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GHSA-h6fg-mjxg-hqq4 BIT-tensorflow-2020-15202 CVE-2020-15202 PYSEC-2020-125 PYSEC-2020-282 PYSEC-2020-317

Integer truncation in Shard API usage

Critical PyPI
Severity
0.0 / 10
Critical · CVSS 4.0
Exploit chance (EPSS)
1.29%
Chance of exploitation in the next 30 days. Higher than 70.17% of scored vulnerabilities.
Known exploited
No
Not in CISA's catalog
Weakness
CWE-197 Numeric Truncation Error
CWE-754 Improper Check for Unusual or Exceptional Conditions
Fix
Available
3 packages have a fixed version

Description

Impact

The Shard API in TensorFlow expects the last argument to be a function taking two int64 (i.e., long long) arguments: https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/core/util/work_sharder.h#L59-L60

However, there are several places in TensorFlow where a lambda taking int or int32 arguments is being used: https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/core/kernels/random_op.cc#L204-L205 https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/core/kernels/random_op.cc#L317-L318

In these cases, if the amount of work to be parallelized is large enough, integer truncation occurs. Depending on how the two arguments of the lambda are used, this can result in segfaults, read/write outside of heap allocated arrays, stack overflows, or data corruption.

Patches

We have patched the issue in 27b417360cbd671ef55915e4bb6bb06af8b8a832 and ca8c013b5e97b1373b3bb1c97ea655e69f31a575. We will release patch releases for all versions between 1.15 and 2.3.

We recommend users to upgrade to TensorFlow 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.

For more information

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

Attribution

This vulnerability has been reported by members of the Aivul Team from Qihoo 360.

Affected versions

PyPI

PackageVulnerableFixed in
tensorflow
< 1.15.4
1.15.4
tensorflow
>= 2.0.0, < 2.0.3
2.0.3
tensorflow
>= 2.1.0, < 2.1.2
2.1.2
tensorflow
>= 2.2.0, < 2.2.1
2.2.1
tensorflow
>= 2.3.0, < 2.3.1
2.3.1
tensorflow
< 1.15.4
>= 2.0.0, < 2.0.3
>= 2.1.0, < 2.1.2
>= 2.2.0, < 2.2.1
>= 2.3.0, < 2.3.1
1.15.4
2.0.3
2.1.2
2.2.1
2.3.1
tensorflow-cpu
< 1.15.4
1.15.4
tensorflow-cpu
>= 2.0.0, < 2.0.3
2.0.3
tensorflow-cpu
>= 2.1.0, < 2.1.2
2.1.2
tensorflow-cpu
>= 2.2.0, < 2.2.1
2.2.1
tensorflow-cpu
>= 2.3.0, < 2.3.1
2.3.1
tensorflow-cpu
< 1.15.4
>= 2.0.0, < 2.0.3
>= 2.1.0, < 2.1.2
>= 2.2.0, < 2.2.1
>= 2.3.0, < 2.3.1
1.15.4
2.0.3
2.1.2
2.2.1
2.3.1
tensorflow-gpu
< 1.15.4
1.15.4
tensorflow-gpu
>= 2.0.0, < 2.0.3
2.0.3
tensorflow-gpu
>= 2.1.0, < 2.1.2
2.1.2
tensorflow-gpu
>= 2.2.0, < 2.2.1
2.2.1
tensorflow-gpu
>= 2.3.0, < 2.3.1
2.3.1
tensorflow-gpu
< 1.15.4
>= 2.0.0, < 2.0.3
>= 2.1.0, < 2.1.2
>= 2.2.0, < 2.2.1
>= 2.3.0, < 2.3.1
1.15.4
2.0.3
2.1.2
2.2.1
2.3.1

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 → 1.15.4

pip
pip install --upgrade "tensorflow>=1.15.4"
poetry
poetry add "tensorflow>=1.15.4"
uv
uv add "tensorflow>=1.15.4"

tensorflow-cpu PyPI → 1.15.4

pip
pip install --upgrade "tensorflow-cpu>=1.15.4"
poetry
poetry add "tensorflow-cpu>=1.15.4"
uv
uv add "tensorflow-cpu>=1.15.4"

tensorflow-gpu PyPI → 1.15.4

pip
pip install --upgrade "tensorflow-gpu>=1.15.4"
poetry
poetry add "tensorflow-gpu>=1.15.4"
uv
uv add "tensorflow-gpu>=1.15.4"

Severity breakdown

CVSS 4.0 0.0

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

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

CVSS 3.1 9.0

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

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

Weakness

CWE-197: Numeric Truncation Error

Truncation errors occur when a primitive is cast to a primitive of a smaller size and data is lost in the conversion.

How to prevent it
  • Implementation: Ensure that no casts, implicit or explicit, take place that move from a larger size primitive or a smaller size primitive.

CWE-754: 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 to prevent it
  • Requirements: 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).
  • Implementation: Check the results of all functions that return a value and verify that the value is expected.
  • Implementation: 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).
  • Implementation: Ensure that error messages only contain minimal details that are useful to the intended audience and no one else. The messages need to strike the balance between being too cryptic (which can confuse users) or being too detailed (which may reveal more than intended). The messages should not reveal the methods that were used to determine the error. Attackers can use detailed information to refine or optimize their original attack, thereby increasing their chances of success. If errors must be captured in some detail, record them in log messages, but consider what could occur if the log messages can be viewed by attackers. Highly sensitive information such as passwords should never be saved to log files. Avoid inconsistent messaging that might accidentally tip off an attacker about internal state, such as whether a user account exists or not. Exposing additional information to a potential attacker in the context of an exceptional condition can help the attacker determine what attack vectors are most likely to succeed beyond DoS.
  • Implementation: Assume all input is malicious. Use an "accept known good" input validation strategy, i.e., use a list of acceptable inputs that strictly conform to specifications. Reject any input that does not strictly conform to specifications, or transform it into something that does. When performing input validation, consider all potentially relevant properties, including length, type of input, the full range of acceptable values, missing or extra inputs, syntax, consistency across related fields, and conformance to business rules. As an example of business rule logic, "boat" may be syntactically valid because it only contains alphanumeric characters, but it is not valid if the input is only expected to contain colors such as "red" or "blue." Do not rely exclusively on looking for malicious or malformed inputs. This is likely to miss at least one undesirable input, especially if the code's environment changes. This can give attackers enough room to bypass the intended validation. However, denylists can be useful for detecting potential attacks or determining which inputs are so malformed that they should be rejected outright.
  • Architecture and Design, Implementation: If the program must fail, ensure that it fails gracefully (fails closed). There may be a temptation to simply let the program fail poorly in cases such as low memory conditions, but an attacker may be able to assert control before the software has fully exited. Alternately, an uncontrolled failure could cause cascading problems with other downstream components; for example, the program could send a signal to a downstream process so the process immediately knows that a problem has occurred and has a better chance of recovery.
  • Architecture and Design: Use system limits, which should help to prevent resource exhaustion. However, the product should still handle low resource conditions since they may still occur.

References

Advisories

Packages

Other links

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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