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GHSA-247x-2f9f-5wp7 BIT-tensorflow-2022-23591 CVE-2022-23591 PYSEC-2022-100 PYSEC-2022-155 PYSEC-2026-3084

Stack overflow in TensorFlow

High PyPI
Severity
0.0 / 10
High · CVSS 4.0
Exploit chance (EPSS)
0.80%
Chance of exploitation in the next 30 days. Higher than 56.44% of scored vulnerabilities.
Known exploited
No
Not in CISA's catalog
Weakness
CWE-400 Uncontrolled Resource Consumption
CWE-674 Uncontrolled Recursion
Fix
Available
3 packages have a fixed version

Description

Impact

The GraphDef format in TensorFlow does not allow self recursive functions. The runtime assumes that this invariant is satisfied. However, a GraphDef containing a fragment such as the following can be consumed when loading a SavedModel:

  library {
    function {
      signature {
        name: "SomeOp"
        description: "Self recursive op"
      }
      node_def {
        name: "1"
        op: "SomeOp"
      }
      node_def {
        name: "2"
        op: "SomeOp"
      }
    }
  } 

This would result in a stack overflow during execution as resolving each NodeDef means resolving the function itself and its nodes.

Patches

We have patched the issue in GitHub commit 448a16182065bd08a202d9057dd8ca541e67996c.

The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

For more information

Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.

Affected versions

PyPI

PackageVulnerableFixed in
tensorflow
< 2.5.3
2.5.3
tensorflow
>= 2.6.0, < 2.6.3
2.6.3
tensorflow
>= 2.7.0, < 2.7.1
2.7.1
tensorflow
< 2.5.3
>= 2.6.0, < 2.6.3
>= 2.7.0, < 2.7.1
2.5.3
2.6.3
2.7.1
tensorflow-cpu
< 2.5.3
2.5.3
tensorflow-cpu
>= 2.6.0, < 2.6.3
2.6.3
tensorflow-cpu
>= 2.7.0, < 2.7.1
2.7.1
tensorflow-cpu
< 2.5.3
>= 2.6.0, < 2.6.3
2.5.3
2.6.3
tensorflow-gpu
< 2.5.3
2.5.3
tensorflow-gpu
>= 2.6.0, < 2.6.3
2.6.3
tensorflow-gpu
>= 2.7.0, < 2.7.1
2.7.1
tensorflow-gpu
< 2.5.3
>= 2.6.0, < 2.6.3
2.5.3
2.6.3

How to fix

Move to the fixed version or later with your package manager, update the lockfile, and run your tests. If the package only arrives through another dependency, upgrade that dependency first. If none of its releases pulls a fixed version yet, force the version with your package manager's override.

tensorflow PyPI → 2.5.3

pip
pip install --upgrade "tensorflow>=2.5.3"
poetry
poetry add "tensorflow>=2.5.3"
uv
uv add "tensorflow>=2.5.3"

tensorflow-cpu PyPI → 2.5.3

pip
pip install --upgrade "tensorflow-cpu>=2.5.3"
poetry
poetry add "tensorflow-cpu>=2.5.3"
uv
uv add "tensorflow-cpu>=2.5.3"

tensorflow-gpu PyPI → 2.5.3

pip
pip install --upgrade "tensorflow-gpu>=2.5.3"
poetry
poetry add "tensorflow-gpu>=2.5.3"
uv
uv add "tensorflow-gpu>=2.5.3"

Severity breakdown

CVSS 4.0 0.0

CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:N/VI:N/VA:H/SC:N/SI:N/SA:N

Attack vector
Network
Attack complexity
Low
Attack requirements
None
Privileges required
Low
User interaction
None
Confidentiality (vulnerable system)
None
Integrity (vulnerable system)
None
Availability (vulnerable system)
High
Confidentiality (subsequent systems)
None
Integrity (subsequent systems)
None
Availability (subsequent systems)
None

CVSS 3.1 7.5

CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H

Attack vector
Network
Attack complexity
Low
Privileges required
None
User interaction
None
Scope
Unchanged
Confidentiality
None
Integrity
None
Availability
High

Weakness

CWE-400: Uncontrolled Resource Consumption

The product does not properly control the allocation and maintenance of a limited resource.

How to prevent it
  • Architecture and Design: Design throttling mechanisms into the system architecture. The best protection is to limit the amount of resources that an unauthorized user can cause to be expended. A strong authentication and access control model will help prevent such attacks from occurring in the first place. The login application should be protected against DoS attacks as much as possible. Limiting the database access, perhaps by caching result sets, can help minimize the resources expended. To further limit the potential for a DoS attack, consider tracking the rate of requests received from users and blocking requests that exceed a defined rate threshold.
  • Architecture and Design: Mitigation of resource exhaustion attacks requires that the target system either: recognizes the attack and denies that user further access for a given amount of time, or uniformly throttles all requests in order to make it more difficult to consume resources more quickly than they can again be freed. The first of these solutions is an issue in itself though, since it may allow attackers to prevent the use of the system by a particular valid user. If the attacker impersonates the valid user, they may be able to prevent the user from accessing the server in question. The second solution is simply difficult to effectively institute -- and even when properly done, it does not provide a full solution. It simply makes the attack require more resources on the part of the attacker.
  • Architecture and Design: Ensure that protocols have specific limits of scale placed on them.
  • Implementation: Ensure that all failures in resource allocation place the system into a safe posture.

CWE-674: Uncontrolled Recursion

The product does not properly control the amount of recursion that takes place, consuming excessive resources, such as allocated memory or the program stack.

How to prevent it
  • Implementation: Ensure that an end condition will be reached under all logic conditions. The end condition may include checking against the depth of recursion and exiting with an error if the recursion goes too deep. The complexity of the end condition contributes to the effectiveness of this action.
  • Implementation: Increase the stack size.

References

Timeline

  1. Published
  2. Last updated by the source
  3. Latest EPSS score
AdvisorySeverityWhy it's related
GHSA-gjh7-xx4r-x345
TensorFlow has segfault in array_ops.upper_bound
High Also affects tensorflow
GHSA-fxgc-95xx-grvq
TensorFlow Denial of Service vulnerability
Medium Also affects tensorflow
GHSA-93vr-9q9m-pj8p
TensorFlow vulnerable to Out-of-Bounds Read in DynamicStitch
High Also affects tensorflow
GHSA-qjqc-vqcf-5qvj
TensorFlow vulnerable to seg fault in `tf.raw_ops.Print`
High Also affects tensorflow
GHSA-7jvm-xxmr-v5cw
TensorFlow vulnerable to integer overflow in EditDistance
High Also affects tensorflow
GHSA-64jg-wjww-7c5w
TensorFlow has Null Pointer Error in TensorArrayConcatV2
High Also affects tensorflow
GHSA-6hg6-5c2q-7rcr
TensorFlow has Heap-buffer-overflow in AvgPoolGrad
High Also affects tensorflow
GHSA-558h-mq8x-7q9g
TensorFlow has Null Pointer Error in SparseSparseMaximum
High Also affects tensorflow
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