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Not in CISA KEV
HIGH severity

GHSA-247x-2f9f-5wp7

HIGHFix: tensorflow/tensorflow@448a161

GHSA-247x-2f9f-5wp7 is a high-severity (CVSS 7.5) Uncontrolled Resource Consumption vulnerability in tensorflow. O3 Security confirms whether GHSA-247x-2f9f-5wp7 is actually reachable in your code before you act, and blocks exploitation at runtime until you patch.

Stack overflow in TensorFlow

Also known asBIT-tensorflow-2022-23591CVE-2022-23591PYSEC-2022-100PYSEC-2022-155PYSEC-2026-3084
Published
Feb 9, 2022
Updated
Jul 13, 2026
Affected
9 pkgs
Patched
9 / 9
Exploits
None indexed
Exploitation data as of Aug 23, 2026 · OSV.dev, NVD, FIRST.org (EPSS)

Exploitation Status

No confirmed exploitation observed yet

  • CISA assesses this as automatable — exploitation doesn’t require manual, per-target effort, which raises the odds of mass scanning and opportunistic attacks.
  • CISA’s own triage has not observed active exploitation or public proof-of-concept code for this CVE as of its last assessment.

Exploitation and automatability from CISA’s SSVC triage for GHSA-247x-2f9f-5wp7.

EPSS Exploitation Probability

via FIRST.org ↗
0.8%probability of exploitation in next 30 days
Lower Risk0.00%
Lower risk than most CVEs53th percentile — riskier than 53% of all scored CVEsHighest risk

EPSS (Exploit Prediction Scoring System) is a daily probability model maintained by FIRST.org. It estimates the likelihood a CVE will be exploited in production environments within the next 30 days, derived from real-world threat intelligence signals.

How urgent is this, really

GHSA-247x-2f9f-5wp7 plotted by exploitation likelihood (EPSS) against impact (CVSS). The shaded corner — EPSS 50%+ and CVSS 7.0+ — is where this CVE doesn't sit, though severity or exploitability alone can still warrant action.

Where this sits among everything scored

Of 365,017 CVEs with a current EPSS score, this one falls in the < 10% band (highlighted). Real counts from FIRST.org, not a sample — log-scaled since the landscape is heavily right-skewed.

Real-World Exposure

9 pkgs affected
🐍tensorflow🐍tensorflow🐍tensorflow🐍tensorflow-cpu🐍tensorflow-cpu🐍tensorflow-cpu🐍tensorflow-gpu🐍tensorflow-gpu+1 more

Real-time download stats are indexed for npm and PyPI packages. This vulnerability affects PyPI packages — download data is not available via public APIs for these ecosystems.

Description

Impact

The GraphDef format in TensorFlow does not allow self recursive functions. The runtime assumes that this invariant is satisfied. However, a GraphDef containing a fragment such as the following can be consumed when loading a SavedModel:

  library {
    function {
      signature {
        name: "SomeOp"
        description: "Self recursive op"
      }
      node_def {
        name: "1"
        op: "SomeOp"
      }
      node_def {
        name: "2"
        op: "SomeOp"
      }
    }
  } 

This would result in a stack overflow during execution as resolving each NodeDef means resolving the function itself and its nodes.

Patches

We have patched the issue in GitHub commit 448a16182065bd08a202d9057dd8ca541e67996c.

The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

For more information

Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.

Affected Packages

9 total 9 fixed
EcosystemPackageVulnerable rangeFix
🐍PyPItensorflowall versions2.5.3
🐍PyPItensorflow2.6.0&&< 2.6.32.6.3
🐍PyPItensorflow2.7.0&&< 2.7.12.7.1
🐍PyPItensorflow-cpuall versions2.5.3
🐍PyPItensorflow-cpu2.6.0&&< 2.6.32.6.3
🐍PyPItensorflow-cpu2.7.0&&< 2.7.12.7.1

Detection & mitigation playbook

Open-source dependency
  1. Detect

    Scan your dependency tree (package-lock.json, pnpm-lock.yaml, requirements.txt, go.sum, etc.) for tensorflow. O3's reachability analysis confirms whether the vulnerable code path is actually invoked in your application, so you act on real exposure instead of every transitive match.

  2. Fix

    Update tensorflow to 2.5.3 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-247x-2f9f-5wp7 is resolved across your whole dependency graph.

  3. Workarounds

    If you can't upgrade right away: gate or disable the affected feature, validate untrusted input at the boundary, and avoid passing attacker-controlled data into the vulnerable path. O3's runtime protection blocks exploitation in production as an interim safeguard until the upgrade lands.

  4. How O3 protects you

    O3 pinpoints whether GHSA-247x-2f9f-5wp7 is reachable in your code and exactly where to fix it, then blocks exploitation in production at runtime until the patched version is deployed.

Tailored to GHSA-247x-2f9f-5wp7. Runtime protection reduces exposure until a permanent patch is applied and verified — it complements patching, it doesn't replace it.

Frequently Asked Questions

### Impact The `GraphDef` format in TensorFlow does not allow self recursive functions. The runtime assumes that this invariant is satisfied. However, a `GraphDef` containing a fragment such as the following can be consumed when loading a `SavedModel`: ``` library { function { signature { name: "SomeOp" description: "Self recursive op" } node_def { name: "1" op: "SomeOp" } node_def { name: "2" op: "SomeOp" } } } ``` This would result in a stack overflow during execution as resolving each `NodeDef
O3 Security · Impact-Aware SCA

Is GHSA-247x-2f9f-5wp7 in your dependencies?

O3 detects GHSA-247x-2f9f-5wp7 across PyPI dependencies and uses function-level reachability to confirm whether the vulnerable code path is actually reachable — not just present. No false positives.

GHSA-247x-2f9f-5wp7: Stack overflow in… | O3 Security