tensorflow: Improper Check for Unusual or Exceptional Conditions
CVE-2022-23593 is a high-severity improper check for unusual or exceptional conditions 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: >= 2.7.0, < 2.8.0. The record does not name a fixed version, so check the vendor advisory. There is no confirmed exploitation so far. EPSS estimates a 0.87% chance of exploitation within 30 days, higher than 59% of scored 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 | >= 2.7.0, < 2.8.0 | 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 | 5.9 | Medium | CNA (github.com) |
| CVSS 3.1 | 7.5 | High | NVD |
| CVSS 2.0 | 5.0 | Medium | NVD |
Official description
Tensorflow is an Open Source Machine Learning Framework. The `simplifyBroadcast` function in the MLIR-TFRT infrastructure in TensorFlow is vulnerable to a segfault (hence, denial of service), if called with scalar shapes. If all shapes are scalar, then `maxRank` is 0, so we build an empty `SmallVector`. The fix will be included in TensorFlow 2.8.0. This is the only affected version.
Source: the CVE record as published by its numbering authority, via NVD and CVE.org.
The underlying weakness: Improper Check for Unusual or Exceptional Conditions
The product does not check or incorrectly checks for unusual or exceptional conditions that are not expected to occur frequently during day to day operation of the product.
How developers prevent this class of flaw
- Use a language that does not allow this weakness to occur or provides constructs that make this weakness easier to avoid. Choose languages with features such as exception handling that force the programmer to anticipate unusual conditions that may generate exceptions. Custom exceptions may need to be developed to handle unusual business-logic conditions. Be careful not to pass sensitive exceptions back to the user (CWE-209, CWE-248).
- Check the results of all functions that return a value and verify that the value is expected.
- If using exception handling, catch and throw specific exceptions instead of overly-general exceptions (CWE-396, CWE-397). Catch and handle exceptions as locally as possible so that exceptions do not propagate too far up the call stack (CWE-705). Avoid unchecked or uncaught exceptions where feasible (CWE-248).
More Improper Check for Unusual or Exceptional Conditions vulnerabilities (CWE-754)
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-gwcx-jrx4-92w2Third Party Advisory
Patches and release notes
- github.com/tensorflow/tensorflow/commit/35f0fabb4c178253a964d7aabdbb15c6a398b69aThird Party Advisory
Technical write-ups (may include proof of concept)
- github.com/tensorflow/tensorflow/blob/274df9b02330b790aa8de1cee164b70f72b9b244/tensorflow/compiler/mlir/tfrt/…Exploit, Third Party Advisory
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 Improper Check for Unusual or Exceptional Conditions vulnerabilities
- 7.5 High severity
- 7.5 High severity
- 7.5 High severity
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