Out-of-Bounds Write in tensorflow (CVE-2020-15212)
Summary
Out-of-bounds write in tensorflow (CVE-2020-15212). Risk of unauthorized operations or information disclosure. Exploitable via segment_ids_data. Mitigation: upgrade to 2.3.1 or later.
Original description
The description as published by the source.
In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger writes outside of bounds of heap allocated buffers by inserting negative elements in the segment ids tensor. Users having access to segment_ids_data can alter output_index and then write to outside of output_data buffer. This might result in a segmentation fault but it can also be used to further corrupt the memory and can be chained with other vulnerabilities to create more advanced exploits. The issue is patched in commit 204945b19e44b57906c9344c0d00120eeeae178a and is released in TensorFlow versions 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 the segment ids are all positive, although this only handles the case when the segment ids are stored statically in the model. A similar validation could be done if the segment ids are generated at runtime between inference steps. If the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code.
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-2020-15212 has been found in tensorflow.
Attackers can target a specific entry point like segment_ids_data 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-2020-15212" on the vendor's site.
CVE-2020-15212 (tensorflow) — CWE-787
Attack vector: remote (network-reachable) / unauthenticated / no user interaction
Attack surface: segment_ids_data / output_index / output_data / 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.
Out-of-bounds write (CWE-787) exists in tensorflow. Attackers reach the vulnerable code path via segment_ids_data without authentication.
Check the vendor advisory for the affected versions of tensorflow. Attack surface: segment_ids_data / output_index / output_data / Verifier.
Severity: ?. Risk of unauthorized operations or information disclosure.
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 nvd.nist.gov
- advisory github.com
- package github.com
- patch github.com
- patch github.com
- web github.com
- web github.com
- web github.com
- web github.com
- web github.com
- web github.com