Buffer Copy without Checking Size of Input ('Classic Buffer Overflow')

A flaw was found in the ZeroMQ server in versions before 4.3.3. This flaw allows a malicious client to cause a stack buffer overflow on the server by sending crafted topic subscription requests and then unsubscribing. The highest threat from this vulnerability is to confidentiality, integrity, as well as system availability.Show less

LookupCol.c in X.Org X through X11R7.7 and libX11 before 1.7.1 might allow remote attackers to execute arbitrary code. The libX11 XLookupColor request (intended for server-side color lookup) contains a flaw allowing a client to send color-name requests with a name longer than the maximum size allowed by the protocol (and also longer than the maximum packet size for normal-sized packets). The user-controlled data exceeding the maximum size is then interpreted by the server as additional X protocol requests and executed, e.g., to disable X server authorization completely. For example, if the victim encounters malicious terminal control sequences for color codes, then the attacker may be able to take full control of the running graphical session.Show less

A buffer overflow vulnerability exists in Windows File Resource Profiles in 9.X allows a remote authenticated user with privileges to browse SMB shares to execute arbitrary code as the root user. As of version 9.1R3, this permission is not enabled by default.Show less

Buffer Overflow vulnerability in FFmpeg 4.2 in mov_write_video_tag due to the out of bounds in libavformat/movenc.c, which could let a remote malicious user obtain sensitive information, cause a Denial of Service, or execute arbitrary code.Show less

Buffer Overflow vulnerability in FFMpeg 4.2.3 in dnn_execute_layer_pad in libavfilter/dnn/dnn_backend_native_layer_pad.c due to a call to memcpy without length checks, which could let a remote malicious user execute arbitrary code.Show less

A local buffer overflow vulnerability was discovered in HPE Integrated Lights-Out 4 (iLO 4); HPE SimpliVity 380 Gen9; HPE Integrated Lights-Out 5 (iLO 5) for HPE Gen10 Servers; HPE SimpliVity 380 Gen10; HPE SimpliVity 2600; HPE SimpliVity 380 Gen10 G; HPE SimpliVity 325; HPE SimpliVity 380 Gen10 H version(s): Prior to version 2.78.Show less

Buffer overflow vulnerability in libsolv 2020-12-13 via the Solver * testcase_read(Pool *pool, FILE *fp, const char *testcase, Queue *job, char **resultp, int *resultflagsp function at src/testcase.c: line 2334, which could cause a denial of serviceShow less

TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a heap buffer overflow in Eigen implementation of `tf.raw_ops.BandedTriangularSolve`. The implementation(https://github.com/tensorflow/tensorflow/blob/eccb7ec454e6617738554a255d77f08e60ee0808/tensorflow/core/kernels/linalg/banded_triangular_solve_op.cc#L269-L278) calls `ValidateInputTensors` for input validation but fails to validate that the two tensors are not empty. Furthermore, since `OP_REQUIRES` macro only stops execution of current function after setting `ctx->status()` to a non-OK value, callers of helper functions that use `OP_REQUIRES` must check value of `ctx->status()` before continuing. This doesn't happen in this op's implementation(https://github.com/tensorflow/tensorflow/blob/eccb7ec454e6617738554a255d77f08e60ee0808/tensorflow/core/kernels/linalg/banded_triangular_solve_op.cc#L219), hence the validation that is present is also not effective. 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 to occur in `Conv2DBackpropFilter`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/1b0296c3b8dd9bd948f924aa8cd62f87dbb7c3da/tensorflow/core/kernels/conv_grad_filter_ops.cc#L495-L497) computes the size of the filter tensor but does not validate that it matches the number of elements in `filter_sizes`. Later, when reading/writing to this buffer, code uses the value computed here, instead of the number of elements in the tensor. 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. Missing validation between arguments to `tf.raw_ops.Conv3DBackprop*` operations can result in heap buffer overflows. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/4814fafb0ca6b5ab58a09411523b2193fed23fed/tensorflow/core/kernels/conv_grad_shape_utils.cc#L94-L153) assumes that the `input`, `filter_sizes` and `out_backprop` tensors have the same shape, as they are accessed in parallel. 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. If the `splits` argument of `RaggedBincount` does not specify a valid `SparseTensor`(https://www.tensorflow.org/api_docs/python/tf/sparse/SparseTensor), then an attacker can trigger a heap buffer overflow. This will cause a read from outside the bounds of the `splits` tensor buffer in the implementation of the `RaggedBincount` op(https://github.com/tensorflow/tensorflow/blob/8b677d79167799f71c42fd3fa074476e0295413a/tensorflow/core/kernels/bincount_op.cc#L430-L433). Before the `for` loop, `batch_idx` is set to 0. The user controls the `splits` array, making it contain only one element, 0. Thus, the code in the `while` loop would increment `batch_idx` and then try to read `splits(1)`, which is outside of bounds. 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, as these are also affected.Show less