GHSA-2r2f-g8mw-9gvr BIT-tensorflow-2022-29208 CVE-2022-29208 PYSEC-2026-3088 PYSEC-2026-3266 PYSEC-2026-943
Segfault and OOB write due to incomplete validation in `EditDistance` in TensorFlow
- Severity
- 7.1 / 10
- High · CVSS 3.1
- Exploit chance (EPSS)
- 0.39%
- Chance of exploitation in the next 30 days. Higher than 32.85% of scored vulnerabilities.
- Known exploited
- No
- Not in CISA's catalog
- Weakness
- CWE-787 Out-of-bounds Write
- Fix
- Available
- 3 packages have a fixed version
Description
Impact
The implementation of tf.raw_ops.EditDistance has incomplete validation. Users can pass negative values to cause a segmentation fault based denial of service:
import tensorflow as tf
hypothesis_indices = tf.constant(-1250999896764, shape=[3, 3], dtype=tf.int64)
hypothesis_values = tf.constant(0, shape=[3], dtype=tf.int64)
hypothesis_shape = tf.constant(0, shape=[3], dtype=tf.int64)
truth_indices = tf.constant(-1250999896764, shape=[3, 3], dtype=tf.int64)
truth_values = tf.constant(2, shape=[3], dtype=tf.int64)
truth_shape = tf.constant(2, shape=[3], dtype=tf.int64)
tf.raw_ops.EditDistance(
hypothesis_indices=hypothesis_indices,
hypothesis_values=hypothesis_values,
hypothesis_shape=hypothesis_shape,
truth_indices=truth_indices,
truth_values=truth_values,
truth_shape=truth_shape)
In multiple places throughout the code, we are computing an index for a write operation:
if (g_truth == g_hypothesis) {
auto loc = std::inner_product(g_truth.begin(), g_truth.end(),
output_strides.begin(), int64_t{0});
OP_REQUIRES(
ctx, loc < output_elements,
errors::Internal("Got an inner product ", loc,
" which would require in writing to outside of "
"the buffer for the output tensor (max elements ",
output_elements, ")"));
output_t(loc) =
gtl::LevenshteinDistance<T>(truth_seq, hypothesis_seq, cmp);
// ...
}
However, the existing validation only checks against the upper bound of the array. Hence, it is possible to write before the array by massaging the input to generate negative values for loc.
Patches
We have patched the issue in GitHub commit 30721cf564cb029d34535446d6a5a6357bebc8e7.
The fix will be included in TensorFlow 2.9.0. We will also cherrypick this commit on TensorFlow 2.8.1, TensorFlow 2.7.2, and TensorFlow 2.6.4, 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 Neophytos Christou from Secure Systems Lab at Brown University.
Affected versions
PyPI
| Package | Vulnerable | Fixed in |
|---|---|---|
| tensorflow | < 2.6.4 |
2.6.4 |
| tensorflow | >= 2.7.0, < 2.7.2 |
2.7.2 |
| tensorflow | >= 2.8.0, < 2.8.1 |
2.8.1 |
| tensorflow | < 2.6.4>= 2.7.0, < 2.7.2>= 2.8.0, < 2.8.1 |
2.6.42.7.22.8.1 |
| tensorflow-cpu | < 2.6.4 |
2.6.4 |
| tensorflow-cpu | >= 2.7.0, < 2.7.2 |
2.7.2 |
| tensorflow-cpu | >= 2.8.0, < 2.8.1 |
2.8.1 |
| tensorflow-cpu | < 2.6.4>= 2.7.0, < 2.7.2>= 2.8.0, < 2.8.1 |
2.6.42.7.22.8.1 |
| tensorflow-gpu | < 2.6.4 |
2.6.4 |
| tensorflow-gpu | >= 2.7.0, < 2.7.2 |
2.7.2 |
| tensorflow-gpu | >= 2.8.0, < 2.8.1 |
2.8.1 |
| tensorflow-gpu | < 2.6.4>= 2.7.0, < 2.7.2>= 2.8.0, < 2.8.1 |
2.6.42.7.22.8.1 |
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 → 2.6.4
- pip
pip install --upgrade "tensorflow>=2.6.4"- poetry
poetry add "tensorflow>=2.6.4"- uv
uv add "tensorflow>=2.6.4"
tensorflow-cpu PyPI → 2.6.4
- pip
pip install --upgrade "tensorflow-cpu>=2.6.4"- poetry
poetry add "tensorflow-cpu>=2.6.4"- uv
uv add "tensorflow-cpu>=2.6.4"
tensorflow-gpu PyPI → 2.6.4
- pip
pip install --upgrade "tensorflow-gpu>=2.6.4"- poetry
poetry add "tensorflow-gpu>=2.6.4"- uv
uv add "tensorflow-gpu>=2.6.4"
Severity breakdown
CVSS 3.1 7.1
CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:H/A:H
- Attack vector
- Local
- Attack complexity
- Low
- Privileges required
- Low
- User interaction
- None
- Scope
- Unchanged
- Confidentiality
- None
- Integrity
- High
- Availability
- High
Weakness
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
Packages
Other links
- github.com/tensorflow/tensorflow/security/advisories/GHSA-2r2f-g8mw-9gvr
- github.com/tensorflow/tensorflow/commit/30721cf564cb029d34535446d6a5a6357bebc8e7
- github.com/tensorflow/tensorflow/releases/tag/v2.6.4
- github.com/tensorflow/tensorflow/releases/tag/v2.7.2
- github.com/tensorflow/tensorflow/releases/tag/v2.8.1
- github.com/tensorflow/tensorflow/releases/tag/v2.9.0
Timeline
- Published
- Last updated by the source
- Latest EPSS score
Related advisories
| 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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