Summary
Out of bounds write in tensorflow-lite
Workarounds
A potential workaround would be to add a custom Verifier to the model loading code to ensure that the segment ids are sorted, although this only handles the case when the segment ids are stored statically in the model.
A similar validation could be done if the segment ids are generated at runtime between inference steps.
If the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code.
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 members of the Aivul Team from Qihoo 360.
Impact
In TensorFlow Lite models using segment sum can trigger a write out bounds / segmentation fault if the segment ids are not sorted. Code assumes that the segment ids are in increasing order, using the last element of the tensor holding them to determine the dimensionality of output tensor:
https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/lite/kernels/segment_sum.cc#L39-L44
This results in allocating insufficient memory for the output tensor and in a write outside the bounds of the output array:
https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/lite/kernels/internal/reference/reference_ops.h#L2625-L2631
This usually results in a segmentation fault, but depending on runtime conditions it can provide for a write gadget to be used in future memory corruption-based exploits.
A write operation targets a memory location beyond the intended buffer boundary. Typical impact: memory corruption, crash, or arbitrary code execution.
CVE-2020-15214 has a CVSS score of 8.1 (Critical). The vector is network-reachable, no privileges required, and no user interaction. A CVSS score reflects the worst-case severity of the vulnerability, not your specific exposure. Whether this affects your application depends on whether the vulnerable code is present and reachable in your environment. A fixed version is available (2.2.1, 2.3.1); upgrading removes the vulnerable code path.
Affected versions
Security releases
Kodem intelligence
Severity tells you how bad this could be in the worst case. It does not tell you whether you are exposed. Exploitability and impact are functions of runtime truth: whether the vulnerable code is present, reachable, and actually executes in your application. A vulnerable package can sit in your dependency tree and never run.
Kodem, an Intelligent Application Security platform, uses runtime intelligence to reveal which vulnerabilities actually execute in production, so teams prioritize the ones that genuinely matter. Kodem's runtime-powered SCA identifies whether this CVE is reachable in your applications.
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We have patched the issue in 204945b and will release patch releases for all affected versions.
We recommend users to upgrade to TensorFlow 2.2.1, or 2.3.1.
Frequently Asked Questions
- What is CVE-2020-15214? CVE-2020-15214 is a critical-severity out-of-bounds write vulnerability in tensorflow (pip), affecting versions = 2.2.0. It is fixed in 2.2.1, 2.3.1. A write operation targets a memory location beyond the intended buffer boundary.
- How severe is CVE-2020-15214? CVE-2020-15214 has a CVSS score of 8.1 (Critical). This score reflects the worst-case severity of the vulnerability, not your specific exposure. Whether it represents real risk in your environment depends on whether the vulnerable code is present and reachable.
- Which packages are affected by CVE-2020-15214?
tensorflow(pip) (versions = 2.2.0)tensorflow-cpu(pip) (versions = 2.2.0)tensorflow-gpu(pip) (versions = 2.2.0)
- Is there a fix for CVE-2020-15214? Yes. CVE-2020-15214 is fixed in 2.2.1, 2.3.1. Upgrade to this version or later.
- Is CVE-2020-15214 exploitable, and should I be worried? Whether CVE-2020-15214 is exploitable in your environment depends on whether the vulnerable code is present and reachable. A CVSS score is a worst-case rating; it does not account for your specific deployment, configuration, or usage patterns. Kodem, an Intelligent Application Security platform, uses runtime intelligence to show which vulnerabilities actually execute in production, so you can focus on the ones that represent real risk. Get a demo
- What actually determines whether CVE-2020-15214 is exploitable, and how bad it is? Exploitability and impact are not fixed properties of a CVE. They depend on runtime truth: whether the vulnerable code is present, reachable, and actually executes in your application. A high CVSS score on a dependency that never runs is not the same as real risk. Kodem, an Intelligent Application Security platform, uses runtime intelligence to reveal which vulnerabilities actually execute in production, so teams prioritize the ones that genuinely matter.
- How do I fix CVE-2020-15214?
- Upgrade
tensorflowto 2.2.1 or later - Upgrade
tensorflowto 2.3.1 or later - Upgrade
tensorflow-cputo 2.2.1 or later - Upgrade
tensorflow-cputo 2.3.1 or later - Upgrade
tensorflow-gputo 2.2.1 or later - Upgrade
tensorflow-gputo 2.3.1 or later
- Upgrade