GHSA-hrg5-737c-2p56 is a medium-severity (CVSS 5.5) Improper Input Validation 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.
Missing validation causes denial of service via `UnsortedSegmentJoin`
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 GHSA-hrg5-737c-2p56.
EPSS Exploitation Probability
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-hrg5-737c-2p56 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 376,715 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
tensorflow🐍tensorflow🐍tensorflow🐍tensorflow-cpu🐍tensorflow-cpu🐍tensorflow-cpu🐍tensorflow-gpu🐍tensorflow-gpu+1 moreReal-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 implementation of tf.raw_ops.UnsortedSegmentJoin does not fully validate the input arguments. This results in a CHECK-failure which can be used to trigger a denial of service attack:
import tensorflow as tf
tf.raw_ops.UnsortedSegmentJoin(
inputs=tf.constant("this", shape=[12], dtype=tf.string),
segment_ids=tf.constant(0, shape=[12], dtype=tf.int64),
num_segments=tf.constant(0, shape=[12], dtype=tf.int64))
The code assumes num_segments is a scalar but there is no validation for this before accessing its value:
const Tensor& num_segments_tensor = context->input(2);
OP_REQUIRES(context, num_segments_tensor.NumElements() != 0,
errors::InvalidArgument("Number of segments cannot be empty."));
auto num_segments = num_segments_tensor.scalar<NUM_SEGMENTS_TYPE>()();
Patches
We have patched the issue in GitHub commit 13d38a07ce9143e044aa737cfd7bb759d0e9b400.
The fix will be included in TensorFlow 2.9.0. We will also cherrypick this commit on TensorFlow 2.8.1, TensorFlow 2.7.2, and TensorFlow 2.6.4, 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.
Attribution
This vulnerability has been reported by Neophytos Christou from Secure Systems Lab at Brown University.
Affected Packages
| Ecosystem | Package | Vulnerable range | Fix |
|---|---|---|---|
| 🐍PyPI | tensorflow | all versions | 2.6.4pip install --upgrade 'tensorflow==2.6.4' |
| 🐍PyPI | tensorflow | ≥ 2.7.0&&< 2.7.2 | 2.7.2pip install --upgrade 'tensorflow==2.7.2' |
| 🐍PyPI | tensorflow | ≥ 2.8.0&&< 2.8.1 | 2.8.1pip install --upgrade 'tensorflow==2.8.1' |
| 🐍PyPI | tensorflow-cpu | all versions | 2.6.4pip install --upgrade 'tensorflow-cpu==2.6.4' |
| 🐍PyPI | tensorflow-cpu | ≥ 2.7.0&&< 2.7.2 | 2.7.2pip install --upgrade 'tensorflow-cpu==2.7.2' |
| 🐍PyPI | tensorflow-cpu | ≥ 2.8.0&&< 2.8.1 | 2.8.1pip install --upgrade 'tensorflow-cpu==2.8.1' |
Research 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 2.6.4 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-hrg5-737c-2p56 is resolved across your whole dependency graph.
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.
How O3 protects you
O3 Security's impact-aware SCA analyses which vulnerable code paths your application actually calls, so a match like GHSA-hrg5-737c-2p56 can be triaged on real exposure rather than presence alone.
Tailored to GHSA-hrg5-737c-2p56. Runtime protection reduces exposure until a permanent patch is applied and verified — it complements patching, it doesn't replace it.
Frequently Asked Questions
Is GHSA-hrg5-737c-2p56 in your dependencies?
O3 Security finds GHSA-hrg5-737c-2p56 across PyPI dependencies, including transitive ones, and its impact-aware SCA ranks findings by whether your code actually calls the vulnerable path.