Vulnerability in tensorflow (CVE-2023-25666)
Summary
Vulnerability in tensorflow (CVE-2023-25666). Risk of unauthorized operations or information disclosure. Mitigation: upgrade to 2.11.1 or later.
Original description
The description as published by the source.
Impact
version:2.11.0 //core/ops/audio_ops.cc:70
Status SpectrogramShapeFn(InferenceContext* c) { ShapeHandle input; TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 2, &input)); int32_t window_size; TF_RETURN_IF_ERROR(c->GetAttr("window_size", &window_size)); int32_t stride; TF_RETURN_IF_ERROR(c->GetAttr("stride", &stride)); .....[1]
DimensionHandle input_length = c->Dim(input, 0); DimensionHandle input_channels = c->Dim(input, 1);
DimensionHandle output_length; if (!c->ValueKnown(input_length)) { output_length = c->UnknownDim(); } else { const int64_t input_length_value = c->Value(input_length); const int64_t length_minus_window = (input_length_value - window_size); int64_t output_length_value; if (length_minus_window < 0) { output_length_value = 0; } else { output_length_value = 1 + (length_minus_window / stride); .....[2] } output_length = c->MakeDim(output_length_value); }
Get the value of stride at [1], and the used at [2]
import tensorflow as tf
para = {'input': tf.constant([[14.], [24.]], dtype=tf.float32), 'window_size': 1, 'stride': 0, 'magnitude_squared': False}
func = tf.raw_ops.AudioSpectrogram
@tf.function(jit_compile=True)
def fuzz_jit():
y = func(**para)
return y
fuzz_jit()
Patches
We have patched the issue in GitHub commit d0d4e779da0d0f56499c6fa5ba09f0a576cc6b14.
The fix will be included in TensorFlow 2.12.0. We will also cherrypick thi…
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-2023-25666 has been found in tensorflow.
Risk of unauthorized operations or information disclosure.
What to do: upgrade tensorflow to 2.11.1 or later.
If unsure, ask your IT team or search "tensorflow CVE-2023-25666" on the vendor's site.
CVE-2023-25666 (tensorflow) — CWE-697
Attack vector: remote (network-reachable) / unauthenticated / no user interaction
Patched: 2.11.1 — 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-697) exists in tensorflow.
Check the vendor advisory for the affected versions of tensorflow.
Severity: ?. Risk of unauthorized operations or information disclosure.
Update to 2.11.1.
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
-
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 2.11.1ステージング環境で 2.11.1 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 nvd.nist.gov
- package github.com
- web github.com
- web github.com