GHSA-jc87-6vpp-7ff3 BIT-tensorflow-2020-15198 CVE-2020-15198 PYSEC-2020-121 PYSEC-2020-278 PYSEC-2020-313
Heap buffer overflow in Tensorflow
Medium
PyPI
- Severity
- 0.0 / 10
- Medium · CVSS 4.0
- Exploit chance (EPSS)
- 0.54%
- Chance of exploitation in the next 30 days. Higher than 45.56% of scored vulnerabilities.
- Known exploited
- No
- Not in CISA's catalog
- Weakness
- CWE-119 Improper Restriction of Operations within the Bounds of a Memory Buffer
- CWE-122 Heap-based Buffer Overflow
- Fix
- Available
- 3 packages have a fixed version
Description
Impact
The SparseCountSparseOutput implementation does not validate that the input arguments form a valid sparse tensor. In particular, there is no validation that the indices tensor has the same shape as the values one. The values in these tensors are always accessed in parallel:
https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/core/kernels/count_ops.cc#L193-L195
Thus, a shape mismatch can result in accesses outside the bounds of heap allocated buffers.
Patches
We have patched the issue in 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and will release a patch release.
We recommend users to upgrade to TensorFlow 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 is a variant of GHSA-p5f8-gfw5-33w4
Affected versions
PyPI
| Package | Vulnerable | Fixed in |
|---|---|---|
| tensorflow | >= 2.3.0, < 2.3.1 |
2.3.1 |
| tensorflow | < 2.3.1 |
2.3.1 |
| tensorflow-cpu | >= 2.3.0, < 2.3.1 |
2.3.1 |
| tensorflow-cpu | < 2.3.1 |
2.3.1 |
| tensorflow-gpu | >= 2.3.0, < 2.3.1 |
2.3.1 |
| tensorflow-gpu | < 2.3.1 |
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 → 2.3.1
- pip
pip install --upgrade "tensorflow>=2.3.1"- poetry
poetry add "tensorflow>=2.3.1"- uv
uv add "tensorflow>=2.3.1"
tensorflow-cpu PyPI → 2.3.1
- pip
pip install --upgrade "tensorflow-cpu>=2.3.1"- poetry
poetry add "tensorflow-cpu>=2.3.1"- uv
uv add "tensorflow-cpu>=2.3.1"
tensorflow-gpu PyPI → 2.3.1
- pip
pip install --upgrade "tensorflow-gpu>=2.3.1"- poetry
poetry add "tensorflow-gpu>=2.3.1"- uv
uv add "tensorflow-gpu>=2.3.1"
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:L/SI:L/SA:N
- 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)
- Low
- Integrity (subsequent systems)
- Low
- Availability (subsequent systems)
- None
CVSS 3.1 5.4
CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:C/C:L/I:L/A:N
- Attack vector
- Network
- Attack complexity
- High
- Privileges required
- None
- User interaction
- None
- Scope
- Changed
- Confidentiality
- Low
- Integrity
- Low
- Availability
- None
Weakness
CWE-119: Improper Restriction of Operations within the Bounds of a Memory Buffer
The product performs operations on a memory buffer, but it reads from or writes to a memory location outside the buffer's intended boundary. This may result in read or write operations on unexpected memory locations that could be linked to other variables, data structures, or internal program data.
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.
CWE-122: Heap-based Buffer Overflow
A heap overflow condition is a buffer overflow, where the buffer that can be overwritten is allocated in the heap portion of memory, generally meaning that the buffer was allocated using a routine such as malloc().
How to prevent it
- Pre-design: Use a language or compiler that performs automatic bounds checking.
- Architecture and Design: Use an abstraction library to abstract away risky APIs. Not a complete solution.
- 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].
- Implementation: Implement and perform bounds checking on input.
- Implementation: Do not use dangerous functions such as gets. Look for their safe equivalent, which checks for the boundary.
- Operation: Use OS-level preventative functionality. This is not a complete solution, but it provides some defense in depth.
References
Advisories
Packages
Other links
- github.com/tensorflow/tensorflow/security/advisories/GHSA-jc87-6vpp-7ff3
- github.com/tensorflow/tensorflow/commit/3cbb917b4714766030b28eba9fb41bb97ce9ee02
- github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2020-278…
- github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2020-313…
- github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2020-121.yaml
- github.com/tensorflow/tensorflow/releases/tag/v2.3.1
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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