Common Weakness Enumeration
CWE-131
AllowedIncorrect Calculation of Buffer Size
Abstraction: Base · Status: Draft
The product does not correctly calculate the size to be used when allocating a buffer, which could lead to a buffer overflow.
270 vulnerabilities reference this CWE, most recent first.
CVE-2021-38423 (GCVE-0-2021-38423)
Vulnerability from cvelistv5 – Published: 2022-05-05 15:23 – Updated: 2025-04-16 16:23
VLAI
EPSS
VEX
Title
GurumDDS Heap-based Incorrect Calculation of Buffer Size
Summary
All versions of GurumDDS improperly calculate the size to be used when allocating the buffer, which may result in a buffer overflow.
Severity
6.6 (Medium)
SSVC
Exploitation: none
Automatable: no
Technical Impact: partial
CISA Coordinator (v2.0.3)
CWE
- CWE-131 - Incorrect Calculation of Buffer Size
Assigner
References
1 reference
| URL | Tags |
|---|---|
| https://www.cisa.gov/uscert/ics/advisories/icsa-2… | x_refsource_CONFIRM |
Impacted products
1 product
| Vendor | Product | Version | |
|---|---|---|---|
| GurumNetworks | GurumDDS |
Affected:
all versions
|
Credits
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CVE-2021-29608 (GCVE-0-2021-29608)
Vulnerability from cvelistv5 – Published: 2021-05-14 19:20 – Updated: 2024-08-03 22:11
VLAI
EPSS
VEX
Title
Heap OOB and null pointer dereference in `RaggedTensorToTensor`
Summary
TensorFlow is an end-to-end open source platform for machine learning. Due to lack of validation in `tf.raw_ops.RaggedTensorToTensor`, an attacker can exploit an undefined behavior if input arguments are empty. The implementation(https://github.com/tensorflow/tensorflow/blob/656e7673b14acd7835dc778867f84916c6d1cac2/tensorflow/core/kernels/ragged_tensor_to_tensor_op.cc#L356-L360) only checks that one of the tensors is not empty, but does not check for the other ones. There are multiple `DCHECK` validations to prevent heap OOB, but these are no-op in release builds, hence they don't prevent anything. The fix will be included in TensorFlow 2.5.0. We will also cherrypick these commits 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.
Severity
5.3 (Medium)
CWE
- CWE-131 - Incorrect Calculation of Buffer Size
Assigner
References
4 references
| URL | Tags |
|---|---|
| https://github.com/tensorflow/tensorflow/security… | x_refsource_CONFIRM |
| https://github.com/tensorflow/tensorflow/commit/b… | x_refsource_MISC |
| https://github.com/tensorflow/tensorflow/commit/c… | x_refsource_MISC |
| https://github.com/tensorflow/tensorflow/commit/f… | x_refsource_MISC |
Impacted products
1 product
| Vendor | Product | Version | |
|---|---|---|---|
| tensorflow | tensorflow |
Affected:
< 2.1.4
Affected: >= 2.2.0, < 2.2.3 Affected: >= 2.3.0, < 2.3.3 Affected: >= 2.4.0, < 2.4.2 |
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CVE-2021-29545 (GCVE-0-2021-29545)
Vulnerability from cvelistv5 – Published: 2021-05-14 19:11 – Updated: 2024-08-03 22:11
VLAI
EPSS
VEX
Title
Heap buffer overflow in `SparseTensorToCSRSparseMatrix`
Summary
TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a denial of service via a `CHECK`-fail in converting sparse tensors to CSR Sparse matrices. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/800346f2c03a27e182dd4fba48295f65e7790739/tensorflow/core/kernels/sparse/kernels.cc#L66) does a double redirection to access an element of an array allocated on the heap. If the value at `indices(i, 0)` is such that `indices(i, 0) + 1` is outside the bounds of `csr_row_ptr`, this results in writing outside of bounds of heap allocated data. 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.
Severity
CWE
- CWE-131 - Incorrect Calculation of Buffer Size
Assigner
References
2 references
| URL | Tags |
|---|---|
| https://github.com/tensorflow/tensorflow/security… | x_refsource_CONFIRM |
| https://github.com/tensorflow/tensorflow/commit/1… | x_refsource_MISC |
Impacted products
1 product
| Vendor | Product | Version | |
|---|---|---|---|
| tensorflow | tensorflow |
Affected:
< 2.1.4
Affected: >= 2.2.0, < 2.2.3 Affected: >= 2.3.0, < 2.3.3 Affected: >= 2.4.0, < 2.4.2 |
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CVE-2021-29542 (GCVE-0-2021-29542)
Vulnerability from cvelistv5 – Published: 2021-05-14 19:11 – Updated: 2024-08-03 22:11
VLAI
EPSS
VEX
Title
Heap buffer overflow in `StringNGrams`
Summary
TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a heap buffer overflow by passing crafted inputs to `tf.raw_ops.StringNGrams`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/1cdd4da14282210cc759e468d9781741ac7d01bf/tensorflow/core/kernels/string_ngrams_op.cc#L171-L185) fails to consider corner cases where input would be split in such a way that the generated tokens should only contain padding elements. If input is such that `num_tokens` is 0, then, for `data_start_index=0` (when left padding is present), the marked line would result in reading `data[-1]`. 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.
Severity
CWE
- CWE-131 - Incorrect Calculation of Buffer Size
Assigner
References
2 references
| URL | Tags |
|---|---|
| https://github.com/tensorflow/tensorflow/security… | x_refsource_CONFIRM |
| https://github.com/tensorflow/tensorflow/commit/b… | x_refsource_MISC |
Impacted products
1 product
| Vendor | Product | Version | |
|---|---|---|---|
| tensorflow | tensorflow |
Affected:
< 2.1.4
Affected: >= 2.2.0, < 2.2.3 Affected: >= 2.3.0, < 2.3.3 Affected: >= 2.4.0, < 2.4.2 |
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CVE-2021-29537 (GCVE-0-2021-29537)
Vulnerability from cvelistv5 – Published: 2021-05-14 19:11 – Updated: 2024-08-03 22:11
VLAI
EPSS
VEX
Title
Heap buffer overflow in `QuantizedResizeBilinear`
Summary
TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a heap buffer overflow in `QuantizedResizeBilinear` by passing in invalid thresholds for the quantization. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/50711818d2e61ccce012591eeb4fdf93a8496726/tensorflow/core/kernels/quantized_resize_bilinear_op.cc#L705-L706) assumes that the 2 arguments are always valid scalars and tries to access the numeric value directly. 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.
Severity
CWE
- CWE-131 - Incorrect Calculation of Buffer Size
Assigner
References
2 references
| URL | Tags |
|---|---|
| https://github.com/tensorflow/tensorflow/security… | x_refsource_CONFIRM |
| https://github.com/tensorflow/tensorflow/commit/f… | x_refsource_MISC |
Impacted products
1 product
| Vendor | Product | Version | |
|---|---|---|---|
| tensorflow | tensorflow |
Affected:
< 2.1.4
Affected: >= 2.2.0, < 2.2.3 Affected: >= 2.3.0, < 2.3.3 Affected: >= 2.4.0, < 2.4.2 |
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CVE-2021-29536 (GCVE-0-2021-29536)
Vulnerability from cvelistv5 – Published: 2021-05-14 19:11 – Updated: 2024-08-03 22:11
VLAI
EPSS
VEX
Title
Heap buffer overflow in `QuantizedReshape`
Summary
TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a heap buffer overflow in `QuantizedReshape` by passing in invalid thresholds for the quantization. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/a324ac84e573fba362a5e53d4e74d5de6729933e/tensorflow/core/kernels/quantized_reshape_op.cc#L38-L55) assumes that the 2 arguments are always valid scalars and tries to access the numeric value directly. However, if any of these tensors is empty, then `.flat<T>()` is an empty buffer and accessing the element at position 0 results in overflow. 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.
Severity
CWE
- CWE-131 - Incorrect Calculation of Buffer Size
Assigner
References
2 references
| URL | Tags |
|---|---|
| https://github.com/tensorflow/tensorflow/security… | x_refsource_CONFIRM |
| https://github.com/tensorflow/tensorflow/commit/a… | x_refsource_MISC |
Impacted products
1 product
| Vendor | Product | Version | |
|---|---|---|---|
| tensorflow | tensorflow |
Affected:
< 2.1.4
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CVE-2021-29535 (GCVE-0-2021-29535)
Vulnerability from cvelistv5 – Published: 2021-05-14 19:11 – Updated: 2024-08-03 22:11
VLAI
EPSS
VEX
Title
Heap buffer overflow in `QuantizedMul`
Summary
TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a heap buffer overflow in `QuantizedMul` by passing in invalid thresholds for the quantization. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/87cf4d3ea9949051e50ca3f071fc909538a51cd0/tensorflow/core/kernels/quantized_mul_op.cc#L287-L290) assumes that the 4 arguments are always valid scalars and tries to access the numeric value directly. However, if any of these tensors is empty, then `.flat<T>()` is an empty buffer and accessing the element at position 0 results in overflow. 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.
Severity
CWE
- CWE-131 - Incorrect Calculation of Buffer Size
Assigner
References
2 references
| URL | Tags |
|---|---|
| https://github.com/tensorflow/tensorflow/security… | x_refsource_CONFIRM |
| https://github.com/tensorflow/tensorflow/commit/e… | x_refsource_MISC |
Impacted products
1 product
| Vendor | Product | Version | |
|---|---|---|---|
| tensorflow | tensorflow |
Affected:
< 2.1.4
Affected: >= 2.2.0, < 2.2.3 Affected: >= 2.3.0, < 2.3.3 Affected: >= 2.4.0, < 2.4.2 |
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CVE-2021-29529 (GCVE-0-2021-29529)
Vulnerability from cvelistv5 – Published: 2021-05-14 19:12 – Updated: 2024-08-03 22:11
VLAI
EPSS
VEX
Title
Heap buffer overflow caused by rounding
Summary
TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a heap buffer overflow in `tf.raw_ops.QuantizedResizeBilinear` by manipulating input values so that float rounding results in off-by-one error in accessing image elements. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/44b7f486c0143f68b56c34e2d01e146ee445134a/tensorflow/core/kernels/quantized_resize_bilinear_op.cc#L62-L66) computes two integers (representing the upper and lower bounds for interpolation) by ceiling and flooring a floating point value. For some values of `in`, `interpolation->upper[i]` might be smaller than `interpolation->lower[i]`. This is an issue if `interpolation->upper[i]` is capped at `in_size-1` as it means that `interpolation->lower[i]` points outside of the image. Then, in the interpolation code(https://github.com/tensorflow/tensorflow/blob/44b7f486c0143f68b56c34e2d01e146ee445134a/tensorflow/core/kernels/quantized_resize_bilinear_op.cc#L245-L264), this would result in heap buffer overflow. 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.
Severity
CWE
- CWE-131 - Incorrect Calculation of Buffer Size
Assigner
References
2 references
| URL | Tags |
|---|---|
| https://github.com/tensorflow/tensorflow/security… | x_refsource_CONFIRM |
| https://github.com/tensorflow/tensorflow/commit/f… | x_refsource_MISC |
Impacted products
1 product
| Vendor | Product | Version | |
|---|---|---|---|
| tensorflow | tensorflow |
Affected:
< 2.1.4
Affected: >= 2.2.0, < 2.2.3 Affected: >= 2.3.0, < 2.3.3 Affected: >= 2.4.0, < 2.4.2 |
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CVE-2021-29521 (GCVE-0-2021-29521)
Vulnerability from cvelistv5 – Published: 2021-05-14 19:35 – Updated: 2024-08-03 22:11
VLAI
EPSS
VEX
Title
Segfault in SparseCountSparseOutput
Summary
TensorFlow is an end-to-end open source platform for machine learning. 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. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/8f7b60ee8c0206a2c99802e3a4d1bb55d2bc0624/tensorflow/core/kernels/count_ops.cc#L199-L213) 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>>`(https://github.com/tensorflow/tensorflow/blob/8f7b60ee8c0206a2c99802e3a4d1bb55d2bc0624/tensorflow/core/kernels/count_ops.cc#L27)) data structure. 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. 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.
Severity
CWE
- CWE-131 - Incorrect Calculation of Buffer Size
Assigner
References
2 references
| URL | Tags |
|---|---|
| https://github.com/tensorflow/tensorflow/security… | x_refsource_CONFIRM |
| https://github.com/tensorflow/tensorflow/commit/c… | x_refsource_MISC |
Impacted products
1 product
| Vendor | Product | Version | |
|---|---|---|---|
| tensorflow | tensorflow |
Affected:
< 2.3.3
Affected: >= 2.4.0, < 2.4.2 |
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CVE-2021-21824 (GCVE-0-2021-21824)
Vulnerability from cvelistv5 – Published: 2021-06-11 16:12 – Updated: 2024-08-03 18:23
VLAI
EPSS
VEX
Summary
An out-of-bounds write vulnerability exists in the JPG Handle_JPEG420 functionality of Accusoft ImageGear 19.9. A specially crafted malformed file can lead to memory corruption. An attacker can provide a malicious file to trigger this vulnerability.
Severity
8.1 (High)
CWE
- CWE-131 - Incorrect Calculation of Buffer Size
Assigner
References
1 reference
| URL | Tags |
|---|---|
| https://talosintelligence.com/vulnerability_repor… | x_refsource_MISC |
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Mitigation
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 "&" for HTML entity encoding, the output buffer needs to be at least 5 times as large as the input buffer.
Mitigation MIT-36
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.
Mitigation MIT-8
Implementation
Strategy: Input Validation
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.
Mitigation MIT-15
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.
Mitigation
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).
Mitigation
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.
Mitigation MIT-13
Implementation
Replace unbounded copy functions with analogous functions that support length arguments, such as strcpy with strncpy. Create these if they are not available.
Mitigation
Implementation
Use sizeof() on the appropriate data type to avoid CWE-467.
Mitigation
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.
Mitigation MIT-4
Architecture and Design
Strategy: Libraries or Frameworks
- 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]
Mitigation MIT-10
Operation
Build and Compilation
Strategy: Environment Hardening
- 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.
Mitigation MIT-11
Operation
Build and Compilation
Strategy: Environment Hardening
- 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].
Mitigation MIT-12
Operation
Strategy: Environment Hardening
- 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].
Mitigation MIT-26
Implementation
Strategy: Compilation or Build Hardening
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.
Mitigation MIT-17
Architecture and Design
Operation
Strategy: Environment Hardening
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.
Mitigation MIT-22
Architecture and Design
Operation
Strategy: Sandbox or Jail
- 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.
CAPEC-100: Overflow Buffers
Buffer Overflow attacks target improper or missing bounds checking on buffer operations, typically triggered by input injected by an adversary. As a consequence, an adversary is able to write past the boundaries of allocated buffer regions in memory, causing a program crash or potentially redirection of execution as per the adversaries' choice.
CAPEC-47: Buffer Overflow via Parameter Expansion
In this attack, the target software is given input that the adversary knows will be modified and expanded in size during processing. This attack relies on the target software failing to anticipate that the expanded data may exceed some internal limit, thereby creating a buffer overflow.