GHSA-mq5c-prh3-3f3h BIT-tensorflow-2021-29610 CVE-2021-29610 PYSEC-2021-247 PYSEC-2021-538 PYSEC-2021-736

Invalid validation in `QuantizeAndDequantizeV2`

Low PyPI

Severity: 0.0 / 10; Low · CVSS 4.0
Exploit chance (EPSS): 0.20%; Chance of exploitation in the next 30 days. Higher than 10.02% of scored vulnerabilities.
Known exploited: No; Not in CISA's catalog
Weakness: CWE-665 Improper Initialization; CWE-787 Out-of-bounds Write
Fix: Available; 3 packages have a fixed version

Description

Impact

The validation in tf.raw_ops.QuantizeAndDequantizeV2 allows invalid values for axis argument:

import tensorflow as tf

input_tensor = tf.constant([0.0], shape=[1], dtype=float)
input_min = tf.constant(-10.0)
input_max = tf.constant(-10.0)

tf.raw_ops.QuantizeAndDequantizeV2(
  input=input_tensor, input_min=input_min, input_max=input_max,
  signed_input=False, num_bits=1, range_given=False, round_mode='HALF_TO_EVEN',
  narrow_range=False, axis=-2)

The validation uses || to mix two different conditions:

OP_REQUIRES(ctx,
  (axis_ == -1 || axis_ < input.shape().dims()),
  errors::InvalidArgument(...));

If axis_ < -1 the condition in OP_REQUIRES will still be true, but this value of axis_ results in heap underflow. This allows attackers to read/write to other data on the heap.

Patches

We have patched the issue in GitHub commit c5b0d5f8ac19888e46ca14b0e27562e7fbbee9a9.

The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, 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.

Attribution

This vulnerability has been reported by Yakun Zhang and Ying Wang of Baidu X-Team.

Affected versions

PyPI

Package	Vulnerable	Fixed in
tensorflow	`< 2.1.4`	`2.1.4`
tensorflow	`>= 2.2.0, < 2.2.3`	`2.2.3`
tensorflow	`>= 2.3.0, < 2.3.3`	`2.3.3`
tensorflow	`>= 2.4.0, < 2.4.2`	`2.4.2`
tensorflow	`< 2.2.0rc0` `>= 2.2.0, < 2.3.0rc0` `>= 2.3.0, < 2.3.4` `>= 2.4.0, < 2.4.3`	`2.2.0rc0` `2.3.0rc0` `2.3.4` `2.4.3`
tensorflow-cpu	`< 2.1.4`	`2.1.4`
tensorflow-cpu	`>= 2.2.0, < 2.2.3`	`2.2.3`
tensorflow-cpu	`>= 2.3.0, < 2.3.3`	`2.3.3`
tensorflow-cpu	`>= 2.4.0, < 2.4.2`	`2.4.2`
tensorflow-cpu	`< 2.2.0rc0` `>= 2.2.0, < 2.3.0rc0` `>= 2.3.0, < 2.3.4` `>= 2.4.0, < 2.4.3`	`2.2.0rc0` `2.3.0rc0` `2.3.4` `2.4.3`
tensorflow-gpu	`< 2.1.4`	`2.1.4`
tensorflow-gpu	`>= 2.2.0, < 2.2.3`	`2.2.3`
tensorflow-gpu	`>= 2.3.0, < 2.3.3`	`2.3.3`
tensorflow-gpu	`>= 2.4.0, < 2.4.2`	`2.4.2`
tensorflow-gpu	`< 2.2.0rc0` `>= 2.2.0, < 2.3.0rc0` `>= 2.3.0, < 2.3.4` `>= 2.4.0, < 2.4.3`	`2.2.0rc0` `2.3.0rc0` `2.3.4` `2.4.3`

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 → `2.1.4`

pip

pip install --upgrade "tensorflow>=2.1.4"

poetry

poetry add "tensorflow>=2.1.4"

uv

uv add "tensorflow>=2.1.4"

tensorflow-cpu PyPI → `2.1.4`

pip

pip install --upgrade "tensorflow-cpu>=2.1.4"

poetry

poetry add "tensorflow-cpu>=2.1.4"

uv

uv add "tensorflow-cpu>=2.1.4"

tensorflow-gpu PyPI → `2.1.4`

pip

pip install --upgrade "tensorflow-gpu>=2.1.4"

poetry

poetry add "tensorflow-gpu>=2.1.4"

uv

uv add "tensorflow-gpu>=2.1.4"

Severity breakdown

CVSS 4.0 0.0

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

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

CVSS 3.1 3.6

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

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

Weakness

CWE-665: Improper Initialization

The product does not initialize or incorrectly initializes a resource, which might leave the resource in an unexpected state when it is accessed or used.

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. For example, in Java, if the programmer does not explicitly initialize a variable, then the code could produce a compile-time error (if the variable is local) or automatically initialize the variable to the default value for the variable's type. In Perl, if explicit initialization is not performed, then a default value of undef is assigned, which is interpreted as 0, false, or an equivalent value depending on the context in which the variable is accessed.
Architecture and Design: Identify all variables and data stores that receive information from external sources, and apply input validation to make sure that they are only initialized to expected values.
Implementation: Explicitly initialize all your variables and other data stores, either during declaration or just before the first usage.
Implementation: Pay close attention to complex conditionals that affect initialization, since some conditions might not perform the initialization.
Implementation: Avoid race conditions (CWE-362) during initialization routines.
Build and Compilation: Run or compile your product with settings that generate warnings about uninitialized variables or data.

CWE-787: Out-of-bounds Write

The product writes data past the end, or before the beginning, of the intended buffer.

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. For example, many languages that perform their own memory management, such as Java and Perl, are not subject to buffer overflows. Other languages, such as Ada and C#, typically provide overflow protection, but the protection can be disabled by the programmer. Be wary that a language's interface to native code may still be subject to overflows, even if the language itself is theoretically safe.
Architecture and Design: Use a vetted library or framework that does not allow this weakness to occur or provides constructs that make this weakness easier to avoid. Examples include the Safe C String Library (SafeStr) by Messier and Viega [REF-57], and the Strsafe.h library from Microsoft [REF-56]. These libraries provide safer versions of overflow-prone string-handling functions.
Operation, Build and Compilation: Use automatic buffer overflow detection mechanisms that are offered by certain compilers or compiler extensions. Examples include: the Microsoft Visual Studio /GS flag, Fedora/Red Hat FORTIFY_SOURCE GCC flag, StackGuard, and ProPolice, which provide various mechanisms including canary-based detection and range/index checking. D3-SFCV (Stack Frame Canary Validation) from D3FEND [REF-1334] discusses canary-based detection in detail.
Implementation: Consider adhering to the following rules when allocating and managing an application's memory: Double check that the buffer is as large as specified. When using functions that accept a number of bytes to copy, such as strncpy(), be aware that if the destination buffer size is equal to the source buffer size, it may not NULL-terminate the string. Check buffer boundaries if accessing the buffer in a loop and make sure there is no danger of writing past the allocated space. If necessary, truncate all input strings to a reasonable length before passing them to the copy and concatenation functions.
Operation, Build and Compilation: Run or compile the software using features or extensions that randomly arrange the positions of a program's executable and libraries in memory. Because this makes the addresses unpredictable, it can prevent an attacker from reliably jumping to exploitable code. Examples include Address Space Layout Randomization (ASLR) [REF-58] [REF-60] and Position-Independent Executables (PIE) [REF-64]. Imported modules may be similarly realigned if their default memory addresses conflict with other modules, in a process known as "rebasing" (for Windows) and "prelinking" (for Linux) [REF-1332] using randomly generated addresses. ASLR for libraries cannot be used in conjunction with prelink since it would require relocating the libraries at run-time, defeating the whole purpose of prelinking. For more information on these techniques see D3-SAOR (Segment Address Offset Randomization) from D3FEND [REF-1335].
Operation: Use a CPU and operating system that offers Data Execution Protection (using hardware NX or XD bits) or the equivalent techniques that simulate this feature in software, such as PaX [REF-60] [REF-61]. These techniques ensure that any instruction executed is exclusively at a memory address that is part of the code segment. For more information on these techniques see D3-PSEP (Process Segment Execution Prevention) from D3FEND [REF-1336].
Implementation: Replace unbounded copy functions with analogous functions that support length arguments, such as strcpy with strncpy. Create these if they are not available.

References

Advisories

nvd.nist.gov/vuln/detail/CVE-2021-29610

Packages

github.com/tensorflow/tensorflow

Timeline

May 14, 2021 Published
Jul 8, 2026 Last updated by the source
Oct 31, 2026 Latest EPSS score

Advisory	Severity	Why 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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Description

Impact

Patches

For more information

Attribution

Affected versions

PyPI

How to fix

tensorflow PyPI → 2.1.4

tensorflow-cpu PyPI → 2.1.4

tensorflow-gpu PyPI → 2.1.4

Severity breakdown

CVSS 4.0 0.0

CVSS 3.1 3.6

Weakness

CWE-665: Improper Initialization

CWE-787: Out-of-bounds Write

References

Advisories

Packages

Other links

Timeline

Related advisories

tensorflow PyPI → `2.1.4`

tensorflow-cpu PyPI → `2.1.4`

tensorflow-gpu PyPI → `2.1.4`