CyberSecBulletin
CVE-2022-36005NVD status: Modified

tensorflow: Reachable Assertion

Published Updated By the CyberSecBulletin desk

CVE-2022-36005 is a high-severity reachable assertion 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: < 2.7.2; >= 2.8.0, < 2.8.1; >= 2.9.0, < 2.9.1. 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.

Next patch cycle

High severity with no evidence of exploitation so far.

What to do

  1. Check which version of tensorflow you run against the affected versions below, and install the vendor's latest release.
  2. Read the advisory on github.com for workarounds and exact fixed builds.
  3. Until you can update, restrict network access to the affected service so it is not reachable from untrusted networks.

Affected products and versions

VendorProductAffected versionsFixed in
TensorFlowtensorflow< 2.7.2; >= 2.8.0, < 2.8.1; >= 2.9.0, < 2.9.1Not 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 systemScoreSeverityScored by
CVSS 3.15.9MediumCNA (github.com)
CVSS 3.17.5HighNVD

Official description

TensorFlow is an open source platform for machine learning. When `tf.quantization.fake_quant_with_min_max_vars_gradient` receives input `min` or `max` that is nonscalar, it gives a `CHECK` fail that can trigger a denial of service attack. We have patched the issue in GitHub commit f3cf67ac5705f4f04721d15e485e192bb319feed. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.

Source: the CVE record as published by its numbering authority, via NVD and CVE.org.

The underlying weakness: Reachable Assertion

The product contains an assert() or similar statement that can be triggered by an attacker, which leads to an application exit or other behavior that is more severe than necessary.

How developers prevent this class of flaw

  • If the assertion statement is required (e.g., for security or reliability), change the invoking code to avoid the conditions that trigger the assertion.
  • Consider changing the assertion to some other less fatal error. This might not be easy to implement, since other code may be needed to handle the error.
  • Make sensitive open/close operations non-reachable from directly user-controlled data (e.g. open/close resources)

More Reachable Assertion vulnerabilities (CWE-617)

Timeline

  1. CVE record published.
  2. CISA published its SSVC assessment (exploitation: none, automatable: no, technical impact: partial).
  3. Record last changed materially (score, affected products, status or references).

References

Other vulnerabilities in tensorflow

All tensorflow vulnerabilities

Recent Reachable Assertion vulnerabilities

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