tensorflow: Incorrect Calculation of Buffer Size
CVE-2022-41887 is a high-severity incorrect calculation of buffer size vulnerability in tensorflow, with a CVSS 3.1 base score of 7.5 out of 10. It can be exploited remotely over the network by an unauthenticated attacker, without any action from a user. Affected tensorflow versions: >= 2.10.0, < 2.10.1; < 2.9.3. The record does not name a fixed version, so check the vendor advisory. There is no confirmed exploitation so far. EPSS estimates a 0.49% chance of exploitation within 30 days, which is low compared with other CVEs.
High severity with no evidence of exploitation so far.
What to do
- Check which version of tensorflow you run against the affected versions below, and install the vendor's latest release.
- Read the advisory on github.com for workarounds and exact fixed builds.
- Until you can update, restrict network access to the affected service so it is not reachable from untrusted networks.
Affected products and versions
| Vendor | Product | Affected versions | Fixed in |
|---|---|---|---|
| TensorFlow | tensorflow | >= 2.10.0, < 2.10.1; < 2.9.3 | Not stated |
Version data comes from the organisation that assigned the CVE. Confirm against the vendor advisory before deciding you are unaffected.
How the severity score breaks down
- Attack vector
- NetworkCan be exploited remotely over a network, including the internet.
- Attack complexity
- LowNo special conditions are needed; the attack works reliably.
- Privileges required
- NoneNo account or login is needed.
- User interaction
- NoneNo action by a victim is required.
- Scope
- UnchangedImpact stays within the vulnerable component.
- Confidentiality
- NoneNo data is disclosed.
- Integrity
- NoneNothing can be modified.
- Availability
- HighThe component can be taken down completely.
Vector: CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H. Scored by NVD.
| Scoring system | Score | Severity | Scored by |
|---|---|---|---|
| CVSS 3.1 | 4.8 | Medium | CNA (github.com) |
| CVSS 3.1 | 7.5 | High | NVD |
Official description
TensorFlow is an open source platform for machine learning. `tf.keras.losses.poisson` receives a `y_pred` and `y_true` that are passed through `functor::mul` in `BinaryOp`. If the resulting dimensions overflow an `int32`, TensorFlow will crash due to a size mismatch during broadcast assignment. We have patched the issue in GitHub commit c5b30379ba87cbe774b08ac50c1f6d36df4ebb7c. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1 and 2.9.3, as these are also affected and still in supported range. However, we will not cherrypick this commit into TensorFlow 2.8.x, as it depends on Eigen behavior that changed between 2.8 and 2.9.
Source: the CVE record as published by its numbering authority, via NVD and CVE.org.
The underlying weakness: Incorrect Calculation of Buffer Size
The product does not correctly calculate the size to be used when allocating a buffer.
How developers prevent this class of flaw
- When allocating a buffer for the purpose of transforming, converting, or encoding an input, allocate enough memory to handle the largest possible encoding. For example, in a routine that converts & characters to & for HTML entity encoding, the output buffer needs to be at least 5 times as large as the input buffer.
- Understand the programming language's underlying representation and how it interacts with numeric calculation (CWE-681). Pay close attention to byte size discrepancies, precision, signed/unsigned distinctions, truncation, conversion and casting between types, not-a-number calculations, and how the language handles numbers that are too large or too small for its underlying representation. Also be careful to account for 32-bit, 64-bit, and other potential differences that may affect the numeric representation.
- Perform input validation on any numeric input by ensuring that it is within the expected range. Enforce that the input meets both the minimum and maximum requirements for the expected range.
More Incorrect Calculation of Buffer Size vulnerabilities (CWE-131)
Timeline
- CVE record published.
- CISA published its SSVC assessment (exploitation: poc, automatable: no, technical impact: partial).
- Record last changed materially (score, affected products, status or references).
References
Advisories
- github.com/tensorflow/tensorflow/security/advisories/GHSA-8fvv-46hw-vpg3Exploit, Third Party Advisory
Patches and release notes
- github.com/tensorflow/tensorflow/commit/c5b30379ba87cbe774b08ac50c1f6d36df4ebb7cThird Party Advisory
Third-party and government advisories
Other vulnerabilities in tensorflow
- 7.8 High severity
- 7.5 High severity
- 9.8 Critical severity
- 7.5 High severity
- 7.8 High severity
- 7.5 High severity
Recent Incorrect Calculation of Buffer Size vulnerabilities
- 7.5 High severity
- 7.5 High severity
- 7.5 High severity
- 7.8 High severity
- 7.8 High severity
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