with by attackers.
Update to 2.3.1.
Until the patch is applied: disable the affected feature, apply WAF rules, or restrict access via network ACLs.
Search webserver/proxy logs for unusual request patterns matching this CVE's known IOCs. Run grep -r 'tensorflow' . against your dependency files (package-lock.json, requirements.txt, go.sum) to find affected services.
Response actions
Concrete steps and command examples for SOC/SRE teams to execute in order
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1Identify exposure identify
grep -r 'tensorflow' . | grep -v node_modulesリポジトリと本番環境の依存ファイル (package-lock.json / requirements.txt / go.sum / Gemfile.lock 等) で
tensorflowを grep し、稼働しているサービス・バージョンを把握する。 -
2Apply patch patch
Upgrade tensorflow to 2.3.1ステージング環境で 2.3.1 に上げて回帰テスト → 本番反映。回帰テストはアプリの主要ハッピーパスを含めること。
-
3Post-deployment verification verify
Confirm patched version is live in productionパッチ適用後、ステージングで PoC または同等の悪用パターンを再現して脆弱性が閉じたことを確認。本番ではアクセスログ・WAF でアラート再発が無いか継続監視。
Affected packages
References
- advisory github.com
- advisory github.com
- advisory github.com
- advisory lists.opensuse.org
- package github.com
- web nvd.nist.gov
- web github.com
- web github.com
- web github.com