CVE-2020-15206 is a high-severity (CVSS 7.5) 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.
Denial of Service in Tensorflow
EPSS Exploitation Probability
Probability of exploitation in the next 30 days, from FIRST.org EPSS.
How urgent is this, really
CVE-2020-15206 by exploitation likelihood (EPSS) against impact (CVSS). Outside the shaded patch-first corner.
Where this sits among everything scored
Of 385,738 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🐍tensorflow🐍tensorflow🐍tensorflow🐍tensorflow-cpu🐍tensorflow-cpu🐍tensorflow-cpu+7 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
Changing the TensorFlow's SavedModel protocol buffer and altering the name of required keys results in segfaults and data corruption while loading the model. This can cause a denial of service in products using tensorflow-serving or other inference-as-a-service installments.
We have added fixes to this in f760f88b4267d981e13f4b302c437ae800445968 and fcfef195637c6e365577829c4d67681695956e7d (both going into TensorFlow 2.2.0 and 2.3.0 but not yet backported to earlier versions). However, this was not enough, as #41097 reports a different failure mode.
Patches
We have patched the issue in adf095206f25471e864a8e63a0f1caef53a0e3a6 and will release patch releases for all versions between 1.15 and 2.3. Patch releases for versions between 1.15 and 2.1 will also contain cherry-picks of f760f88b4267d981e13f4b302c437ae800445968 and fcfef195637c6e365577829c4d67681695956e7d.
We recommend users to upgrade to TensorFlow 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.
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 Shuaike Dong, from Alipay Tian Qian Security Lab && Lab for Applied Security Research, CUHK.
Affected Packages
| Ecosystem | Package | Vulnerable range | Fix |
|---|---|---|---|
| 🐍PyPI | tensorflow | all versions | 1.15.4pip install --upgrade 'tensorflow==1.15.4' |
| 🐍PyPI | tensorflow | ≥ 2.0.0&&< 2.0.3 | 2.0.3pip install --upgrade 'tensorflow==2.0.3' |
| 🐍PyPI | tensorflow | ≥ 2.1.0&&< 2.1.2 | 2.1.2pip install --upgrade 'tensorflow==2.1.2' |
| 🐍PyPI | tensorflow | ≥ 2.2.0&&< 2.2.1 | 2.2.1pip install --upgrade 'tensorflow==2.2.1' |
| 🐍PyPI | tensorflow | ≥ 2.3.0&&< 2.3.1 | 2.3.1pip install --upgrade 'tensorflow==2.3.1' |
| 🐍PyPI | tensorflow-cpu | all versions | 1.15.4pip install --upgrade 'tensorflow-cpu==1.15.4' |
Affected Products
tensorflowgoogleleapopensuseResearch 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 1.15.4 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms CVE-2020-15206 is resolved across your whole dependency graph.
Workarounds
Cap what an attacker can consume: apply request size, rate and timeout limits in front of the affected component, and run it with memory and CPU limits so exhaustion degrades one worker rather than the whole service.
Frequently Asked Questions
Is CVE-2020-15206 in your dependencies?
Find it across PyPI, including transitive dependencies.