alirezaaminzadeh/cve-riskrank huggingface dataset:
CVE-2021-29492 (published 2021-05-28)
Envoy is a cloud-native edge/middle/service proxy. Envoy does not decode escaped slash sequences `%2F` and `%5C` in HTTP URL paths in versions 1.18.2 and before. A remote attacker may craft a path with escaped slashes, e.g. `/something%2F..%2Fadmin`, to bypass access control, e.g. a block on `/admin`. A backend server could then decode slash sequences and normalize path and provide an attacker access beyond the scope provided for by the access control policy. ### Impact Escalation of Privileges when using RBAC or JWT filters with enforcement based on URL path. Users with back end servers that interpret `%2F` and `/` and `%5C` and `\` interchangeably are impacted. ### Attack Vector URL paths containing escaped slash characters delivered by untrusted client. Patches in versions 1.18.3, 1.17.3, 1.16.4, 1.15.5 contain new path normalization option to decode escaped slash characters. As a workaround, if back end servers treat `%2F` and `/` and `%5C` and `\` interchangeably and a URL path based access control is configured, one may reconfigure the back end server to not treat `%2F` and `/` and `%5C` and `\` interchangeably.
Severity: HIGH (CVSS 3.1 vector: CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:C/C:L/I:L/A:L)
Weakness: CWE-22
Affected vendors: envoyproxy
Affected products: envoy
CVE-2021-29493 (published 2021-05-06)
Kennnyshiwa-cogs contains cogs for Red Discordbot. An RCE exploit has been found in the Tickets module of kennnyshiwa-cogs. This exploit allows discord users to craft a message that can reveal sensitive and harmful information. Users can upgrade to version 5a84d60018468e5c0346f7ee74b2b4650a6dade7 to receive a patch or, as a workaround, unload tickets to render the exploit unusable.
Severity: HIGH (CVSS 3.1 vector: CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H)
Weakness: CWE-94
Affected vendors: kennnyshiwa-cogs_project
Affected products: kennnyshiwa-cogs
CVE-2021-29495 (published 2021-05-07)
Nim is a statically typed compiled systems programming language. In Nim standard library before 1.4.2, httpClient SSL/TLS certificate verification was disabled by default. Users can upgrade to version 1.4.2 to receive a patch or, as a workaround, set "verifyMode = CVerifyPeer" as documented.
Severity: HIGH (CVSS 3.1 vector: CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:H/A:N)
Weakness: CWE-295
Affected vendors: nim-lang
Affected products: nim
CVE-2021-29499 (published 2021-05-07)
SIF is an open source implementation of the Singularity Container Image Format. The `siftool new` command and func siftool.New() produce predictable UUID identifiers due to insecure randomness in the version of the `github.com/satori/go.uuid` module used as a dependency. A patch is available in version >= v1.2.3 of the module. Users are encouraged to upgrade. As a workaround, users passing CreateInfo struct should ensure the `ID` field is generated using a version of `github.com/satori/go.uuid` that is not vulnerable to this issue.
Severity: HIGH (CVSS 3.1 vector: CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:N)
Weakness: CWE-330
Affected vendors: sylabs
Affected products: singularity_image_format
CVE-2021-29500 (published 2021-06-04)
bubble fireworks is an open source java package relating to Spring Framework. In bubble fireworks before version 2021.BUILD-SNAPSHOT there is a vulnerability in which the package did not properly verify the signature of JSON Web Tokens. This allows to forgery of valid JWTs.
Severity: HIGH (CVSS 3.1 vector: CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:H/A:N)
Weakness: CWE-347
Affected vendors: bubble_fireworks_project
Affected products: bubble_fireworks
CVE-2021-29501 (published 2021-05-10)
Ticketer is a command based ticket system cog (plugin) for the red discord bot. A vulnerability allowing discord users to expose sensitive information has been found in the Ticketer cog. Please upgrade to version 1.0.1 as soon as possible. As a workaround users may unload the ticketer cog to disable the exploitable code.
Severity: MEDIUM (CVSS 3.1 vector: CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:N/A:N)
Weakness: CWE-74
Affected vendors: dav-cogs_project
Affected products: dav-cogs
CVE-2021-29502 (published 2021-05-10)
WarnSystem is a cog (plugin) for the Red discord bot. A vulnerability has been found in the code that allows any user to access sensible informations by setting up a specific template which is not properly sanitized. The problem has been patched in version 1.3.18. Users should update and type `!warnsysteminfo` to check that their version is 1.3.18 or above. As a workaround users may unload the WarnSystem cog or disable the `!warnset description` command globally.
Severity: MEDIUM (CVSS 3.1 vector: CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:N/A:N)
Weakness: CWE-74
Affected vendors: warnsystem_project
Affected products: warnsystem
CVE-2021-29503 (published 2021-05-19)
HedgeDoc is a platform to write and share markdown. HedgeDoc before version 1.8.2 is vulnerable to a cross-site scripting attack using the YAML-metadata of a note. An attacker with write access to a note can embed HTML tags in the Open Graph metadata section of the note, resulting in the frontend rendering the script tag as part of the `<head>` section. Unless your instance prevents guests from editing notes, this vulnerability allows unauthenticated attackers to inject JavaScript into notes that allow guest edits. If your instance prevents guests from editing notes, this vulnerability allows authenticated attackers to inject JavaScript into any note pages they have write-access to. This vulnerability is patched in version 1.8.2. As a workaround, one can disable guest edits until the next update.
Severity: MEDIUM (CVSS 3.1 vector: CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:C/C:L/I:L/A:N)
Weakness: CWE-80
Affected vendors: hedgedoc
Affected products: hedgedoc
CVE-2021-29504 (published 2021-06-07)
WP-CLI is the command-line interface for WordPress. An improper error handling in HTTPS requests management in WP-CLI version 0.12.0 and later allows remote attackers able to intercept the communication to remotely disable the certificate verification on WP-CLI side, gaining full control over the communication content, including the ability to impersonate update servers and push malicious updates towards WordPress instances controlled by the vulnerable WP-CLI agent, or push malicious updates toward WP-CLI itself. The vulnerability stems from the fact that the default behavior of `WP_CLI\Utils\http_request()` when encountering a TLS handshake error is to disable certificate validation and retry the same request. The default behavior has been changed with version 2.5.0 of WP-CLI and the `wp-cli/wp-cli` framework (via https://github.com/wp-cli/wp-cli/pull/5523) so that the `WP_CLI\Utils\http_request()` method accepts an `$insecure` option that is `false` by default and consequently that a TLS handshake failure is a hard error by default. This new default is a breaking change and ripples through to all consumers of `WP_CLI\Utils\http_request()`, including those in separate WP-CLI bundled or third-party packages. https://github.com/wp-cli/wp-cli/pull/5523 has also added an `--insecure` flag to the `cli update` command to counter this breaking change. There is no direct workaround for the default insecure behavior of `wp-cli/wp-cli` versions before 2.5.0. The workaround for dealing with the breaking change in the commands directly affected by the new secure default behavior is to add the `--insecure` flag to manually opt-in to the previous insecure behavior.
Severity: HIGH (CVSS 3.1 vector: CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:H/I:H/A:N)
Weakness: CWE-295
Affected vendors: wp-cli
Affected products: wp-cli
CVE-2021-29505 (published 2021-05-28)
XStream is software for serializing Java objects to XML and back again. A vulnerability in XStream versions prior to 1.4.17 may allow a remote attacker has sufficient rights to execute commands of the host only by manipulating the processed input stream. No user who followed the recommendation to setup XStream's security framework with a whitelist limited to the minimal required types is affected. The vulnerability is patched in version 1.4.17.
Severity: HIGH (CVSS 3.1 vector: CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H)
Weakness: CWE-94
Affected vendors: debian, fedoraproject, netapp, oracle, xstream
Affected products: banking_cash_management, banking_corporate_lending_process_management, banking_credit_facilities_process_management, banking_supply_chain_finance, banking_trade_finance_process_management, business_activity_monitoring, communications_brm_-_elastic_charging_engine, communications_unified_inventory_management, debian_linux, enterprise_manager_ops_center, fedora, retail_customer_insights, retail_xstore_point_of_service, snapmanager, webcenter_portal, webcenter_sites, xstream
CVE-2021-29506 (published 2021-05-13)
GraphHopper is an open-source Java routing engine. In GrassHopper from version 2.0 and before version 2.4, there is a regular expression injection vulnerability that may lead to Denial of Service. This has been patched in 2.4 and 3.0 See this pull request for the fix: https://github.com/graphhopper/graphhopper/pull/2304
Severity: MEDIUM (CVSS 3.1 vector: CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H)
Weakness: CWE-400
Affected vendors: graphhopper
Affected products: graphhopper
CVE-2021-29507 (published 2021-05-28)
GENIVI Diagnostic Log and Trace (DLT) provides a log and trace interface. In versions of GENIVI DLT between 2.10.0 and 2.18.6, a configuration file containing the special characters could cause a vulnerable component to crash. All the applications which are using the configuration file could fail to generate their dlt logs in system. As of time of publication, no patch exists. As a workaround, one may check the integrity of information in configuration file manually.
Severity: MEDIUM (CVSS 3.1 vector: CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H)
Weakness: CWE-20
Affected vendors: genivi
Affected products: diagnostic_log_and_trace
CVE-2021-29508 (published 2021-05-11)
Due to how Wire handles type information in its serialization format, malicious payloads can be passed to a deserializer. e.g. using a surrogate on the sender end, an attacker can pass information about a different type for the receiving end. And by doing so allowing the serializer to create any type on the deserializing end. This is the same issue that exists for .NET BinaryFormatter https://docs.microsoft.com/en-us/visualstudio/code-quality/ca2300?view=vs-2019. This also applies to the fork of Wire.
Severity: CRITICAL (CVSS 3.1 vector: CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:H/A:H)
Weakness: CWE-502
Affected vendors: asynkron
Affected products: wire
CVE-2021-29509 (published 2021-05-11)
Puma is a concurrent HTTP 1.1 server for Ruby/Rack applications. The fix for CVE-2019-16770 was incomplete. The original fix only protected existing connections that had already been accepted from having their requests starved by greedy persistent-connections saturating all threads in the same process. However, new connections may still be starved by greedy persistent-connections saturating all threads in all processes in the cluster. A `puma` server which received more concurrent `keep-alive` connections than the server had threads in its threadpool would service only a subset of connections, denying service to the unserved connections. This problem has been fixed in `puma` 4.3.8 and 5.3.1. Setting `queue_requests false` also fixes the issue. This is not advised when using `puma` without a reverse proxy, such as `nginx` or `apache`, because you will open yourself to slow client attacks (e.g. slowloris). The fix is very small and a git patch is available for those using unsupported versions of Puma.
Severity: HIGH (CVSS 3.1 vector: CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H)
Weakness: CWE-400
Affected vendors: debian, puma
Affected products: debian_linux, puma
CVE-2021-29510 (published 2021-05-13)
Pydantic is a data validation and settings management using Python type hinting. In affected versions passing either `'infinity'`, `'inf'` or `float('inf')` (or their negatives) to `datetime` or `date` fields causes validation to run forever with 100% CPU usage (on one CPU). Pydantic has been patched with fixes available in the following versions: v1.8.2, v1.7.4, v1.6.2. All these versions are available on pypi(https://pypi.org/project/pydantic/#history), and will be available on conda-forge(https://anaconda.org/conda-forge/pydantic) soon. See the changelog(https://pydantic-docs.helpmanual.io/) for details. If you absolutely can't upgrade, you can work around this risk using a validator(https://pydantic-docs.helpmanual.io/usage/validators/) to catch these values. This is not an ideal solution (in particular you'll need a slightly different function for datetimes), instead of a hack like this you should upgrade pydantic. If you are not using v1.8.x, v1.7.x or v1.6.x and are unable to upgrade to a fixed version of pydantic, please create an issue at https://github.com/samuelcolvin/pydantic/issues requesting a back-port, and we will endeavour to release a patch for earlier versions of pydantic.
Severity: HIGH (CVSS 3.1 vector: CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H)
Weakness: CWE-835
Affected vendors: fedoraproject, pydantic
Affected products: fedora, pydantic
CVE-2021-29511 (published 2021-05-12)
evm is a pure Rust implementation of Ethereum Virtual Machine. Prior to the patch, when executing specific EVM opcodes related to memory operations that use `evm_core::Memory::copy_large`, the `evm` crate can over-allocate memory when it is not needed, making it possible for an attacker to perform denial-of-service attack. The flaw was corrected in commit `19ade85`. Users should upgrade to `==0.21.1, ==0.23.1, ==0.24.1, ==0.25.1, >=0.26.1`. There are no workarounds. Please upgrade your `evm` crate version.
Severity: MEDIUM (CVSS 3.1 vector: CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H)
Weakness: CWE-770
Affected vendors: evm_project
Affected products: evm
CVE-2021-29512 (published 2021-05-14)
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.
Severity: HIGH (CVSS 3.1 vector: CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H)
Weakness: CWE-120
Affected vendors: google
Affected products: tensorflow
CVE-2021-29513 (published 2021-05-14)
TensorFlow is an end-to-end open source platform for machine learning. Calling TF operations with tensors of non-numeric types when the operations expect numeric tensors result in null pointer dereferences. The conversion from Python array to C++ array(https://github.com/tensorflow/tensorflow/blob/ff70c47a396ef1e3cb73c90513da4f5cb71bebba/tensorflow/python/lib/core/ndarray_tensor.cc#L113-L169) is vulnerable to a type confusion. 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: HIGH (CVSS 3.1 vector: CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H)
Weakness: CWE-476
Affected vendors: google
Affected products: tensorflow
CVE-2021-29514 (published 2021-05-14)
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-L446). Before the `for` loop, `batch_idx` is set to 0. The attacker sets `splits(0)` to be 7, hence the `while` loop does not execute and `batch_idx` remains 0. This then results in writing to `out(-1, bin)`, which is before the heap allocated buffer for the output tensor. 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.
Severity: HIGH (CVSS 3.1 vector: CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H)
Weakness: CWE-787
Affected vendors: google
Affected products: tensorflow
CVE-2021-29515 (published 2021-05-14)
TensorFlow is an end-to-end open source platform for machine learning. The implementation of `MatrixDiag*` operations(https://github.com/tensorflow/tensorflow/blob/4c4f420e68f1cfaf8f4b6e8e3eb857e9e4c3ff33/tensorflow/core/kernels/linalg/matrix_diag_op.cc#L195-L197) does not validate that the tensor arguments are non-empty. 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: HIGH (CVSS 3.1 vector: CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H)
Weakness: CWE-476
Affected vendors: google
Affected products: tensorflow
CVE-2021-29516 (published 2021-05-14)
TensorFlow is an end-to-end open source platform for machine learning. Calling `tf.raw_ops.RaggedTensorToVariant` with arguments specifying an invalid ragged tensor results in a null pointer dereference. The implementation of `RaggedTensorToVariant` operations(https://github.com/tensorflow/tensorflow/blob/904b3926ed1c6c70380d5313d282d248a776baa1/tensorflow/core/kernels/ragged_tensor_to_variant_op.cc#L39-L40) does not validate that the ragged tensor argument is non-empty. Since `batched_ragged` contains no elements, `batched_ragged.splits` is a null vector, thus `batched_ragged.splits(0)` will result in dereferencing `nullptr`. 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: MEDIUM (CVSS 3.1 vector: CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H)
Weakness: CWE-476
Affected vendors: google
Affected products: tensorflow
CVE-2021-29517 (published 2021-05-14)
TensorFlow is an end-to-end open source platform for machine learning. A malicious user could trigger a division by 0 in `Conv3D` implementation. The implementation(https://github.com/tensorflow/tensorflow/blob/42033603003965bffac51ae171b51801565e002d/tensorflow/core/kernels/conv_ops_3d.cc#L143-L145) does a modulo operation based on user controlled input. Thus, when `filter` has a 0 as the fifth element, this results in a division by 0. Additionally, if the shape of the two tensors is not valid, an Eigen assertion can be triggered, resulting in a program crash. 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: MEDIUM (CVSS 3.1 vector: CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H)
Weakness: CWE-369
Affected vendors: google
Affected products: tensorflow
CVE-2021-29518 (published 2021-05-14)
TensorFlow is an end-to-end open source platform for machine learning. In eager mode (default in TF 2.0 and later), session operations are invalid. However, users could still call the raw ops associated with them and trigger a null pointer dereference. The implementation(https://github.com/tensorflow/tensorflow/blob/eebb96c2830d48597d055d247c0e9aebaea94cd5/tensorflow/core/kernels/session_ops.cc#L104) dereferences the session state pointer without checking if it is valid. Thus, in eager mode, `ctx->session_state()` is nullptr and the call of the member function is undefined behavior. 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: HIGH (CVSS 3.1 vector: CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H)
Weakness: CWE-476
Affected vendors: google
Affected products: tensorflow
CVE-2021-29519 (published 2021-05-14)
TensorFlow is an end-to-end open source platform for machine learning. The API of `tf.raw_ops.SparseCross` allows combinations which would result in a `CHECK`-failure and denial of service. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/3d782b7d47b1bf2ed32bd4a246d6d6cadc4c903d/tensorflow/core/kernels/sparse_cross_op.cc#L114-L116) is tricked to consider a tensor of type `tstring` which in fact contains integral elements. Fixing the type confusion by preventing mixing `DT_STRING` and `DT_INT64` types solves this issue. 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: MEDIUM (CVSS 3.1 vector: CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H)
Weakness: CWE-843
Affected vendors: google
Affected products: tensorflow
CVE-2021-29520 (published 2021-05-14)
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.
Severity: HIGH (CVSS 3.1 vector: CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H)
Weakness: CWE-120
Affected vendors: google
Affected products: tensorflow
CVE-2021-29521 (published 2021-05-14)
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: MEDIUM (CVSS 3.1 vector: CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H)
Weakness: CWE-131
Affected vendors: google
Affected products: tensorflow
CVE-2021-29522 (published 2021-05-14)
TensorFlow is an end-to-end open source platform for machine learning. The `tf.raw_ops.Conv3DBackprop*` operations fail to validate that the input tensors are not empty. In turn, this would result in a division by 0. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/a91bb59769f19146d5a0c20060244378e878f140/tensorflow/core/kernels/conv_grad_ops_3d.cc#L430-L450) does not check that the divisor used in computing the shard size is not zero. Thus, if attacker controls the input sizes, they can trigger a denial of service via a division by zero error. 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: MEDIUM (CVSS 3.1 vector: CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H)
Weakness: CWE-369
Affected vendors: google
Affected products: tensorflow
CVE-2021-29523 (published 2021-05-14)
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.AddManySparseToTensorsMap`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/6f9896890c4c703ae0a0845394086e2e1e523299/tensorflow/core/kernels/sparse_tensors_map_ops.cc#L257) takes the values specified in `sparse_shape` 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.
Severity: MEDIUM (CVSS 3.1 vector: CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H)
Weakness: CWE-190
Affected vendors: google
Affected products: tensorflow
CVE-2021-29524 (published 2021-05-14)
TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a division by 0 in `tf.raw_ops.Conv2DBackpropFilter`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/496c2630e51c1a478f095b084329acedb253db6b/tensorflow/core/kernels/conv_grad_shape_utils.cc#L130) does a modulus operation where the divisor is controlled by the caller. 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: MEDIUM (CVSS 3.1 vector: CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H)
Weakness: CWE-369
Affected vendors: google
Affected products: tensorflow
CVE-2021-29525 (published 2021-05-14)
TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a division by 0 in `tf.raw_ops.Conv2DBackpropInput`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/b40060c9f697b044e3107917c797ba052f4506ab/tensorflow/core/kernels/conv_grad_input_ops.h#L625-L655) does a division by a quantity that is controlled by the caller. 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: HIGH (CVSS 3.1 vector: CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H)
Weakness: CWE-369
Affected vendors: google
Affected products: tensorflow
CVE-2021-29526 (published 2021-05-14)
TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a division by 0 in `tf.raw_ops.Conv2D`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/988087bd83f144af14087fe4fecee2d250d93737/tensorflow/core/kernels/conv_ops.cc#L261-L263) does a division by a quantity that is controlled by the caller. 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: MEDIUM (CVSS 3.1 vector: CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H)
Weakness: CWE-369
Affected vendors: google
Affected products: tensorflow
CVE-2021-29527 (published 2021-05-14)
TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a division by 0 in `tf.raw_ops.QuantizedConv2D`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/00e9a4d67d76703fa1aee33dac582acf317e0e81/tensorflow/core/kernels/quantized_conv_ops.cc#L257-L259) does a division by a quantity that is controlled by the caller. 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: MEDIUM (CVSS 3.1 vector: CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H)
Weakness: CWE-369
Affected vendors: google
Affected products: tensorflow
CVE-2021-29528 (published 2021-05-14)
TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a division by 0 in `tf.raw_ops.QuantizedMul`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/55900e961ed4a23b438392024912154a2c2f5e85/tensorflow/core/kernels/quantized_mul_op.cc#L188-L198) does a division by a quantity that is controlled by the caller. 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: MEDIUM (CVSS 3.1 vector: CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H)
Weakness: CWE-369
Affected vendors: google
Affected products: tensorflow
CVE-2021-29529 (published 2021-05-14)
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: HIGH (CVSS 3.1 vector: CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H)
Weakness: CWE-131
Affected vendors: google
Affected products: tensorflow
CVE-2021-29530 (published 2021-05-14)
TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a null pointer dereference by providing an invalid `permutation` to `tf.raw_ops.SparseMatrixSparseCholesky`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/080f1d9e257589f78b3ffb75debf584168aa6062/tensorflow/core/kernels/sparse/sparse_cholesky_op.cc#L85-L86) fails to properly validate the input arguments. Although `ValidateInputs` is called and there are checks in the body of this function, the code proceeds to the next line in `ValidateInputs` since `OP_REQUIRES`(https://github.com/tensorflow/tensorflow/blob/080f1d9e257589f78b3ffb75debf584168aa6062/tensorflow/core/framework/op_requires.h#L41-L48) is a macro that only exits the current function. Thus, the first validation condition that fails in `ValidateInputs` will cause an early return from that function. However, the caller will continue execution from the next line. The fix is to either explicitly check `context->status()` or to convert `ValidateInputs` to return a `Status`. 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: HIGH (CVSS 3.1 vector: CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H)
Weakness: CWE-476
Affected vendors: google
Affected products: tensorflow
CVE-2021-29531 (published 2021-05-14)
TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a `CHECK` fail in PNG encoding by providing an empty input tensor as the pixel data. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/e312e0791ce486a80c9d23110841525c6f7c3289/tensorflow/core/kernels/image/encode_png_op.cc#L57-L60) only validates that the total number of pixels in the image does not overflow. Thus, an attacker can send an empty matrix for encoding. However, if the tensor is empty, then the associated buffer is `nullptr`. Hence, when calling `png::WriteImageToBuffer`(https://github.com/tensorflow/tensorflow/blob/e312e0791ce486a80c9d23110841525c6f7c3289/tensorflow/core/kernels/image/encode_png_op.cc#L79-L93), the first argument (i.e., `image.flat<T>().data()`) is `NULL`. This then triggers the `CHECK_NOTNULL` in the first line of `png::WriteImageToBuffer`(https://github.com/tensorflow/tensorflow/blob/e312e0791ce486a80c9d23110841525c6f7c3289/tensorflow/core/lib/png/png_io.cc#L345-L349). Since `image` is null, this results in `abort` being called after printing the stacktrace. Effectively, this allows an attacker to mount a denial of service attack. 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: MEDIUM (CVSS 3.1 vector: CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H)
Weakness: CWE-754
Affected vendors: google
Affected products: tensorflow
CVE-2021-29532 (published 2021-05-14)
TensorFlow is an end-to-end open source platform for machine learning. An attacker can force accesses outside the bounds of heap allocated arrays by passing in invalid tensor values to `tf.raw_ops.RaggedCross`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/efea03b38fb8d3b81762237dc85e579cc5fc6e87/tensorflow/core/kernels/ragged_cross_op.cc#L456-L487) lacks validation for the user supplied arguments. Each of the above branches call a helper function after accessing array elements via a `*_list[next_*]` pattern, followed by incrementing the `next_*` index. However, as there is no validation that the `next_*` values are in the valid range for the corresponding `*_list` arrays, this results in heap OOB reads. 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: HIGH (CVSS 3.1 vector: CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:N/A:H)
Weakness: CWE-125
Affected vendors: google
Affected products: tensorflow
CVE-2021-29533 (published 2021-05-14)
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.
Severity: MEDIUM (CVSS 3.1 vector: CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H)
Weakness: CWE-754
Affected vendors: google
Affected products: tensorflow
CVE-2021-29534 (published 2021-05-14)
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.
Severity: MEDIUM (CVSS 3.1 vector: CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H)
Weakness: CWE-754
Affected vendors: google
Affected products: tensorflow
CVE-2021-29535 (published 2021-05-14)
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: HIGH (CVSS 3.1 vector: CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H)
Weakness: CWE-131
Affected vendors: google
Affected products: tensorflow
CVE-2021-29536 (published 2021-05-14)
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: HIGH (CVSS 3.1 vector: CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H)
Weakness: CWE-131
Affected vendors: google
Affected products: tensorflow
CVE-2021-29537 (published 2021-05-14)
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: HIGH (CVSS 3.1 vector: CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H)
Weakness: CWE-131
Affected vendors: google
Affected products: tensorflow
CVE-2021-29538 (published 2021-05-14)
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.
Severity: MEDIUM (CVSS 3.1 vector: CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H)
Weakness: CWE-369
Affected vendors: google
Affected products: tensorflow
CVE-2021-29539 (published 2021-05-14)
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.
Severity: MEDIUM (CVSS 3.1 vector: CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H)
Weakness: CWE-681
Affected vendors: google
Affected products: tensorflow
CVE-2021-29540 (published 2021-05-14)
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.
Severity: HIGH (CVSS 3.1 vector: CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H)
Weakness: CWE-120
Affected vendors: google
Affected products: tensorflow
CVE-2021-29541 (published 2021-05-14)
TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a dereference of a null pointer in `tf.raw_ops.StringNGrams`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/1cdd4da14282210cc759e468d9781741ac7d01bf/tensorflow/core/kernels/string_ngrams_op.cc#L67-L74) does not fully validate the `data_splits` argument. This would result in `ngrams_data`(https://github.com/tensorflow/tensorflow/blob/1cdd4da14282210cc759e468d9781741ac7d01bf/tensorflow/core/kernels/string_ngrams_op.cc#L106-L110) to be a null pointer when the output would be computed to have 0 or negative size. Later writes to the output tensor would then cause a null pointer dereference. 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: MEDIUM (CVSS 3.1 vector: CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H)
Weakness: CWE-476
Affected vendors: google
Affected products: tensorflow
CVE-2021-29542 (published 2021-05-14)
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: MEDIUM (CVSS 3.1 vector: CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H)
Weakness: CWE-131
Affected vendors: google
Affected products: tensorflow
CVE-2021-29543 (published 2021-05-14)
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.
Severity: MEDIUM (CVSS 3.1 vector: CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H)
Weakness: CWE-617
Affected vendors: google
Affected products: tensorflow
CVE-2021-29544 (published 2021-05-14)
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 does not validate the rank of the `input_*` tensors. In turn, this results in the tensors being passes as they are to `QuantizeAndDequantizePerChannelGradientImpl`. 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.
Severity: MEDIUM (CVSS 3.1 vector: CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H)
Weakness: CWE-754
Affected vendors: google
Affected products: tensorflow