Buffer Overflow in tensorflow (CVE-2020-15196)
Summary
Vulnerability in tensorflow (CVE-2020-15196). Successful exploitation can lead to full system takeover. Exploitable via SparseCountSparseOutput. Mitigation: upgrade to 2.3.1 or later.
Original description
The description as published by the source.
In Tensorflow version 2.3.0, the SparseCountSparseOutput and RaggedCountSparseOutput implementations don't validate that the weights tensor has the same shape as the data. The check exists for DenseCountSparseOutput, where both tensors are fully specified. In the sparse and ragged count weights are still accessed in parallel with the data. But, since there is no validation, a user passing fewer weights than the values for the tensors can generate a read from outside the bounds of the heap buffer allocated for the weights. The issue is patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and is released in TensorFlow version 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-2020-15196 has been found in tensorflow.
Attackers can target a specific entry point like SparseCountSparseOutput over the network to misuse the product.
Successful exploitation can lead to full system takeover.
What to do: upgrade tensorflow to 2.3.1 or later.
If unsure, ask your IT team or search "tensorflow CVE-2020-15196" on the vendor's site.
CVE-2020-15196 (tensorflow) — CWE-119
Attack vector: remote (network-reachable) / no user interaction
Attack surface: SparseCountSparseOutput / RaggedCountSparseOutput / weights / DenseCountSparseOutput
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-119) exists in tensorflow. Attackers reach the vulnerable code path via SparseCountSparseOutput.
Check the vendor advisory for the affected versions of tensorflow. Attack surface: SparseCountSparseOutput / RaggedCountSparseOutput / weights / DenseCountSparseOutput.
Severity: ?. Successful exploitation can lead to full system takeover.
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。
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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
- package github.com
- web nvd.nist.gov
- web github.com
- web github.com
- web github.com