Incorrect Calculation of Buffer Size

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.Show less

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.Show less

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.Show less

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.Show less

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.Show less

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.Show less

A buffer size validation vulnerability in the overlayd service of Juniper Networks Junos OS may allow an unauthenticated remote attacker to send specially crafted packets to the device, triggering a partial Denial of Service (DoS) condition, or leading to remote code execution (RCE). Continued receipt and processing of these packets will sustain the partial DoS. The overlayd daemon handles Overlay OAM packets, such as ping and traceroute, sent to the overlay. The service runs as root by default and listens for UDP connections on port 4789. This issue results from improper buffer size validation, which can lead to a buffer overflow. Unauthenticated attackers can send specially crafted packets to trigger this vulnerability, resulting in possible remote code execution. overlayd runs by default in MX Series, ACX Series, and QFX Series platforms. The SRX Series does not support VXLAN and is therefore not vulnerable to this issue. Other platforms are also vulnerable if a Virtual Extensible LAN (VXLAN) overlay network is configured. This issue affects Juniper Networks Junos OS: 15.1 versions prior to 15.1R7-S9; 17.3 versions prior to 17.3R3-S11; 17.4 versions prior to 17.4R2-S13, 17.4R3-S4; 18.1 versions prior to 18.1R3-S12; 18.2 versions prior to 18.2R2-S8, 18.2R3-S7; 18.3 versions prior to 18.3R3-S4; 18.4 versions prior to 18.4R1-S8, 18.4R2-S7, 18.4R3-S7; 19.1 versions prior to 19.1R2-S2, 19.1R3-S4; 19.2 versions prior to 19.2R1-S6, 19.2R3-S2; 19.3 versions prior to 19.3R3-S1; 19.4 versions prior to 19.4R2-S4, 19.4R3-S1; 20.1 versions prior to 20.1R2-S1, 20.1R3; 20.2 versions prior to 20.2R2, 20.2R2-S1, 20.2R3; 20.3 versions prior to 20.3R1-S1.Show less

An out-of-bounds write vulnerability exists in the SGI format buffer size processing functionality of Accusoft ImageGear 19.8. A specially crafted malformed file can lead to memory corruption. An attacker can provide a malicious file to trigger this vulnerability.Show less

An out-of-bounds write vulnerability exists in the SGI Format Buffer Size Processing functionality of Accusoft ImageGear 19.8. A specially crafted malformed file can lead to memory corruption. An attacker can provide a malicious file to trigger this vulnerability.Show less

An out-of-bounds write vulnerability exists in the TIFF header count-processing functionality of Accusoft ImageGear 19.8. A specially crafted malformed file can lead to memory corruption. An attacker can provide a malicious file to trigger this vulnerability.Show less

An issue was discovered in the Linux kernel 5.9.x through 5.11.3, as used with Xen. In some less-common configurations, an x86 PV guest OS user can crash a Dom0 or driver domain via a large amount of I/O activity. The issue relates to misuse of guest physical addresses when a configuration has CONFIG_XEN_UNPOPULATED_ALLOC but not CONFIG_XEN_BALLOON_MEMORY_HOTPLUG.Show less

An out-of-bounds write vulnerability exists in the PSD Header processing functionality of Accusoft ImageGear 19.8. A specially crafted malformed file can lead to code execution. An attacker can provide a malicious file to trigger this vulnerability.Show less

In SoftMaker Software GmbH SoftMaker Office TextMaker 2021 (revision 1014), a specially crafted document can cause the document parser to miscalculate a length used to allocate a buffer, later upon usage of this buffer the application will write outside its bounds resulting in a heap-based buffer overflow. An attacker can entice the victim to open a document to trigger this vulnerability.Show less

On Juniper Networks MX Series with MS-MIC or MS-MPC card configured with NAT64 configuration, receipt of a malformed IPv6 packet may crash the MS-PIC component on MS-MIC or MS-MPC. This issue occurs when a multiservice card is translating the malformed IPv6 packet to IPv4 packet. An unauthenticated attacker can continuously send crafted IPv6 packets through the device causing repetitive MS-PIC process crashes, resulting in an extended Denial of Service condition. This issue affects Juniper Networks Junos OS on MX Series: 15.1 versions prior to 15.1R7-S7; 15.1X53 versions prior to 15.1X53-D593; 16.1 versions prior to 16.1R7-S8; 17.2 versions prior to 17.2R3-S4; 17.3 versions prior to 17.3R3-S6; 17.4 versions prior to 17.4R2-S11, 17.4R3; 18.1 versions prior to 18.1R3-S11; 18.2 versions prior to 18.2R3-S6; 18.2X75 versions prior to 18.2X75-D41, 18.2X75-D430, 18.2X75-D53, 18.2X75-D65; 18.3 versions prior to 18.3R2-S4, 18.3R3; 18.4 versions prior to 18.4R2-S5, 18.4R3; 19.1 versions prior to 19.1R2; 19.2 versions prior to 19.2R1-S5, 19.2R2; 19.3 versions prior to 19.3R2.Show less

An exploitable code execution vulnerability exists in the fsck_chk_orphan_node functionality of F2fs-Tools F2fs.Fsck 1.13. A specially crafted f2fs filesystem can cause a heap buffer overflow resulting in a code execution. An attacker can provide a malicious file to trigger this vulnerability.Show less

An exploitable information disclosure vulnerability exists in the init_node_manager functionality of F2fs-Tools F2fs.Fsck 1.12 and 1.13. A specially crafted filesystem can be used to disclose information. An attacker can provide a malicious file to trigger this vulnerability.Show less

An arbitrary code execution vulnerability exists in the rendering functionality of Nitro Software, Inc.’s Nitro Pro 13.13.2.242. When drawing the contents of a page using colors from an indexed colorspace, the application can miscalculate the size of a buffer when allocating space for its colors. When using this allocated buffer, the application can write outside its bounds and cause memory corruption which can lead to code execution. A specially crafted document must be loaded by a victim in order to trigger this vulnerability.Show less

An exploitable vulnerability exists in the object stream parsing functionality of Nitro Software, Inc.’s Nitro Pro 13.13.2.242 when updating its cross-reference table. When processing an object stream from a PDF document, the application will perform a calculation in order to allocate memory for the list of indirect objects. Due to an error when calculating this size, an integer overflow may occur which can result in an undersized buffer being allocated. Later when initializing this buffer, the application can write outside its bounds which can cause a memory corruption that can lead to code execution. A specially crafted document can be delivered to a victim in order to trigger this vulnerability.Show less