CVE-2022-29211 (published 2022-05-21T00:15Z)

TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implementation of `tf.histogram_fixed_width` is vulnerable to a crash when the values array contain `Not a Number` (`NaN`) elements. The implementation assumes that all floating point operations are defined and then converts a floating point result to an integer index. If `values` contains `NaN` then the result of the division is still `NaN` and the cast to `int32` would result in a crash. This only occurs on the CPU implementation. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

Severity: LOW
CVSS v2 base score: 2.1 (AV:L/AC:L/Au:N/C:N/I:N/A:P)
CVSS v3 base score: 5.5, MEDIUM (CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H)

Weakness information:
cwe_id: CWE-20
cwe_name: Improper Input Validation
description: The product receives input or data, but it does
        not validate or incorrectly validates that the input has the
        properties that are required to process the data safely and
        correctly.
extended_description: 
	   Input validation is a frequently-used technique
	   for checking potentially dangerous inputs in order to
	   ensure that the inputs are safe for processing within the
	   code, or when communicating with other components.  When
	   software does not validate input properly, an attacker is
	   able to craft the input in a form that is not expected by
	   the rest of the application. This will lead to parts of the
	   system receiving unintended input, which may result in
	   altered control flow, arbitrary control of a resource, or
	   arbitrary code execution.
	   Input validation is not the only technique for
	   processing input, however.  Other techniques attempt to
	   transform potentially-dangerous input into something safe, such
	   as filtering (CWE-790) - which attempts to remove dangerous
	   inputs - or encoding/escaping (CWE-116), which attempts to
	   ensure that the input is not misinterpreted when it is included
	   in output to another component. Other techniques exist as well
	   (see CWE-138 for more examples.)
	   Input validation can be applied to:
	   
	     raw data - strings, numbers, parameters, file contents, etc.
	     metadata - information about the raw data, such as headers or size
	   
	   Data can be simple or structured.  Structured data
	   can be composed of many nested layers, composed of
	   combinations of metadata and raw data, with other simple or
	   structured data.
	   Many properties of raw data or metadata may need
	   to be validated upon entry into the code, such
	   as:
	   
	     specified quantities such as size, length, frequency, price, rate, number of operations, time, etc.
	     implied or derived quantities, such as the actual size of a file instead of a specified size
	     indexes, offsets, or positions into more complex data structures
	     symbolic keys or other elements into hash tables, associative arrays, etc.
	     well-formedness, i.e. syntactic correctness - compliance with expected syntax 
	     lexical token correctness - compliance with rules for what is treated as a token
	     specified or derived type - the actual type of the input (or what the input appears to be)
	     consistency - between individual data elements, between raw data and metadata, between references, etc.
	     conformance to domain-specific rules, e.g. business logic 
	     equivalence - ensuring that equivalent inputs are treated the same
	     authenticity, ownership, or other attestations about the input, e.g. a cryptographic signature to prove the source of the data
	   
	   Implied or derived properties of data must often
	   be calculated or inferred by the code itself.  Errors in
	   deriving properties may be considered a contributing factor
	   to improper input validation.
	   
	   Note that "input validation" has very different
	   meanings to different people, or within different
	   classification schemes.  Caution must be used when
	   referencing this CWE entry or mapping to it.  For example,
	   some weaknesses might involve inadvertently giving control
	   to an attacker over an input when they should not be able
	   to provide an input at all, but sometimes this is referred
	   to as input validation.
	   Finally, it is important to emphasize that the
	   distinctions between input validation and output escaping
	   are often blurred, and developers must be careful to
	   understand the difference, including how input validation
	   is not always sufficient to prevent vulnerabilities,
	   especially when less stringent data types must be
	   supported, such as free-form text. Consider a SQL injection
	   scenario in which a person's last name is inserted into a
	   query. The name "O'Reilly" would likely pass the validation
	   step since it is a common last name in the English
	   language. However, this valid name cannot be directly
	   inserted into the database because it contains the "'"
	   apostrophe character, which would need to be escaped or
	   otherwise transformed. In this case, removing the
	   apostrophe might reduce the risk of SQL injection, but it
	   would produce incorrect behavior because the wrong name
	   would be recorded.
       
         

Fix information:
repo_url: https://github.com/tensorflow/tensorflow
commit_details: repo_url: https://github.com/tensorflow/tensorflow
author: Mihai Maruseac
author_date: 2022-04-20 11:35:47-07:00
msg: Prevent crash when histogram is called with NaN values.

Fixes #45770

PiperOrigin-RevId: 443149951
repository: repo_url: https://github.com/tensorflow/tensorflow
repo_name: tensorflow/tensorflow
description: An Open Source Machine Learning Framework for Everyone
repo_language: C++
owner: tensorflow
file_changes: filename: histogram_op.cc
change_type: ModificationType.MODIFY
diff: @@ -50,6 +50,15 @@ struct HistogramFixedWidthFunctor<CPUDevice, T, Tout> {
                         static_cast<double>(nbins);
     const double nbins_minus_1 = static_cast<double>(nbins - 1);
 
+    // We cannot handle NANs in the algorithm below (due to the case to int32)
+    const Eigen::Tensor<int32, 1, 1> nans_tensor =
+        values.isnan().template cast<int32>();
+    const Eigen::Tensor<int32, 0, 1> reduced_tensor = nans_tensor.sum();
+    const int num_nans = reduced_tensor(0);
+    if (num_nans > 0) {
+      return errors::InvalidArgument("Histogram values must not contain NaN");
+    }
+
     // The calculation is done by finding the slot of each value in `values`.
     // With [a, b]:
     //   step = (b - a) / nbins
@@ -98,12 +107,12 @@ class HistogramFixedWidthOp : public OpKernel {
     const auto nbins = nbins_tensor.scalar<int32>()();
 
     OP_REQUIRES(
-        ctx, (value_range(0) < value_range(1)),
+        ctx, value_range(0) < value_range(1),
         errors::InvalidArgument("value_range should satisfy value_range[0] < "
                                 "value_range[1], but got '[",
                                 value_range(0), ", ", value_range(1), "]'"));
     OP_REQUIRES(
-        ctx, (nbins > 0),
+        ctx, nbins > 0,
         errors::InvalidArgument("nbins should be a positive number, but got '",
                                 nbins, "'"));
 

programming_language: C++