CVE-2022-23592 is a high-severity (CVSS 8.1) Out-of-bounds Read vulnerability in tensorflow. 1 public exploit reference exists, so weaponization risk is real. A fix is available for tensorflow — see the affected versions and patch details below.
Out of bounds read in Tensorflow
Exploitation Status
Proof-of-concept exploit code exists
- CISA’s SSVC triage found public proof-of-concept exploit code for this CVE, though no confirmed active exploitation.
Exploitation and automatability from CISA’s SSVC triage for CVE-2022-23592.
EPSS Exploitation Probability
Probability of exploitation in the next 30 days, from FIRST.org EPSS.
How urgent is this, really
CVE-2022-23592 by exploitation likelihood (EPSS) against impact (CVSS). Outside the shaded patch-first corner.
Where this sits among everything scored
Of 385,386 CVEs with a current EPSS score, this one falls in the < 10% band (highlighted). Counts from FIRST.org, log-scaled.
Real-World Exposure
tensorflow🐍tensorflow-cpu🐍tensorflow-gpuReal-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
TensorFlow's type inference can cause a heap OOB read as the bounds checking is done in a DCHECK (which is a no-op during production):
if (node_t.type_id() != TFT_UNSET) {
int ix = input_idx[i];
DCHECK(ix < node_t.args_size())
<< "input " << i << " should have an output " << ix
<< " but instead only has " << node_t.args_size()
<< " outputs: " << node_t.DebugString();
input_types.emplace_back(node_t.args(ix));
// ...
}
An attacker can control input_idx such that ix would be larger than the number of values in node_t.args.
Patches
We have patched the issue in GitHub commit c99d98cd189839dcf51aee94e7437b54b31f8abd.
The fix will be included in TensorFlow 2.8.0. This is the only affected version.
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
| Ecosystem | Package | Vulnerable range | Fix |
|---|---|---|---|
| 🐍PyPI | tensorflow | ≥ 2.8.0-rc0&&< 2.8.0 | 2.8.0pip install --upgrade 'tensorflow==2.8.0' |
| 🐍PyPI | tensorflow-cpu | ≥ 2.8.0-rc0&&< 2.8.0 | 2.8.0pip install --upgrade 'tensorflow-cpu==2.8.0' |
| 🐍PyPI | tensorflow-gpu | ≥ 2.8.0-rc0&&< 2.8.0 | 2.8.0pip install --upgrade 'tensorflow-gpu==2.8.0' |
Affected Products
tensorflowgoogleResearch use only. For defensive security, authorized penetration testing, and academic research only. Never execute exploit code against systems without explicit written authorization.
Detection & mitigation playbook
Open-source dependencyDetect
Scan your dependency tree (package-lock.json, pnpm-lock.yaml, requirements.txt, go.sum, etc.) for tensorflow, including transitive dependencies — a direct dependency you never call can still pull in a vulnerable version.
Fix
Update tensorflow to 2.8.0 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms CVE-2022-23592 is resolved across your whole dependency graph.
Workarounds
Constrain what reaches the vulnerable code: limit the size and shape of untrusted input, isolate the affected component in a sandboxed or least-privileged process, and enable the platform's memory-safety mitigations (ASLR, stack protector, hardened allocator) so an out-of-bounds access is more likely to fail closed than to be exploitable.
Frequently Asked Questions
Is CVE-2022-23592 in your dependencies?
Find it across PyPI, including transitive dependencies.