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GHSA-hr84-fqvp-48mm BIT-tensorflow-2021-29521 CVE-2021-29521 PYSEC-2021-158 PYSEC-2021-449 PYSEC-2021-647

Segfault in SparseCountSparseOutput

Low PyPI
Severity
0.0 / 10
Low · CVSS 4.0
Exploit chance (EPSS)
0.19%
Chance of exploitation in the next 30 days. Higher than 8.58% of scored vulnerabilities.
Known exploited
No
Not in CISA's catalog
Weakness
CWE-131 Incorrect Calculation of Buffer Size
Fix
Available
3 packages have a fixed version

Description

Impact

Specifying a negative dense shape in tf.raw_ops.SparseCountSparseOutput results in a segmentation fault being thrown out from the standard library as std::vector invariants are broken.

import tensorflow as tf

indices = tf.constant([], shape=[0, 0], dtype=tf.int64)
values = tf.constant([], shape=[0, 0], dtype=tf.int64)
dense_shape = tf.constant([-100, -100, -100], shape=[3], dtype=tf.int64)
weights = tf.constant([], shape=[0, 0], dtype=tf.int64)

tf.raw_ops.SparseCountSparseOutput(indices=indices, values=values, dense_shape=dense_shape, weights=weights, minlength=79, maxlength=96, binary_output=False)

This is because the implementation assumes the first element of the dense shape is always positive and uses it to initialize a BatchedMap<T> (i.e., std::vector<absl::flat_hash_map<int64,T>>) data structure.

  bool is_1d = shape.NumElements() == 1;
  int num_batches = is_1d ? 1 : shape.flat<int64>()(0);
  ...
  auto per_batch_counts = BatchedMap<W>(num_batches);

If the shape tensor has more than one element, num_batches is the first value in shape.

Ensuring that the dense_shape argument is a valid tensor shape (that is, all elements are non-negative) solves this issue.

Patches

We have patched the issue in GitHub commit c57c0b9f3a4f8684f3489dd9a9ec627ad8b599f5.

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

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

PackageVulnerableFixed in
tensorflow
>= 2.3.0, < 2.3.3
2.3.3
tensorflow
>= 2.4.0, < 2.4.2
2.4.2
tensorflow
>= 2.3.0, < 2.3.3
>= 2.4.0, < 2.4.2
2.3.3
2.4.2
tensorflow-cpu
>= 2.3.0, < 2.3.3
2.3.3
tensorflow-cpu
>= 2.4.0, < 2.4.2
2.4.2
tensorflow-cpu
>= 2.3.0, < 2.3.3
>= 2.4.0, < 2.4.2
2.3.3
2.4.2
tensorflow-gpu
>= 2.3.0, < 2.3.3
2.3.3
tensorflow-gpu
>= 2.4.0, < 2.4.2
2.4.2
tensorflow-gpu
>= 2.3.0, < 2.3.3
>= 2.4.0, < 2.4.2
2.3.3
2.4.2

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 → 2.3.3

pip
pip install --upgrade "tensorflow>=2.3.3"
poetry
poetry add "tensorflow>=2.3.3"
uv
uv add "tensorflow>=2.3.3"

tensorflow-cpu PyPI → 2.3.3

pip
pip install --upgrade "tensorflow-cpu>=2.3.3"
poetry
poetry add "tensorflow-cpu>=2.3.3"
uv
uv add "tensorflow-cpu>=2.3.3"

tensorflow-gpu PyPI → 2.3.3

pip
pip install --upgrade "tensorflow-gpu>=2.3.3"
poetry
poetry add "tensorflow-gpu>=2.3.3"
uv
uv add "tensorflow-gpu>=2.3.3"

Severity breakdown

CVSS 4.0 0.0

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

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

CVSS 3.1 2.5

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

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

Weakness

CWE-131: Incorrect Calculation of Buffer Size

The product does not correctly calculate the size to be used when allocating a buffer, which could lead to a buffer overflow.

How to prevent it
  • Implementation: When allocating a buffer for the purpose of transforming, converting, or encoding an input, allocate enough memory to handle the largest possible encoding. For example, in a routine that converts "&" characters to "&amp;" for HTML entity encoding, the output buffer needs to be at least 5 times as large as the input buffer.
  • Implementation: Understand the programming language's underlying representation and how it interacts with numeric calculation (CWE-681). Pay close attention to byte size discrepancies, precision, signed/unsigned distinctions, truncation, conversion and casting between types, "not-a-number" calculations, and how the language handles numbers that are too large or too small for its underlying representation. [REF-7] Also be careful to account for 32-bit, 64-bit, and other potential differences that may affect the numeric representation.
  • Implementation: Perform input validation on any numeric input by ensuring that it is within the expected range. Enforce that the input meets both the minimum and maximum requirements for the expected range.
  • Architecture and Design: For any security checks that are performed on the client side, ensure that these checks are duplicated on the server side, in order to avoid CWE-602. Attackers can bypass the client-side checks by modifying values after the checks have been performed, or by changing the client to remove the client-side checks entirely. Then, these modified values would be submitted to the server.
  • Implementation: When processing structured incoming data containing a size field followed by raw data, identify and resolve any inconsistencies between the size field and the actual size of the data (CWE-130).
  • Implementation: When allocating memory that uses sentinels to mark the end of a data structure - such as NUL bytes in strings - make sure you also include the sentinel in your calculation of the total amount of memory that must be allocated.
  • Implementation: Replace unbounded copy functions with analogous functions that support length arguments, such as strcpy with strncpy. Create these if they are not available.
  • Implementation: Use sizeof() on the appropriate data type to avoid CWE-467.
  • Implementation: Use the appropriate type for the desired action. For example, in C/C++, only use unsigned types for values that could never be negative, such as height, width, or other numbers related to quantity. This will simplify validation and will reduce surprises related to unexpected casting.
  • 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 [REF-1482]. Use libraries or frameworks that make it easier to handle numbers without unexpected consequences, or buffer allocation routines that automatically track buffer size. Examples include safe integer handling packages such as SafeInt (C++) or IntegerLib (C or C++). [REF-106]
  • 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.
  • 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: Examine compiler warnings closely and eliminate problems with potential security implications, such as signed / unsigned mismatch in memory operations, or use of uninitialized variables. Even if the weakness is rarely exploitable, a single failure may lead to the compromise of the entire system.
  • Architecture and Design, Operation: Run your code using the lowest privileges that are required to accomplish the necessary tasks [REF-76]. If possible, create isolated accounts with limited privileges that are only used for a single task. That way, a successful attack will not immediately give the attacker access to the rest of the software or its environment. For example, database applications rarely need to run as the database administrator, especially in day-to-day operations.
  • Architecture and Design, Operation: Run the code in a "jail" or similar sandbox environment that enforces strict boundaries between the process and the operating system. This may effectively restrict which files can be accessed in a particular directory or which commands can be executed by the software. OS-level examples include the Unix chroot jail, AppArmor, and SELinux. In general, managed code may provide some protection. For example, java.io.FilePermission in the Java SecurityManager allows the software to specify restrictions on file operations. This may not be a feasible solution, and it only limits the impact to the operating system; the rest of the application may still be subject to compromise. Be careful to avoid CWE-243 and other weaknesses related to jails.

References

Advisories

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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