CVE-2020-15213
Description
In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger a denial of service by causing an out of memory allocation in the implementation of segment sum. Since code uses the last element of the tensor holding them to determine the dimensionality of output tensor, attackers can use a very large value to trigger a large allocation. The issue is patched in commit 204945b19e44b57906c9344c0d00120eeeae178a and is released in TensorFlow versions 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to limit the maximum value in the segment ids tensor. This only handles the case when the segment ids are stored statically in the model, but a similar validation could be done if the segment ids are generated at runtime, between inference steps. However, 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.
Predictions
Heuristic predictions, AS-IS, for prioritization only.
Mitigations
No mitigations published for this CVE yet.
The vendor-content worker queues fetches as references arrive (check back in a few minutes). Or — if you've already worked around this in production — publish your fix to the community-verified tier.
✚ Propose a mitigation on Community → Mitigations published via the community go through AI scoring + 2 human reviewers + 7-day silent objection window before landing here withsource_tier=community-verified.
OS impact
Debian Fixed 2 releases
| Version | Status | Fixed in |
|---|---|---|
| sid | Fixed | 0 |
| forky | Fixed | 0 |
Package impact
| Ecosystem | Package | Vulnerable | Fixed |
|---|---|---|---|
| PyPI | tensorflow | >=2.2.0,<2.2.1 | 2.2.1 |
| PyPI | tensorflow | >=2.3.0,<2.3.1 | 2.3.1 |
| PyPI | tensorflow-cpu | >=2.2.0,<2.2.1 | 2.2.1 |
| PyPI | tensorflow-cpu | >=2.3.0,<2.3.1 | 2.3.1 |
| PyPI | tensorflow-gpu | >=2.2.0,<2.2.1 | 2.2.1 |
| PyPI | tensorflow-gpu | >=2.3.0,<2.3.1 | 2.3.1 |
| PyPI | tensorflow-gpu | <204945b19e44b57906c9344c0d00120eeeae178a||>=2.3.0,<2.3.1 | 204945b19e44b57906c9344c0d00120eeeae178a |
| PyPI | tensorflow | <204945b19e44b57906c9344c0d00120eeeae178a||>=2.3.0,<2.3.1 | 204945b19e44b57906c9344c0d00120eeeae178a |
| PyPI | tensorflow-cpu | <204945b19e44b57906c9344c0d00120eeeae178a||>=2.3.0,<2.3.1 | 204945b19e44b57906c9344c0d00120eeeae178a |
References
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hjmq-236j-8m87
- https://nvd.nist.gov/vuln/detail/CVE-2020-15213
- https://github.com/tensorflow/tensorflow/commit/00c7ed7ce81c2126ebc17dfe7073b5c0efd5ec0a
- https://github.com/tensorflow/tensorflow/commit/204945b19e44b57906c9344c0d00120eeeae178a
- https://github.com/tensorflow/tensorflow/commit/a4030d8ba3692c438997c27be2dd95f3d5f54827
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2020-293.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2020-328.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2020-136.yaml
- https://github.com/tensorflow/tensorflow
- https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/lite/kernels/segment_sum.cc#L39-L44
- https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1
- https://security-tracker.debian.org/tracker/CVE-2020-15213
Community-verified mitigations for this CVE will appear above when contributors publish them.
Verify integrity in audit chain (admin only). AS-IS.