rainbutterfly/cve_corpus huggingface dataset:
CVE-2020-15199 Denial of Service in Tensorflow
In Tensorflow before version 2.3.1, the `RaggedCountSparseOutput` does not validate that the input arguments form a valid ragged tensor. In particular, there is no validation that the `splits` tensor has the minimum required number of elements. Code uses this quantity to initialize a different data structure. Since `BatchedMap` is equivalent to a vector, it needs to have at least one element to not be `nullptr`. If user passes a `splits` tensor that is empty or has exactly one element, we get a `SIGABRT` signal raised by the operating system. The issue is patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and is released in TensorFlow version 2.3.1.
Problem types: cwe_id: CWE-20
description: CWE-20: Improper Input Validation
Affected products: vendor: tensorflow
product: tensorflow
CVSS v3.1: attackComplexity: HIGH
attackVector: NETWORK
availabilityImpact: HIGH
baseScore: 5.9
baseSeverity: MEDIUM
confidentialityImpact: NONE
integrityImpact: NONE
privilegesRequired: NONE
scope: UNCHANGED
userInteraction: NONE
vectorString: CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:H
version: 3.1
Published: 2020-09-25T18:40:20.000Z
CVE-2020-15200 Segfault in Tensorflow
In Tensorflow before version 2.3.1, the `RaggedCountSparseOutput` implementation does not validate that the input arguments form a valid ragged tensor. In particular, there is no validation that the values in the `splits` tensor generate a valid partitioning of the `values` tensor. Thus, the code sets up conditions to cause a heap buffer overflow. A `BatchedMap` is equivalent to a vector where each element is a hashmap. However, if the first element of `splits_values` is not 0, `batch_idx` will never be 1, hence there will be no hashmap at index 0 in `per_batch_counts`. Trying to access that in the user code results in a segmentation fault. The issue is patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and is released in TensorFlow version 2.3.1.
Problem types: cwe_id: CWE-20
description: CWE-20: Improper Input Validation
cwe_id: CWE-122
description: CWE-122: Heap-based Buffer Overflow
Affected products: vendor: tensorflow
product: tensorflow
CVSS v3.1: attackComplexity: HIGH
attackVector: NETWORK
availabilityImpact: HIGH
baseScore: 5.9
baseSeverity: MEDIUM
confidentialityImpact: NONE
integrityImpact: NONE
privilegesRequired: NONE
scope: UNCHANGED
userInteraction: NONE
vectorString: CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:H
version: 3.1
Published: 2020-09-25T18:40:15.000Z
CVE-2020-15201 Heap buffer overflow in Tensorflow
In Tensorflow before version 2.3.1, the `RaggedCountSparseOutput` implementation does not validate that the input arguments form a valid ragged tensor. In particular, there is no validation that the values in the `splits` tensor generate a valid partitioning of the `values` tensor. Hence, the code is prone to heap buffer overflow. If `split_values` does not end with a value at least `num_values` then the `while` loop condition will trigger a read outside of the bounds of `split_values` once `batch_idx` grows too large. The issue is patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and is released in TensorFlow version 2.3.1.
Problem types: cwe_id: CWE-20
description: CWE-20: Improper Input Validation
cwe_id: CWE-122
description: CWE-122: Heap-based Buffer Overflow
Affected products: vendor: tensorflow
product: tensorflow
CVSS v3.1: attackComplexity: HIGH
attackVector: NETWORK
availabilityImpact: NONE
baseScore: 4.8
baseSeverity: MEDIUM
confidentialityImpact: LOW
integrityImpact: LOW
privilegesRequired: NONE
scope: UNCHANGED
userInteraction: NONE
vectorString: CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N
version: 3.1
Published: 2020-09-25T18:46:21.000Z
CVE-2020-15202 Integer truncation in Shard API usage
In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the `Shard` API in TensorFlow expects the last argument to be a function taking two `int64` (i.e., `long long`) arguments. However, there are several places in TensorFlow where a lambda taking `int` or `int32` arguments is being used. In these cases, if the amount of work to be parallelized is large enough, integer truncation occurs. Depending on how the two arguments of the lambda are used, this can result in segfaults, read/write outside of heap allocated arrays, stack overflows, or data corruption. The issue is patched in commits 27b417360cbd671ef55915e4bb6bb06af8b8a832 and ca8c013b5e97b1373b3bb1c97ea655e69f31a575, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.
Problem types: cwe_id: CWE-197
description: CWE-197: Numeric Truncation Error
cwe_id: CWE-754
description: CWE-754: Improper Check for Unusual or Exceptional Conditions
Affected products: vendor: tensorflow
product: tensorflow
CVSS v3.1: attackComplexity: HIGH
attackVector: NETWORK
availabilityImpact: HIGH
baseScore: 9
baseSeverity: CRITICAL
confidentialityImpact: HIGH
integrityImpact: HIGH
privilegesRequired: NONE
scope: CHANGED
userInteraction: NONE
vectorString: CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:C/C:H/I:H/A:H
version: 3.1
Published: 2020-09-25T18:46:15.000Z
CVE-2020-15203 Denial of Service in Tensorflow
In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, by controlling the `fill` argument of tf.strings.as_string, a malicious attacker is able to trigger a format string vulnerability due to the way the internal format use in a `printf` call is constructed. This may result in segmentation fault. The issue is patched in commit 33be22c65d86256e6826666662e40dbdfe70ee83, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.
Problem types: cwe_id: CWE-20
description: CWE-20: Improper Input Validation
Affected products: vendor: tensorflow
product: tensorflow
CVSS v3.1: attackComplexity: LOW
attackVector: NETWORK
availabilityImpact: HIGH
baseScore: 7.5
baseSeverity: HIGH
confidentialityImpact: NONE
integrityImpact: NONE
privilegesRequired: NONE
scope: UNCHANGED
userInteraction: NONE
vectorString: CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H
version: 3.1
Published: 2020-09-25T18:46:08.000Z
CVE-2020-15204 Segfault in Tensorflow
In eager mode, TensorFlow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1 does not set the session state. Hence, calling `tf.raw_ops.GetSessionHandle` or `tf.raw_ops.GetSessionHandleV2` results in a null pointer dereference In linked snippet, in eager mode, `ctx->session_state()` returns `nullptr`. Since code immediately dereferences this, we get a segmentation fault. The issue is patched in commit 9a133d73ae4b4664d22bd1aa6d654fec13c52ee1, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.
Problem types: cwe_id: CWE-476
description: CWE-476: NULL Pointer Dereference
Affected products: vendor: tensorflow
product: tensorflow
CVSS v3.1: attackComplexity: LOW
attackVector: NETWORK
availabilityImpact: LOW
baseScore: 5.3
baseSeverity: MEDIUM
confidentialityImpact: NONE
integrityImpact: NONE
privilegesRequired: NONE
scope: UNCHANGED
userInteraction: NONE
vectorString: CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:L
version: 3.1
Published: 2020-09-25T18:46:02.000Z
CVE-2020-15205 Data leak in Tensorflow
In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the `data_splits` argument of `tf.raw_ops.StringNGrams` lacks validation. This allows a user to pass values that can cause heap overflow errors and even leak contents of memory In the linked code snippet, all the binary strings after `ee ff` are contents from the memory stack. Since these can contain return addresses, this data leak can be used to defeat ASLR. The issue is patched in commit 0462de5b544ed4731aa2fb23946ac22c01856b80, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.
Problem types: cwe_id: CWE-122
description: CWE-122: Heap-based Buffer Overflow
cwe_id: CWE-119
description: CWE-119: Improper Restriction of Operations within the Bounds of a Memory Buffer
Affected products: vendor: tensorflow
product: tensorflow
CVSS v3.1: attackComplexity: HIGH
attackVector: NETWORK
availabilityImpact: HIGH
baseScore: 9
baseSeverity: CRITICAL
confidentialityImpact: HIGH
integrityImpact: HIGH
privilegesRequired: NONE
scope: CHANGED
userInteraction: NONE
vectorString: CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:C/C:H/I:H/A:H
version: 3.1
Published: 2020-09-25T18:45:57.000Z
CVE-2020-15206 Denial of Service in Tensorflow
In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, changing the TensorFlow's `SavedModel` protocol buffer and altering the name of required keys results in segfaults and data corruption while loading the model. This can cause a denial of service in products using `tensorflow-serving` or other inference-as-a-service installments. Fixed were added in commits f760f88b4267d981e13f4b302c437ae800445968 and fcfef195637c6e365577829c4d67681695956e7d (both going into TensorFlow 2.2.0 and 2.3.0 but not yet backported to earlier versions). However, this was not enough, as #41097 reports a different failure mode. The issue is patched in commit adf095206f25471e864a8e63a0f1caef53a0e3a6, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.
Problem types: cwe_id: CWE-20
description: CWE-20: Improper Input Validation
Affected products: vendor: tensorflow
product: tensorflow
CVSS v3.1: attackComplexity: HIGH
attackVector: NETWORK
availabilityImpact: HIGH
baseScore: 9
baseSeverity: CRITICAL
confidentialityImpact: HIGH
integrityImpact: HIGH
privilegesRequired: NONE
scope: CHANGED
userInteraction: NONE
vectorString: CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:C/C:H/I:H/A:H
version: 3.1
Published: 2020-09-25T18:45:51.000Z
CVE-2020-15207 Segfault and data corruption in tensorflow-lite
In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, to mimic Python's indexing with negative values, TFLite uses `ResolveAxis` to convert negative values to positive indices. However, the only check that the converted index is now valid is only present in debug builds. If the `DCHECK` does not trigger, then code execution moves ahead with a negative index. This, in turn, results in accessing data out of bounds which results in segfaults and/or data corruption. The issue is patched in commit 2d88f470dea2671b430884260f3626b1fe99830a, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.
Problem types: cwe_id: CWE-119
description: CWE-119: Improper Restriction of Operations within the Bounds of a Memory Buffer
Affected products: vendor: tensorflow
product: tensorflow
CVSS v3.1: attackComplexity: HIGH
attackVector: NETWORK
availabilityImpact: HIGH
baseScore: 8.7
baseSeverity: HIGH
confidentialityImpact: NONE
integrityImpact: HIGH
privilegesRequired: NONE
scope: CHANGED
userInteraction: NONE
vectorString: CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:C/C:N/I:H/A:H
version: 3.1
Published: 2020-09-25T18:45:46.000Z
CVE-2020-15208 Data corruption in tensorflow-lite
In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, when determining the common dimension size of two tensors, TFLite uses a `DCHECK` which is no-op outside of debug compilation modes. Since the function always returns the dimension of the first tensor, malicious attackers can craft cases where this is larger than that of the second tensor. In turn, this would result in reads/writes outside of bounds since the interpreter will wrongly assume that there is enough data in both tensors. The issue is patched in commit 8ee24e7949a203d234489f9da2c5bf45a7d5157d, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.
Problem types: cwe_id: CWE-125
description: CWE-125: Out-of-bounds Read
cwe_id: CWE-787
description: CWE-787: Out-of-bounds Write
Affected products: vendor: tensorflow
product: tensorflow
CVSS v3.1: attackComplexity: HIGH
attackVector: NETWORK
availabilityImpact: NONE
baseScore: 7.4
baseSeverity: HIGH
confidentialityImpact: HIGH
integrityImpact: HIGH
privilegesRequired: NONE
scope: UNCHANGED
userInteraction: NONE
vectorString: CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:H/I:H/A:N
version: 3.1
Published: 2020-09-25T18:45:40.000Z
CVE-2020-15209 Null pointer dereference in tensorflow-lite
In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, a crafted TFLite model can force a node to have as input a tensor backed by a `nullptr` buffer. This can be achieved by changing a buffer index in the flatbuffer serialization to convert a read-only tensor to a read-write one. The runtime assumes that these buffers are written to before a possible read, hence they are initialized with `nullptr`. However, by changing the buffer index for a tensor and implicitly converting that tensor to be a read-write one, as there is nothing in the model that writes to it, we get a null pointer dereference. The issue is patched in commit 0b5662bc, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.
Problem types: cwe_id: CWE-476
description: CWE-476: NULL Pointer Dereference
Affected products: vendor: tensorflow
product: tensorflow
CVSS v3.1: attackComplexity: HIGH
attackVector: NETWORK
availabilityImpact: HIGH
baseScore: 5.9
baseSeverity: MEDIUM
confidentialityImpact: NONE
integrityImpact: NONE
privilegesRequired: NONE
scope: UNCHANGED
userInteraction: NONE
vectorString: CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:H
version: 3.1
Published: 2020-09-25T18:45:35.000Z
CVE-2020-15210 Segmentation fault in tensorflow-lite
In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, if a TFLite saved model uses the same tensor as both input and output of an operator, then, depending on the operator, we can observe a segmentation fault or just memory corruption. We have patched the issue in d58c96946b and will release patch releases for all versions between 1.15 and 2.3. We recommend users to upgrade to TensorFlow 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.
Problem types: cwe_id: CWE-20
description: CWE-20: Improper Input Validation
Affected products: vendor: tensorflow
product: tensorflow
CVSS v3.1: attackComplexity: HIGH
attackVector: NETWORK
availabilityImpact: HIGH
baseScore: 6.5
baseSeverity: MEDIUM
confidentialityImpact: NONE
integrityImpact: LOW
privilegesRequired: NONE
scope: UNCHANGED
userInteraction: NONE
vectorString: CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:L/A:H
version: 3.1
Published: 2020-09-25T18:45:30.000Z
CVE-2020-15211 Out of bounds access in tensorflow-lite
In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code.
Problem types: cwe_id: CWE-125
description: CWE-125: Out-of-bounds Read
cwe_id: CWE-787
description: CWE-787: Out-of-bounds Write
Affected products: vendor: tensorflow
product: tensorflow
CVSS v3.1: attackComplexity: HIGH
attackVector: NETWORK
availabilityImpact: NONE
baseScore: 4.8
baseSeverity: MEDIUM
confidentialityImpact: LOW
integrityImpact: LOW
privilegesRequired: NONE
scope: UNCHANGED
userInteraction: NONE
vectorString: CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N
version: 3.1
Published: 2020-09-25T18:45:24.000Z
CVE-2020-15212 Out of bounds access in tensorflow-lite
In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger writes outside of bounds of heap allocated buffers by inserting negative elements in the segment ids tensor. Users having access to `segment_ids_data` can alter `output_index` and then write to outside of `output_data` buffer. This might result in a segmentation fault but it can also be used to further corrupt the memory and can be chained with other vulnerabilities to create more advanced exploits. The issue is patched in commit 204945b19e44b57906c9344c0d00120eeeae178a and is released in TensorFlow versions 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that the segment ids are all positive, although this only handles the case when the segment ids are stored statically in the model. A similar validation could be done if the segment ids are generated at runtime between inference steps. If the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code.
Problem types: cwe_id: CWE-787
description: CWE-787: Out-of-bounds Write
Affected products: vendor: tensorflow
product: tensorflow
CVSS v3.1: attackComplexity: HIGH
attackVector: NETWORK
availabilityImpact: HIGH
baseScore: 8.1
baseSeverity: HIGH
confidentialityImpact: LOW
integrityImpact: LOW
privilegesRequired: NONE
scope: CHANGED
userInteraction: NONE
vectorString: CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:C/C:L/I:L/A:H
version: 3.1
Published: 2020-09-25T18:50:34.000Z
CVE-2020-15213 Denial of service in tensorflow-lite
In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger a denial of service by causing an out of memory allocation in the implementation of segment sum. Since code uses the last element of the tensor holding them to determine the dimensionality of output tensor, attackers can use a very large value to trigger a large allocation. The issue is patched in commit 204945b19e44b57906c9344c0d00120eeeae178a and is released in TensorFlow versions 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to limit the maximum value in the segment ids tensor. This only handles the case when the segment ids are stored statically in the model, but a similar validation could be done if the segment ids are generated at runtime, between inference steps. However, if the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code.
Problem types: cwe_id: CWE-119
description: CWE-119: Improper Restriction of Operations within the Bounds of a Memory Buffer
cwe_id: CWE-770
description: CWE-770: Allocation of Resources Without Limits or Throttling
Affected products: vendor: tensorflow
product: tensorflow
CVSS v3.1: attackComplexity: HIGH
attackVector: NETWORK
availabilityImpact: LOW
baseScore: 4
baseSeverity: MEDIUM
confidentialityImpact: NONE
integrityImpact: NONE
privilegesRequired: NONE
scope: CHANGED
userInteraction: NONE
vectorString: CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:C/C:N/I:N/A:L
version: 3.1
Published: 2020-09-25T18:50:29.000Z
CVE-2020-15214 Out of bounds write in tensorflow-lite
In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger a write out bounds / segmentation fault if the segment ids are not sorted. Code assumes that the segment ids are in increasing order, using the last element of the tensor holding them to determine the dimensionality of output tensor. This results in allocating insufficient memory for the output tensor and in a write outside the bounds of the output array. This usually results in a segmentation fault, but depending on runtime conditions it can provide for a write gadget to be used in future memory corruption-based exploits. The issue is patched in commit 204945b19e44b57906c9344c0d00120eeeae178a and is released in TensorFlow versions 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that the segment ids are sorted, although this only handles the case when the segment ids are stored statically in the model. A similar validation could be done if the segment ids are generated at runtime between inference steps. If the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code.
Problem types: cwe_id: CWE-787
description: CWE-787: Out-of-bounds Write
Affected products: vendor: tensorflow
product: tensorflow
CVSS v3.1: attackComplexity: HIGH
attackVector: NETWORK
availabilityImpact: HIGH
baseScore: 8.1
baseSeverity: HIGH
confidentialityImpact: LOW
integrityImpact: LOW
privilegesRequired: NONE
scope: CHANGED
userInteraction: NONE
vectorString: CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:C/C:L/I:L/A:H
version: 3.1
Published: 2020-09-25T18:50:23.000Z
CVE-2020-15215 Context isolation bypass in Electron
Electron before versions 11.0.0-beta.6, 10.1.2, 9.3.1 or 8.5.2 is vulnerable to a context isolation bypass. Apps using both `contextIsolation` and `sandbox: true` are affected. Apps using both `contextIsolation` and `nodeIntegrationInSubFrames: true` are affected. This is a context isolation bypass, meaning that code running in the main world context in the renderer can reach into the isolated Electron context and perform privileged actions.
Problem types: cwe_id: CWE-693
description: CWE-693 Protection Mechanism Failure
cwe_id: CWE-668
description: CWE-668 Exposure of Resource to Wrong Sphere
Affected products: vendor: electron
product: electron
CVSS v3.1: attackComplexity: HIGH
attackVector: NETWORK
availabilityImpact: LOW
baseScore: 5.6
baseSeverity: MEDIUM
confidentialityImpact: LOW
integrityImpact: LOW
privilegesRequired: NONE
scope: UNCHANGED
userInteraction: NONE
vectorString: CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:L
version: 3.1
Published: 2020-10-06T18:00:17.000Z
CVE-2020-15216 Signature Validation Bypass in goxmldsig
In goxmldsig (XML Digital Signatures implemented in pure Go) before version 1.1.0, with a carefully crafted XML file, an attacker can completely bypass signature validation and pass off an altered file as a signed one. A patch is available, all users of goxmldsig should upgrade to at least revision f6188febf0c29d7ffe26a0436212b19cb9615e64 or version 1.1.0
Problem types: cwe_id: CWE-347
description: CWE-347 Improper Verification of Cryptographic Signature
Affected products: vendor: russellhaering
product: goxmldsig
CVSS v3.1: attackComplexity: HIGH
attackVector: NETWORK
availabilityImpact: NONE
baseScore: 5.3
baseSeverity: MEDIUM
confidentialityImpact: NONE
integrityImpact: HIGH
privilegesRequired: NONE
scope: UNCHANGED
userInteraction: REQUIRED
vectorString: CVSS:3.1/AV:N/AC:H/PR:N/UI:R/S:U/C:N/I:H/A:N
version: 3.1
Published: 2020-09-29T16:00:18.000Z
CVE-2020-15217 User data exposure in GLPI
In GLPI before version 9.5.2, there is a leakage of user information through the public FAQ. The issue was introduced in version 9.5.0 and patched in 9.5.2. As a workaround, disable public access to the FAQ.
Problem types: cwe_id: CWE-79
description: CWE-79 Cross-site Scripting (XSS)
Affected products: vendor: glpi-project
product: glpi
CVSS v3.1: attackComplexity: LOW
attackVector: NETWORK
availabilityImpact: NONE
baseScore: 5.3
baseSeverity: MEDIUM
confidentialityImpact: LOW
integrityImpact: NONE
privilegesRequired: NONE
scope: UNCHANGED
userInteraction: NONE
vectorString: CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:N/A:N
version: 3.1
Published: 2020-10-07T19:10:13.000Z