Sign in Sign up
JA · EN · FR
SecPulse
Categories
CVE-2020-15211

Out-of-Bounds Read in tensorflow (CVE-2020-15211)

Summary

Vulnerability in tensorflow (CVE-2020-15211). Risk of unauthorized operations or information disclosure. Exploitable via Verifier. Mitigation: upgrade to 2.3.1 or later.

Original description

The description as published by the source.

In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative -1 value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the -1 index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom Verifier to the model loading code to ensure that only operators which accept optional inputs use the -1 special value and only for the tensors that the…

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-2020-15211 has been found in tensorflow.
Attackers can target a specific entry point like Verifier over the network to misuse the product.
Risk of unauthorized operations or information disclosure.
What to do: upgrade tensorflow to 2.3.1 or later.
If unsure, ask your IT team or search "tensorflow CVE-2020-15211" on the vendor's site.

CVE-2020-15211 (tensorflow) — CWE-125
Attack vector: remote (network-reachable) / unauthenticated / no user interaction
Attack surface: Verifier
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.

❓ What is the problem

Out-of-bounds read (CWE-125) exists in tensorflow. Attackers reach the vulnerable code path via Verifier without authentication.

📍 Affected scope

Check the vendor advisory for the affected versions of tensorflow. Attack surface: Verifier.

🔥 Severity

Severity: ?. Risk of unauthorized operations or information disclosure.

🔧 How to fix

Update to 2.3.1.

🛡️ Workaround

Until the patch is applied: disable the affected feature, apply WAF rules, or restrict access via network ACLs.

🔍 Detection

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

  1. 1
    Identify 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。

  2. 2
    Apply 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。

  3. 3
    Post-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

PyPI tensorflow
[{"type":"ECOSYSTEM","events":[{"introduced":"2.3.0"},{"fixed":"2.3.1"}]}]
PyPI tensorflow-cpu
[{"type":"ECOSYSTEM","events":[{"introduced":"2.3.0"},{"fixed":"2.3.1"}]}]
PyPI tensorflow-gpu
[{"type":"ECOSYSTEM","events":[{"introduced":"2.3.0"},{"fixed":"2.3.1"}]}]

References

We use cookies for analytics and ad measurement. This setting applies to all Game co., Ltd. sites. Details