Total
210374 CVE
| CVE | Vendors | Products | Updated | CVSS v2 | CVSS v3 |
|---|---|---|---|---|---|
| CVE-2017-9650 | 2 Automatedlogic, Carrier | 3 I-vu, Sitescan Web, Automatedlogic Webctrl | 2021-07-27 | 4.6 MEDIUM | 7.8 HIGH |
| An Unrestricted Upload of File with Dangerous Type issue was discovered in Automated Logic Corporation (ALC) ALC WebCTRL, i-Vu, SiteScan Web 6.5 and prior; ALC WebCTRL, SiteScan Web 6.1 and prior; ALC WebCTRL, i-Vu 6.0 and prior; ALC WebCTRL, i-Vu, SiteScan Web 5.5 and prior; and ALC WebCTRL, i-Vu, SiteScan Web 5.2 and prior. An authenticated attacker may be able to upload a malicious file allowing the execution of arbitrary code. | |||||
| CVE-2018-8819 | 1 Carrier | 1 Automatedlogic Webctrl | 2021-07-27 | 5.0 MEDIUM | 7.5 HIGH |
| An XXE issue was discovered in Automated Logic Corporation (ALC) WebCTRL Versions 6.0, 6.1 and 6.5. An unauthenticated attacker could enter malicious input to WebCTRL and a weakly configured XML parser will allow the application to disclose full file contents from the underlying web server OS via the "X-Wap-Profile" HTTP header. | |||||
| CVE-2021-0282 | 1 Juniper | 1 Junos | 2021-07-27 | 7.1 HIGH | 7.5 HIGH |
| On Juniper Networks Junos OS devices with Multipath or add-path feature enabled, processing a specific BGP UPDATE can lead to a routing process daemon (RPD) crash and restart, causing a Denial of Service (DoS). Continued receipt and processing of this UPDATE message will create a sustained Denial of Service (DoS) condition. This BGP UPDATE message can propagate to other BGP peers with vulnerable Junos versions on which Multipath or add-path feature is enabled, and cause RPD to crash and restart. This issue affects both IBGP and EBGP deployments in IPv4 or IPv6 network. Junos OS devices that do not have the BGP Multipath or add-path feature enabled are not affected by this issue. This issue affects: Juniper Networks Junos OS 12.3 versions prior to 12.3R12-S18; 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 18.2R3-S7; 18.3 versions prior to 18.3R3-S4; 18.4 versions prior to 18.4R2-S6, 18.4R3-S6; 19.1 versions prior to 19.1R3-S3; | |||||
| CVE-2021-0279 | 1 Juniper | 1 Contrail Cloud | 2021-07-27 | 5.5 MEDIUM | 5.5 MEDIUM |
| Juniper Networks Contrail Cloud (CC) releases prior to 13.6.0 have RabbitMQ service enabled by default with hardcoded credentials. The messaging services of RabbitMQ are used when coordinating operations and status information among Contrail services. An attacker with access to an administrative service for RabbitMQ (e.g. GUI), can use these hardcoded credentials to cause a Denial of Service (DoS) or have access to unspecified sensitive system information. This issue affects the Juniper Networks Contrail Cloud releases on versions prior to 13.6.0. | |||||
| CVE-2021-3279 | 1 Fortics | 1 Szchat | 2021-07-27 | 4.3 MEDIUM | 6.1 MEDIUM |
| sz.chat version 4 allows injection of web scripts and HTML in the message box. | |||||
| CVE-2021-34817 | 1 Etherpad | 1 Etherpad | 2021-07-27 | 4.3 MEDIUM | 6.1 MEDIUM |
| A Cross-Site Scripting (XSS) issue in the chat component of Etherpad 1.8.13 allows remote attackers to inject arbitrary JavaScript or HTML by importing a crafted pad. | |||||
| CVE-2021-1099 | 1 Nvidia | 1 Virtual Gpu | 2021-07-27 | 4.6 MEDIUM | 7.8 HIGH |
| NVIDIA vGPU software contains a vulnerability in the Virtual GPU Manager (vGPU plugin) that could allow an attacker to cause stack-based buffer overflow and put a customized ROP gadget on the stack. Such an attack may lead to information disclosure, data tampering, or denial of service. This affects vGPU version 12.x (prior to 12.3), version 11.x (prior to 11.5) and version 8.x (prior 8.8). | |||||
| CVE-2008-7220 | 2 Debian, Prototypejs | 2 Debian Linux, Prototype | 2021-07-27 | 7.5 HIGH | N/A |
| Unspecified vulnerability in Prototype JavaScript framework (prototypejs) before 1.6.0.2 allows attackers to make "cross-site ajax requests" via unknown vectors. | |||||
| CVE-2021-29533 | 1 Google | 1 Tensorflow | 2021-07-27 | 2.1 LOW | 5.5 MEDIUM |
| TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a denial of service via a `CHECK` failure by passing an empty image to `tf.raw_ops.DrawBoundingBoxes`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/ea34a18dc3f5c8d80a40ccca1404f343b5d55f91/tensorflow/core/kernels/image/draw_bounding_box_op.cc#L148-L165) uses `CHECK_*` assertions instead of `OP_REQUIRES` to validate user controlled inputs. Whereas `OP_REQUIRES` allows returning an error condition back to the user, the `CHECK_*` macros result in a crash if the condition is false, similar to `assert`. In this case, `height` is 0 from the `images` input. This results in `max_box_row_clamp` being negative and the assertion being falsified, followed by aborting program execution. 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. | |||||
| CVE-2021-29534 | 1 Google | 1 Tensorflow | 2021-07-27 | 2.1 LOW | 5.5 MEDIUM |
| 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 `tf.raw_ops.SparseConcat`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/b432a38fe0e1b4b904a6c222cbce794c39703e87/tensorflow/core/kernels/sparse_concat_op.cc#L76) takes the values specified in `shapes[0]` as dimensions for the output shape. The `TensorShape` constructor(https://github.com/tensorflow/tensorflow/blob/6f9896890c4c703ae0a0845394086e2e1e523299/tensorflow/core/framework/tensor_shape.cc#L183-L188) uses a `CHECK` operation which triggers when `InitDims`(https://github.com/tensorflow/tensorflow/blob/6f9896890c4c703ae0a0845394086e2e1e523299/tensorflow/core/framework/tensor_shape.cc#L212-L296) returns a non-OK status. This is a legacy implementation of the constructor and operations should use `BuildTensorShapeBase` or `AddDimWithStatus` to prevent `CHECK`-failures in the presence of overflows. 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. | |||||
| CVE-2021-29538 | 1 Google | 1 Tensorflow | 2021-07-27 | 2.1 LOW | 5.5 MEDIUM |
| TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a division by zero to occur in `Conv2DBackpropFilter`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/1b0296c3b8dd9bd948f924aa8cd62f87dbb7c3da/tensorflow/core/kernels/conv_grad_filter_ops.cc#L513-L522) computes a divisor based on user provided data (i.e., the shape of the tensors given as arguments). If all shapes are empty then `work_unit_size` is 0. Since there is no check for this case before division, this results in a runtime exception, with potential to be abused for a denial of service. 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. | |||||
| CVE-2021-29539 | 1 Google | 1 Tensorflow | 2021-07-27 | 2.1 LOW | 5.5 MEDIUM |
| TensorFlow is an end-to-end open source platform for machine learning. Calling `tf.raw_ops.ImmutableConst`(https://www.tensorflow.org/api_docs/python/tf/raw_ops/ImmutableConst) with a `dtype` of `tf.resource` or `tf.variant` results in a segfault in the implementation as code assumes that the tensor contents are pure scalars. We have patched the issue in 4f663d4b8f0bec1b48da6fa091a7d29609980fa4 and will release TensorFlow 2.5.0 containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved. If using `tf.raw_ops.ImmutableConst` in code, you can prevent the segfault by inserting a filter for the `dtype` argument. | |||||
| CVE-2021-29543 | 1 Google | 1 Tensorflow | 2021-07-27 | 2.1 LOW | 5.5 MEDIUM |
| 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 `tf.raw_ops.CTCGreedyDecoder`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/1615440b17b364b875eb06f43d087381f1460a65/tensorflow/core/kernels/ctc_decoder_ops.cc#L37-L50) has a `CHECK_LT` inserted to validate some invariants. When this condition is false, the program aborts, instead of returning a valid error to the user. This abnormal termination can be weaponized in denial of service attacks. 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. | |||||
| CVE-2021-29544 | 1 Google | 1 Tensorflow | 2021-07-27 | 2.1 LOW | 5.5 MEDIUM |
| 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 `tf.raw_ops.QuantizeAndDequantizeV4Grad`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/95078c145b5a7a43ee046144005f733092756ab5/tensorflow/core/kernels/quantize_and_dequantize_op.cc#L162-L163) does not validate the rank of the `input_*` tensors. In turn, this results in the tensors being passes as they are to `QuantizeAndDequantizePerChannelGradientImpl`(https://github.com/tensorflow/tensorflow/blob/95078c145b5a7a43ee046144005f733092756ab5/tensorflow/core/kernels/quantize_and_dequantize_op.h#L295-L306). However, the `vec<T>` method, requires the rank to 1 and triggers a `CHECK` failure otherwise. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2 as this is the only other affected version. | |||||
| CVE-2021-29545 | 1 Google | 1 Tensorflow | 2021-07-27 | 2.1 LOW | 5.5 MEDIUM |
| 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. | |||||
| CVE-2021-29547 | 1 Google | 1 Tensorflow | 2021-07-27 | 2.1 LOW | 5.5 MEDIUM |
| TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a segfault and denial of service via accessing data outside of bounds in `tf.raw_ops.QuantizedBatchNormWithGlobalNormalization`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/55a97caa9e99c7f37a0bbbeb414dc55553d3ae7f/tensorflow/core/kernels/quantized_batch_norm_op.cc#L176-L189) assumes the inputs are not empty. If any of these inputs is empty, `.flat<T>()` is an empty buffer, so accessing the element at index 0 is accessing data outside of bounds. 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. | |||||
| CVE-2021-29549 | 1 Google | 1 Tensorflow | 2021-07-27 | 2.1 LOW | 5.5 MEDIUM |
| TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a runtime division by zero error and denial of service in `tf.raw_ops.QuantizedBatchNormWithGlobalNormalization`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/6f26b3f3418201479c264f2a02000880d8df151c/tensorflow/core/kernels/quantized_add_op.cc#L289-L295) computes a modulo operation without validating that the divisor is not zero. Since `vector_num_elements` is determined based on input shapes(https://github.com/tensorflow/tensorflow/blob/6f26b3f3418201479c264f2a02000880d8df151c/tensorflow/core/kernels/quantized_add_op.cc#L522-L544), a user can trigger scenarios where this quantity is 0. 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. | |||||
| CVE-2021-29550 | 1 Google | 1 Tensorflow | 2021-07-27 | 2.1 LOW | 5.5 MEDIUM |
| TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a runtime division by zero error and denial of service in `tf.raw_ops.FractionalAvgPool`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/acc8ee69f5f46f92a3f1f11230f49c6ac266f10c/tensorflow/core/kernels/fractional_avg_pool_op.cc#L85-L89) computes a divisor quantity by dividing two user controlled values. The user controls the values of `input_size[i]` and `pooling_ratio_[i]` (via the `value.shape()` and `pooling_ratio` arguments). If the value in `input_size[i]` is smaller than the `pooling_ratio_[i]`, then the floor operation results in `output_size[i]` being 0. The `DCHECK_GT` line is a no-op outside of debug mode, so in released versions of TF this does not trigger. Later, these computed values are used as arguments(https://github.com/tensorflow/tensorflow/blob/acc8ee69f5f46f92a3f1f11230f49c6ac266f10c/tensorflow/core/kernels/fractional_avg_pool_op.cc#L96-L99) to `GeneratePoolingSequence`(https://github.com/tensorflow/tensorflow/blob/acc8ee69f5f46f92a3f1f11230f49c6ac266f10c/tensorflow/core/kernels/fractional_pool_common.cc#L100-L108). There, the first computation is a division in a modulo operation. Since `output_length` can be 0, this results in runtime crashing. 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. | |||||
| CVE-2021-29551 | 1 Google | 1 Tensorflow | 2021-07-27 | 2.1 LOW | 5.5 MEDIUM |
| TensorFlow is an end-to-end open source platform for machine learning. The implementation of `MatrixTriangularSolve`(https://github.com/tensorflow/tensorflow/blob/8cae746d8449c7dda5298327353d68613f16e798/tensorflow/core/kernels/linalg/matrix_triangular_solve_op_impl.h#L160-L240) fails to terminate kernel execution if one validation condition fails. 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. | |||||
| CVE-2021-29552 | 1 Google | 1 Tensorflow | 2021-07-27 | 2.1 LOW | 5.5 MEDIUM |
| TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a denial of service by controlling the values of `num_segments` tensor argument for `UnsortedSegmentJoin`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/a2a607db15c7cd01d754d37e5448d72a13491bdb/tensorflow/core/kernels/unsorted_segment_join_op.cc#L92-L93) assumes that the `num_segments` tensor is a valid scalar. Since the tensor is empty the `CHECK` involved in `.scalar<T>()()` that checks that the number of elements is exactly 1 will be invalidated and this would result in process termination. 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. | |||||