ecosystem stringclasses 14 values | vuln_id stringlengths 10 19 | summary stringlengths 4 267 ⌀ | details stringlengths 9 13.5k | aliases stringlengths 17 144 ⌀ | modified_date stringdate 2010-05-27 05:47:00 2022-05-10 08:46:52 | published_date stringdate 2005-12-31 05:00:00 2022-05-10 08:46:50 | severity stringclasses 5 values | score float64 0 10 ⌀ | cwe_id stringclasses 988 values | refs stringlengths 30 17.7k ⌀ | introduced stringlengths 75 4.26k ⌀ |
|---|---|---|---|---|---|---|---|---|---|---|---|
PyPI | GHSA-2qx8-589j-gcpx | Moderate severity vulnerability that affects Plone and plone.app.users | plone.app.users in Plone 4.0 and 4.1 allows remote authenticated users to modify the properties of arbitrary accounts via unspecified vectors, as exploited in the wild in June 2011. | {'CVE-2011-1950'} | 2022-03-03T05:14:03.044447Z | 2018-07-23T20:26:45Z | MODERATE | null | null | {'https://exchange.xforce.ibmcloud.com/vulnerabilities/67695', 'https://nvd.nist.gov/vuln/detail/CVE-2011-1950', 'http://secunia.com/advisories/44775', 'http://securityreason.com/securityalert/8269', 'http://www.securityfocus.com/archive/1/518155/100/0/threaded', 'http://plone.org/products/plone/security/advisories/CVE-2011-1950', 'http://osvdb.org/72729', 'https://github.com/advisories/GHSA-2qx8-589j-gcpx', 'http://www.securityfocus.com/bid/48005'} | null |
PyPI | PYSEC-2009-5 | null | schema.py in FormEncode for Python (python-formencode) 1.0 does not apply the chained_validators feature, which allows attackers to bypass intended access restrictions via unknown vectors. | {'CVE-2008-6547'} | 2021-07-16T01:31:20.304490Z | 2009-03-30T01:30:00Z | null | null | null | {'https://www.redhat.com/archives/fedora-package-announce/2008-July/msg00607.html', 'http://secunia.com/advisories/31081', 'https://exchange.xforce.ibmcloud.com/vulnerabilities/43878', 'http://secunia.com/advisories/31163', 'http://sourceforge.net/tracker/index.php?func=detail&aid=1925164&group_id=91231&atid=596416', 'http://osvdb.org/47082', 'http://sourceforge.net/tracker/download.php?group_id=91231&atid=596416&file_id=271779&aid=1925164', 'http://www.securityfocus.com/bid/30282'} | null |
PyPI | PYSEC-2019-152 | null | A vulnerability was found in openstack-ironic-inspector all versions excluding 5.0.2, 6.0.3, 7.2.4, 8.0.3 and 8.2.1. A SQL-injection vulnerability was found in openstack-ironic-inspector's node_cache.find_node(). This function makes a SQL query using unfiltered data from a server reporting inspection results (by a POST to the /v1/continue endpoint). Because the API is unauthenticated, the flaw could be exploited by an attacker with access to the network on which ironic-inspector is listening. Because of how ironic-inspector uses the query results, it is unlikely that data could be obtained. However, the attacker could pass malicious data and create a denial of service. | {'CVE-2019-10141'} | 2021-07-05T00:01:21.998814Z | 2019-07-30T17:15:00Z | null | null | null | {'https://access.redhat.com/errata/RHSA-2019:2505', 'https://docs.openstack.org/releasenotes/ironic-inspector/pike.html#relnotes-6-0-3-4-stable-pike', 'https://docs.openstack.org/releasenotes/ironic-inspector/rocky.html#relnotes-8-0-3-stable-rocky', 'https://docs.openstack.org/releasenotes/ironic-inspector/queens.html#relnotes-7-2-4-stable-queens', 'https://docs.openstack.org/releasenotes/ironic-inspector/ocata.html#relnotes-5-0-2-7-origin-stable-ocata', 'https://bugzilla.redhat.com/show_bug.cgi?id=CVE-2019-10141', 'https://docs.openstack.org/releasenotes/ironic-inspector/stein.html#relnotes-8-2-1-stable-stein'} | null |
PyPI | PYSEC-2022-32 | null | B2 Command Line Tool is the official command line tool for the backblaze cloud storage service. Linux and Mac releases of the B2 command-line tool version 3.2.0 and below contain a key disclosure vulnerability that, in certain conditions, can be exploited by local attackers through a time-of-check-time-of-use (TOCTOU) race condition. The command line tool saves API keys (and bucket name-to-id mapping) in a local database file (`$XDG_CONFIG_HOME/b2/account_info`, `~/.b2_account_info` or a user-defined path) when `b2 authorize-account` is first run. This happens regardless of whether a valid key is provided or not. When first created, the file is world readable and is (typically a few milliseconds) later altered to be private to the user. If the directory is readable by a local attacker and the user did not yet run `b2 authorize-account` then during the brief period between file creation and permission modification, a local attacker can race to open the file and maintain a handle to it. This allows the local attacker to read the contents after the file after the sensitive information has been saved to it. Users that have not yet run `b2 authorize-account` should upgrade to B2 Command-Line Tool v3.2.1 before running it. Users that have run `b2 authorize-account` are safe if at the time of the file creation no other local users had read access to the local configuration file. Users that have run `b2 authorize-account` where the designated path could be opened by another local user should upgrade to B2 Command-Line Tool v3.2.1 and remove the database and regenerate all application keys. Note that `b2 clear-account` does not remove the database file and it should not be used to ensure that all open handles to the file are invalidated. If B2 Command-Line Tool cannot be upgraded to v3.2.1 due to a dependency conflict, a binary release can be used instead. Alternatively a new version could be installed within a virtualenv, or the permissions can be changed to prevent local users from opening the database file. | {'CVE-2022-23653', 'GHSA-8wr4-2wm6-w3pr'} | 2022-03-07T17:33:45.997096Z | 2022-02-23T23:15:00Z | null | null | null | {'https://github.com/Backblaze/B2_Command_Line_Tool/commit/c74029f9f75065e8f7e3c3ec8e0a23fb8204feeb', 'https://github.com/Backblaze/B2_Command_Line_Tool/security/advisories/GHSA-8wr4-2wm6-w3pr'} | null |
PyPI | PYSEC-2018-89 | null | mpatch.c in Mercurial before 4.6.1 mishandles integer addition and subtraction, aka OVE-20180430-0002. | {'CVE-2018-13347'} | 2021-08-27T03:22:07.281860Z | 2018-07-06T00:29:00Z | null | null | null | {'https://www.mercurial-scm.org/repo/hg-committed/log?rev=modifies%28%22mercurial%2Fmpatch.c%22%29+and+4.5%3A%3A', 'https://www.mercurial-scm.org/wiki/WhatsNew#Mercurial_4.6.1_.282018-06-06.29', 'https://www.mercurial-scm.org/repo/hg/rev/1acfc35d478c', 'https://access.redhat.com/errata/RHSA-2019:2276', 'https://lists.debian.org/debian-lts-announce/2020/07/msg00032.html'} | null |
PyPI | GHSA-8jj7-5vxc-pg2q | Integer overflow in TensorFlow | ### Impact
Under certain scenarios, Grappler component of TensorFlow is vulnerable to an integer overflow during [cost estimation for crop and resize](https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/grappler/costs/op_level_cost_estimator.cc#L2621-L2689). Since the cropping parameters are user controlled, a malicious person can trigger undefined behavior.
### Patches
We have patched the issue in GitHub commit [0aaaae6eca5a7175a193696383f582f53adab23f](https://github.com/tensorflow/tensorflow/commit/0aaaae6eca5a7175a193696383f582f53adab23f).
The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions. | {'CVE-2022-23587'} | 2022-03-03T05:13:04.949665Z | 2022-02-09T23:27:49Z | HIGH | null | {'CWE-190'} | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-8jj7-5vxc-pg2q', 'https://github.com/tensorflow/tensorflow/commit/0aaaae6eca5a7175a193696383f582f53adab23f', 'https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/grappler/costs/op_level_cost_estimator.cc#L2621-L2689', 'https://nvd.nist.gov/vuln/detail/CVE-2022-23587', 'https://github.com/tensorflow/tensorflow/'} | null |
PyPI | PYSEC-2013-16 | null | The administrative interface for Django 1.3.x before 1.3.6, 1.4.x before 1.4.4, and 1.5 before release candidate 2 does not check permissions for the history view, which allows remote authenticated administrators to obtain sensitive object history information. | {'CVE-2013-0305'} | 2021-07-15T02:22:08.650123Z | 2013-05-02T14:55:00Z | null | null | null | {'https://www.djangoproject.com/weblog/2013/feb/19/security/', 'http://ubuntu.com/usn/usn-1757-1', 'http://rhn.redhat.com/errata/RHSA-2013-0670.html', 'http://www.debian.org/security/2013/dsa-2634'} | null |
PyPI | PYSEC-2021-728 | null | TensorFlow is an end-to-end open source platform for machine learning. The implementation of the `DepthwiseConv` TFLite operator is vulnerable to a division by zero error(https://github.com/tensorflow/tensorflow/blob/1a8e885b864c818198a5b2c0cbbeca5a1e833bc8/tensorflow/lite/kernels/depthwise_conv.cc#L287-L288). An attacker can craft a model such that `input`'s fourth dimension would be 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. | {'GHSA-rf3h-xgv5-2q39', 'CVE-2021-29602'} | 2021-12-09T06:35:32.373015Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/cbda3c6b2dbbd3fbdc482ff8c0170a78ec2e97d0', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-rf3h-xgv5-2q39'} | null |
PyPI | PYSEC-2021-378 | null | Apache Superset up to and including 1.3.0 when configured with ENABLE_TEMPLATE_PROCESSING on (disabled by default) allowed SQL injection when a malicious authenticated user sends an http request with a custom URL. | {'CVE-2021-41971'} | 2021-10-24T23:24:36.552552Z | 2021-10-18T15:15:00Z | null | null | null | {'https://lists.apache.org/thread.html/rf7292731268c6c6e2196ae1583e32ac7189385364268f8d9215e8e6d%40%3Cdev.superset.apache.org%3E'} | null |
PyPI | GHSA-wqgp-vphw-hphf | Security issues in AWS KMS and AWS Encryption SDKs: in-band protocol negotiation and robustness | Authors: Thai "[thaidn](https://twitter.com/xorninja)" Duong
# Summary
The following security vulnerabilities was discovered and reported to Amazon, affecting AWS KMS and all versions of [AWS Encryption SDKs](https://docs.aws.amazon.com/encryption-sdk/latest/developer-guide/introduction.html) prior to version 2.0.0:
* **Information leakage**: an attacker can create ciphertexts that would leak the user’s AWS account ID, encryption context, user agent, and IP address upon decryption
* **Ciphertext forgery**: an attacker can create ciphertexts that are accepted by other users
* **Robustness**: an attacker can create ciphertexts that decrypt to different plaintexts for different users
The first two bugs are somewhat surprising because they show that the ciphertext format can lead to vulnerabilities. These bugs (and the infamous [alg: "None"](https://auth0.com/blog/critical-vulnerabilities-in-json-web-token-libraries/) bugs in JWT) belong to a class of vulnerabilities called **in-band protocol negotiation**. This is the second time we’ve found in-band protocol negotiation vulnerabilities in AWS cryptography libraries; see this [bug](https://github.com/google/security-research/security/advisories/GHSA-7f33-f4f5-xwgw) in S3 Crypto SDK discovered by my colleague Sophie Schmieg.
In JWT and S3 SDK the culprit is the algorithm field—here it is the key ID. Because the key ID is used to determine which decryption key to use, it can’t be meaningfully authenticated despite being under the attacker’s control. If the key ID is a URL indicating where to fetch the key, the attacker can replace it with their own URL, and learn side-channel information such as the timing and machines on which the decryption happens (this can also lead to [SSRF](https://portswigger.net/web-security/ssrf) issues, but that’s another topic for another day).
In AWS, the key ID is a unique [Amazon Resource Name](https://docs.aws.amazon.com/general/latest/gr/aws-arns-and-namespaces.html). If an attacker were to capture a ciphertext from a user and replace its key ID with their own, the victim’s AWS account ID, encryption context, user agent, and IP address would be logged to the attacker’s AWS account whenever the victim attempted to decrypt the modified ciphertext.
The last bug shows that the non-committing property of AES-GCM (and other AEAD ciphers such as [AES-GCM-SIV](https://keymaterial.net/2020/09/07/invisible-salamanders-in-aes-gcm-siv/) or (X)ChaCha20Poly1305) is especially problematic in multi-recipient settings. These ciphers have a property that can cause nonidentical plaintexts when decrypting a single ciphertext with two different keys! For example, you can send a single encrypted email to Alice and Bob which, upon decryption, reads “attack” to Alice and “retreat” to Bob. The AWS Encryption SDKs are vulnerable to this attack because they allow a single ciphertext to be generated for multiple recipients, with each decrypting using a different key. I believe this kind of problem is prevalent. I briefly looked at [JWE](https://tools.ietf.org/html/rfc7516) and I think it is vulnerable.
# Mitigations
Amazon has fixed these bugs in release 2.0.0 of the SDKs. A new major version was required because, unfortunately, the fix for the last bug requires a breaking change from earlier versions. All users are recommended to upgrade. More details about Amazon’s mitigations can be found in [their announcement](https://aws.amazon.com/blogs/security/improved-client-side-encryption-explicit-keyids-and-key-commitment/).
We’re collaborating with Shay Gueron on a paper regarding fast committing AEADs.
# Vulnerabilities
## Information Leakage
The [Encrypt](https://docs.aws.amazon.com/kms/latest/APIReference/API_Encrypt.html) API in AWS KMS encrypts plaintext into ciphertext by using a customer master key (CMK). The ciphertext format is undocumented, but it contains metadata that specifies the CMK and the encryption algorithm. I reverse-engineered the format and found the location of the CMK. Externally the CMK is identified by its key ARN, but within a ciphertext it is represented by an internal ID, which remained stable during my testing.
When I replaced the internal ID of a CMK in a ciphertext with the internal ID of another CMK, I found that AWS KMS attempted to decrypt the ciphertext with the new CMK. The encryption failed and the failure event—including the AWS Account ID, the user agent and the IP address of the caller—was logged to Cloud Trail in the account that owned the replacement CMK.
This enables the following attack:
* The attacker creates a CMK that has a key policy that allows access from everyone. This requires no prior knowledge about the victim.
* The attacker intercepts a ciphertext from the victim, and replaces its CMK with their CMK.
* Whenever the victim attempts to decrypt the modified ciphertext, the attacker learns the timing of such actions, the victim’s AWS Account ID, user agent, encryption context, and IP address.
This attack requires the victim to have an IAM policy that allows them to access the attacker’s CMK. I found that this practice was allowed by the AWS Visual Policy Editor, but I don’t know whether it is common.
The AWS Encryption SDKs also succumb to this attack. The SDKs implement envelope encryption: encrypting data with a data encryption key (DEK) and then wrapping the DEK with a CMK using the Encrypt API in AWS KMS. The wrapped DEK is stored as part of the final ciphertext (format is defined [here](https://docs.aws.amazon.com/encryption-sdk/latest/developer-guide/message-format.html)). The attacker can mount this attack by replacing the CMK in the wrapped DEK with their own.
```
{
"eventVersion": "1.05",
"userIdentity": {
"type": "AWSAccount",
"principalId": "<redacted this is the principal ID of the victim>",
"accountId": "<redacted - this is the AWS account ID of the victim>"
},
"eventTime": "2020-06-21T21:05:04Z",
"eventSource": "kms.amazonaws.com",
"eventName": "Decrypt",
"awsRegion": "us-west-2",
"sourceIPAddress": "<redacted - this is the IP address of the victim>",
"userAgent": "<redacted - this is the user agent of the victim>",
"errorCode": "InvalidCiphertextException",
"requestParameters": {
// The encryption context might include other data from the victim
"encryptionContext": {
"aws-crypto-public-key": "AzfNOGOnNYFmpHspKrAm1L6XtRybONkmkhmB/IriKSA7b2NsV4MEPMph9yX2KTPKWw=="
},
"encryptionAlgorithm": "SYMMETRIC_DEFAULT"
},
"responseElements": null,
"requestID": "aeced8e8-75a2-42c3-96ac-d1fa2a1c5ee6",
"eventID": "780a0a6e-4ad8-43d4-a426-75d05022f870",
"readOnly": true,
"resources": [
{
"accountId": "<redacted - this is the account ID of the attacker>",
"type": "AWS::KMS::Key",
"ARN": <redacted - this is the key ARN of the attacker>
}
],
"eventType": "AwsApiCall",
"recipientAccountId": "<redacted - this is the account ID of the attacker>",
"sharedEventID": "033e147c-8a36-42f5-9d6c-9e071eb752b7"
}
```
**Figure 1: A failure event logged to the attacker’s Cloud Trail when the victim attempted to decrypt a modified ciphertext containing the attacker’s CMK.**
## Ciphertext Forgery
The [Decrypt](https://docs.aws.amazon.com/kms/latest/APIReference/API_Decrypt.html) API in AWS KMS doesn’t require the caller to specify the CMK. This parameter is required only when the ciphertext was encrypted under an asymmetric CMK. Otherwise, AWS KMS uses the metadata that it adds to the ciphertext blob to determine which CMK was used to encrypt the ciphertext.
This leads to the following attack:
* The attacker creates a CMK that has a key policy that allows access from everyone. This requires no prior knowledge about the victim.
* The attacker generates a ciphertext by calling the Encrypt API with their key.
* The attacker intercepts a ciphertext from the victim, and replaces it entirely with their ciphertext.
* The victim successfully decrypts the ciphertext, as if it was encrypted under their own key. The attacker also learns when this happened, the victim’s AWS Account ID, user agent, encryption context, and IP address.
Similar to the information leakage attack, this attack also requires the victim to have an IAM policy that allows them to access the attacker’s CMK.
The AWS Encryption SDKs also succumb to this attack. They don’t specify the CMK when they call the Decrypt API to unwrap the DEK.
## Robustness
The AWS Encryption SDKs allow a single ciphertext to be generated for multiple recipients, with each decrypting using a different key. To that end, it wraps the DEK multiple times, each under a different CMK. The wrapped DEKs can be combined to form a single ciphertext which can be sent to multiple recipients who can use their own credentials to decrypt it. It’s reasonable to expect that all recipients should decrypt the ciphertext to an identical plaintext. However, because of the use of AES-GMAC and AES-GCM, it’s possible to create a ciphertext that decrypts to two valid yet different plaintexts for two different users. In other words, the AWS Encryption SDKs are [not](https://eprint.iacr.org/2008/440.pdf) [robust](https://eprint.iacr.org/2019/016.pdf).
The encryption of a message under two CMKs can be summarized as follows:
* A DEK is randomly generated, and two wrapped DEKs are produced by calling the Encrypt API using the two CMKs
* A per-message AES-GCM key (K) is derived using HKDF from the DEK, a randomly generated message ID, and a fixed algorithm ID.
* A header is formed from the wrapped DEKs, the encryption context, and other metadata. A header authentication tag is computed on the header using AES-GMAC with K and a zero IV.
* The message is encrypted using AES-GCM with K, a non-zero IV, and fixed associated additional data. This produces a message authentication tag.
* The ciphertext consists of the header, the header authentication tag, the encrypted message, and the message authentication tag.
(There’s also a self-signed digital signature that is irrelevant to this discussion).
In order to decrypt a ciphertext, the AWS Encryption SDKs loops over the list of wrapped DEKs and returns the first one that it can successfully unwrap. The attacker therefore can wrap a unique DEK for each recipient. Next, the attacker exploits the non-committing property of GMAC to produce two messages that have the same GMAC tag under two different keys. The attacker has to do this twice, one for the header authentication tag and one for the message authentication tag.
```
Given a data blob B of one 128-bit block B_1, a GMAC tag is computed as follows:
B_1 * H^2 + B_len * H + J
where H and J depends on the key and B_len depends on the length of B.
To find a message that can produce the same tag under two different keys, one
can add append to B a new block B_2 whose value can be deduced by solving
an algebraic equation. That is, we want to find B_2 such that:
B_1 * H^3 + B_2 * H^2 + B_len * H + J = B_1 * H’^3 + B_2 * H’^2 + B_len * H’ + J’
where H’ and J’ are the corresponding H and J of the other key.
B_2 is the only unknown value in this equation, thus it can be computed using
finite field arithmetics of GF(2^128):
B_2 = [B_1 * (H^3+H’^3) + B_len * (H + H’) + J + J’] * (H^2 + H’^2)^-1.
```
**Figure 2: How to find a message that has the same GMAC tag under two different keys.**
The overall attack works as follows:
* The attacker generates a random DEK, derives a per-message key K, and encrypts message M with it using AES in counter mode. This generates a ciphertext C.
* The attacker generates another random DEK’, derives a per-message key K’, and performs trial decryption of C until the decrypted message M’ has desirable properties. For example, if the attacker wants the first bit of M’ different from that of M, this process should only take a few attempts.
* The attacker finds a block C* such that the GMAC of C’ = C || C* under K and K’ are identical. Denote this tag C’_tag.
* The attacker wraps DEK and DEK’ under two recipients’ CMK.
* The attacker forms a header H and adds a block H* to the encryption context such that the new H’ has the same authentication tag H’_tag under K and K’.
* The attacker output H’, H’_tag, C’, C’_tag.
This attack is similar to the one discovered in [Facebook Messenger](https://eprint.iacr.org/2019/016.pdf).
# Acknowledgement
I’m grateful to Jen Barnason for carefully editing this advisory. I will never publish anything without her approval! I want to thank my friend and coworker Sophie “Queen of Hashing” Schmieg for wonderful discussions and for showing me how the arithmetic in GF(2^128) works. I want to thank Jonathan Bannet for asking the questions that led to this work. | {'CVE-2020-8897'} | 2022-03-03T05:14:01.809111Z | 2021-10-12T16:01:12Z | HIGH | null | {'CWE-327'} | {'https://nvd.nist.gov/vuln/detail/CVE-2020-8897', 'https://github.com/google/security-research/security/advisories/GHSA-wqgp-vphw-hphf', 'https://aws.amazon.com/blogs/security/improved-client-side-encryption-explicit-keyids-and-key-commitment/'} | null |
PyPI | GHSA-wm93-f238-7v37 | Integer overflow in Tensorflow | ### Impact
The [implementation of `OpLevelCostEstimator::CalculateOutputSize`](https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/grappler/costs/op_level_cost_estimator.cc#L1598-L1617) is vulnerable to an integer overflow if an attacker can create an operation which would involve tensors with large enough number of elements:
```cc
for (const auto& dim : output_shape.dim()) {
output_size *= dim.size();
}
```
Here, we can have a large enough number of dimensions in `output_shape.dim()` or just a small number of dimensions being large enough to cause an overflow in the multiplication.
### Patches
We have patched the issue in GitHub commit [b9bd6cfd1c50e6807846af9a86f9b83cafc9c8ae](https://github.com/tensorflow/tensorflow/commit/b9bd6cfd1c50e6807846af9a86f9b83cafc9c8ae).
The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
| {'CVE-2022-23576'} | 2022-03-03T05:13:44.914237Z | 2022-02-10T00:32:44Z | MODERATE | null | {'CWE-190'} | {'https://github.com/tensorflow/tensorflow/commit/b9bd6cfd1c50e6807846af9a86f9b83cafc9c8ae', 'https://nvd.nist.gov/vuln/detail/CVE-2022-23576', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-wm93-f238-7v37', 'https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/grappler/costs/op_level_cost_estimator.cc#L1598-L1617', 'https://github.com/tensorflow/tensorflow/'} | null |
PyPI | PYSEC-2021-682 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a denial of service via a FPE runtime error in `tf.raw_ops.Reverse`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/36229ea9e9451dac14a8b1f4711c435a1d84a594/tensorflow/core/kernels/reverse_op.cc#L75-L76) performs a division based on the first dimension of the tensor argument. 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. | {'GHSA-fxqh-cfjm-fp93', 'CVE-2021-29556'} | 2021-12-09T06:35:24.446471Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fxqh-cfjm-fp93', 'https://github.com/tensorflow/tensorflow/commit/4071d8e2f6c45c1955a811fee757ca2adbe462c1'} | null |
PyPI | PYSEC-2017-55 | null | Plone 4.0 through 5.1a1 does not have security declarations for Dexterity content-related WebDAV requests, which allows remote attackers to gain webdav access via unspecified vectors. | {'CVE-2016-4041'} | 2021-07-25T23:34:48.563067Z | 2017-02-24T20:59:00Z | null | null | null | {'http://www.openwall.com/lists/oss-security/2016/04/20/1', 'https://plone.org/security/hotfix/20160419/privilege-escalation-in-webdav'} | null |
PyPI | GHSA-g7p5-5759-qv46 | Data leak in Tensorflow | ### Impact
The `data_splits` argument of [`tf.raw_ops.StringNGrams`](https://www.tensorflow.org/api_docs/python/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
```python
>>> tf.raw_ops.StringNGrams(data=["aa", "bb", "cc", "dd", "ee", "ff"], data_splits=[0,8], separator=" ", ngram_widths=[3], left_pad="", right_pad="", pad_width=0, preserve_short_sequences=False)
StringNGrams(ngrams=<tf.Tensor: shape=(6,), dtype=string, numpy=
array([b'aa bb cc', b'bb cc dd', b'cc dd ee', b'dd ee ff',
b'ee ff \xf4j\xa7q\x7f\x00\x00q\x00\x00\x00\x00\x00\x00\x00\xd8\x9b~\xa8q\x7f\x00',
b'ff \xf4j\xa7q\x7f\x00\x00q\x00\x00\x00\x00\x00\x00\x00\xd8\x9b~\xa8q\x7f\x00 \x9b~\xa8q\x7f\x00\x00p\xf5j\xa7q\x7f\x00\x00H\xf8j\xa7q\x7f\x00\x00\xf0\xf3\xf7\x85q\x7f\x00\x00`}\xa6\x00\x00\x00\x00\x00`~\xa6\x00\x00\x00\x00\x00\xb0~\xeb\x9bq\x7f\x00'],...
```
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.
### Patches
We have patched the issue in 0462de5b544ed4731aa2fb23946ac22c01856b80 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.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by members of the Aivul Team from Qihoo 360. | {'CVE-2020-15205'} | 2022-03-03T05:14:10.912571Z | 2020-09-25T18:28:38Z | CRITICAL | null | {'CWE-787', 'CWE-119', 'CWE-122'} | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-g7p5-5759-qv46', 'https://nvd.nist.gov/vuln/detail/CVE-2020-15205', 'https://github.com/tensorflow/tensorflow', 'https://github.com/tensorflow/tensorflow/commit/0462de5b544ed4731aa2fb23946ac22c01856b80', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html'} | null |
PyPI | PYSEC-2021-397 | null | TensorFlow is an open source platform for machine learning. In affected versions during TensorFlow's Grappler optimizer phase, constant folding might attempt to deep copy a resource tensor. This results in a segfault, as these tensors are supposed to not change. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range. | {'GHSA-786j-5qwq-r36x', 'CVE-2021-41204'} | 2021-11-13T06:52:42.949977Z | 2021-11-05T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-786j-5qwq-r36x', 'https://github.com/tensorflow/tensorflow/commit/7731e8dfbe4a56773be5dc94d631611211156659'} | null |
PyPI | PYSEC-2021-22 | null | Sydent is a reference Matrix identity server. Sydent can be induced to send HTTP GET requests to internal systems, due to lack of parameter validation or IP address blacklisting. It is not possible to exfiltrate data or control request headers, but it might be possible to use the attack to perform an internal port enumeration. This issue has been addressed in in 9e57334, 8936925, 3d531ed, 0f00412. A potential workaround would be to use a firewall to ensure that Sydent cannot reach internal HTTP resources. | {'CVE-2021-29431', 'GHSA-9jhm-8m8c-c3f4'} | 2021-04-22T15:27:00Z | 2021-04-15T21:15:00Z | null | null | null | {'https://github.com/matrix-org/sydent/commit/9e573348d81df8191bbe8c266c01999c9d57cd5f', 'https://github.com/matrix-org/sydent/commit/0f00412017f25619bc36c264b29ea96808bf310a', 'https://github.com/matrix-org/sydent/releases/tag/v2.3.0', 'https://github.com/matrix-org/sydent/commit/8936925f561b0c352c2fa922d5097d7245aad00a', 'https://pypi.org/project/matrix-sydent/', 'https://github.com/matrix-org/sydent/commit/3d531ed50d2fd41ac387f36d44d3fb2c62dd22d3', 'https://github.com/matrix-org/sydent/security/advisories/GHSA-9jhm-8m8c-c3f4'} | null |
PyPI | GHSA-34f9-hjfq-rr8j | Overflow and uncaught divide by zero in Tensorflow | ### Impact
The [implementation of `UnravelIndex`](https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/unravel_index_op.cc#L36-L135) is vulnerable to a division by zero caused by an integer overflow bug:
```python
import tensorflow as tf
tf.raw_ops.UnravelIndex(indices=-0x100000,dims=[0x100000,0x100000])
```
### Patches
We have patched the issue in GitHub commit [58b34c6c8250983948b5a781b426f6aa01fd47af](https://github.com/tensorflow/tensorflow/commit/58b34c6c8250983948b5a781b426f6aa01fd47af).
The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by Yu Tian of Qihoo 360 AIVul Team. | {'CVE-2022-21729'} | 2022-03-03T05:13:12.330440Z | 2022-02-10T00:18:53Z | MODERATE | null | {'CWE-190'} | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-34f9-hjfq-rr8j', 'https://github.com/tensorflow/tensorflow', 'https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/unravel_index_op.cc#L36-L135', 'https://github.com/tensorflow/tensorflow/commit/58b34c6c8250983948b5a781b426f6aa01fd47af', 'https://nvd.nist.gov/vuln/detail/CVE-2022-21729'} | null |
PyPI | PYSEC-2022-166 | null | In Paramiko before 2.10.1, a race condition (between creation and chmod) in the write_private_key_file function could allow unauthorized information disclosure. | {'GHSA-f8q4-jwww-x3wv', 'CVE-2022-24302'} | 2022-04-25T17:02:20.739998Z | 2022-03-17T22:15:00Z | null | null | null | {'https://github.com/paramiko/paramiko/blob/363a28d94cada17f012c1604a3c99c71a2bda003/paramiko/pkey.py#L546', 'https://www.paramiko.org/changelog.html', 'https://github.com/advisories/GHSA-f8q4-jwww-x3wv'} | null |
PyPI | GHSA-w578-j992-554x | Ansible fails to properly mark lookup-plugin results as unsafe | Ansible before versions 2.3.1.0 and 2.4.0.0 fails to properly mark lookup-plugin results as unsafe. If an attacker could control the results of lookup() calls, they could inject Unicode strings to be parsed by the jinja2 templating system, resulting in code execution. By default, the jinja2 templating language is now marked as 'unsafe' and is not evaluated. | {'CVE-2017-7481'} | 2022-04-26T18:47:54.780386Z | 2018-09-06T03:28:50Z | CRITICAL | null | {'CWE-20'} | {'https://github.com/advisories/GHSA-w578-j992-554x', 'https://github.com/ansible/ansible', 'https://nvd.nist.gov/vuln/detail/CVE-2017-7481'} | null |
PyPI | PYSEC-2021-180 | null | 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. | {'GHSA-fphq-gw9m-ghrv', 'CVE-2021-29543'} | 2021-08-27T03:22:29.100995Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fphq-gw9m-ghrv', 'https://github.com/tensorflow/tensorflow/commit/ea3b43e98c32c97b35d52b4c66f9107452ca8fb2'} | null |
PyPI | PYSEC-2019-129 | null | In words.protocols.jabber.xmlstream in Twisted through 19.2.1, XMPP support did not verify certificates when used with TLS, allowing an attacker to MITM connections. | {'CVE-2019-12855', 'GHSA-65rm-h285-5cc5'} | 2019-08-14T03:15:00Z | 2019-06-16T12:29:00Z | null | null | null | {'https://usn.ubuntu.com/4308-1/', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/PLTZDMFBNFSJMBXYJNGJHENJA4H2TSMZ/', 'http://lists.opensuse.org/opensuse-security-announce/2019-09/msg00028.html', 'https://www.oracle.com/security-alerts/cpuapr2020.html', 'https://usn.ubuntu.com/4308-2/', 'https://github.com/advisories/GHSA-65rm-h285-5cc5', 'https://twistedmatrix.com/trac/ticket/9561', 'https://github.com/twisted/twisted/pull/1147', 'http://lists.opensuse.org/opensuse-security-announce/2019-09/msg00013.html'} | null |
PyPI | GHSA-9x6q-5423-w5v9 | Ansible fails to cache SSH host keys | Ansible before 1.2.1 makes it easier for remote attackers to conduct man-in-the-middle attacks by leveraging failure to cache SSH host keys. | {'CVE-2013-2233'} | 2022-04-26T18:48:02.024562Z | 2018-10-10T17:23:39Z | HIGH | null | null | {'https://www.ansible.com/security', 'http://www.openwall.com/lists/oss-security/2013/07/02/6', 'https://github.com/advisories/GHSA-9x6q-5423-w5v9', 'https://nvd.nist.gov/vuln/detail/CVE-2013-2233', 'https://github.com/ansible/ansible/issues/857', 'https://github.com/ansible/ansible', 'https://github.com/pypa/advisory-database/tree/main/vulns/ansible/PYSEC-2018-36.yaml', 'https://bugzilla.redhat.com/show_bug.cgi?id=980821', 'http://www.openwall.com/lists/oss-security/2013/07/01/2'} | null |
PyPI | PYSEC-2020-74 | null | Multiple cross-site scripting (XSS) vulnerabilities in Papermerge before 1.5.2 allow remote attackers to inject arbitrary web script or HTML via the rename, tag, upload, or create folder function. The payload can be in a folder, a tag, or a document's filename. If email consumption is configured in Papermerge, a malicious document can be sent by email and is automatically uploaded into the Papermerge web application. Therefore, no authentication is required to exploit XSS if email consumption is configured. Otherwise authentication is required. | {'GHSA-9w49-m7xh-5r39', 'CVE-2020-29456'} | 2020-12-02T16:29:00Z | 2020-12-02T08:15:00Z | null | null | null | {'https://www.papermerge.com/', 'https://github.com/ciur/papermerge/issues/228', 'https://github.com/advisories/GHSA-9w49-m7xh-5r39', 'https://github.com/ciur/papermerge/releases/tag/v1.5.2'} | null |
PyPI | PYSEC-2022-49 | null | Tensorflow is an Open Source Machine Learning Framework. The estimator for the cost of some convolution operations can be made to execute a division by 0. The function fails to check that the stride argument is strictly positive. Hence, the fix is to add a check for the stride argument to ensure it is valid. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range. | {'GHSA-v3f7-j968-4h5f', 'CVE-2022-21725'} | 2022-03-09T00:17:29.922594Z | 2022-02-03T13:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/3218043d6d3a019756607643cf65574fbfef5d7a', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-v3f7-j968-4h5f', 'https://github.com/tensorflow/tensorflow/blob/ffa202a17ab7a4a10182b746d230ea66f021fe16/tensorflow/core/grappler/costs/op_level_cost_estimator.cc#L189-L198'} | null |
PyPI | GHSA-xcjx-m2pj-8g79 | Manipulated inline images can cause Infinite Loop in PyPDF2 | ### Impact
An attacker who uses this vulnerability can craft a PDF which leads to an infinite loop if the PyPDF2 user wrote the following code:
```python
from PyPDF2 import PdfFileReader, PdfFileWriter
from PyPDF2.pdf import ContentStream
reader = PdfFileReader("malicious.pdf", strict=False)
for page in reader.pages:
ContentStream(page.getContents(), reader)
```
### Patches
[`PyPDF2==1.27.5`](https://pypi.org/project/PyPDF2) and later are patched.
Credits to [Sebastian Krause](https://github.com/sekrause) for finding ([issue](https://github.com/py-pdf/PyPDF2/issues/329)) and fixing ([PR](https://github.com/py-pdf/PyPDF2/pull/740)) it.
| {'CVE-2022-24859'} | 2022-04-22T21:00:27.629806Z | 2022-04-22T20:54:41Z | MODERATE | null | {'CWE-835'} | {'https://github.com/py-pdf/PyPDF2/security/advisories/GHSA-xcjx-m2pj-8g79', 'https://github.com/py-pdf/PyPDF2', 'https://github.com/py-pdf/PyPDF2/issues/329', 'https://github.com/py-pdf/PyPDF2/pull/740', 'https://nvd.nist.gov/vuln/detail/CVE-2022-24859', 'https://github.com/py-pdf/PyPDF2/releases/tag/1.27.5'} | null |
PyPI | PYSEC-2021-495 | null | TensorFlow is an end-to-end open source platform for machine learning. Due to lack of validation in `tf.raw_ops.SparseDenseCwiseMul`, an attacker can trigger denial of service via `CHECK`-fails or accesses to outside the bounds of heap allocated data. Since the implementation(https://github.com/tensorflow/tensorflow/blob/38178a2f7a681a7835bb0912702a134bfe3b4d84/tensorflow/core/kernels/sparse_dense_binary_op_shared.cc#L68-L80) only validates the rank of the input arguments but no constraints between dimensions(https://www.tensorflow.org/api_docs/python/tf/raw_ops/SparseDenseCwiseMul), an attacker can abuse them to trigger internal `CHECK` assertions (and cause program termination, denial of service) or to write to memory outside of bounds of heap allocated tensor buffers. 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-2021-29567', 'GHSA-wp3c-xw9g-gpcg'} | 2021-12-09T06:34:53.752999Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/7ae2af34087fb4b5c8915279efd03da3b81028bc', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-wp3c-xw9g-gpcg'} | null |
PyPI | PYSEC-2021-5 | null | CairoSVG is a Python (pypi) package. CairoSVG is an SVG converter based on Cairo. In CairoSVG before version 2.5.1, there is a regular expression denial of service (REDoS) vulnerability. When processing SVG files, the python package CairoSVG uses two regular expressions which are vulnerable to Regular Expression Denial of Service (REDoS). If an attacker provides a malicious SVG, it can make cairosvg get stuck processing the file for a very long time. This is fixed in version 2.5.1. See Referenced GitHub advisory for more information. | {'CVE-2021-21236', 'GHSA-hq37-853p-g5cf'} | 2021-01-13T15:43:00Z | 2021-01-06T17:15:00Z | null | null | null | {'https://github.com/Kozea/CairoSVG/security/advisories/GHSA-hq37-853p-g5cf', 'https://github.com/Kozea/CairoSVG/commit/cfc9175e590531d90384aa88845052de53d94bf3', 'https://pypi.org/project/CairoSVG/', 'https://github.com/Kozea/CairoSVG/releases/tag/2.5.1'} | null |
PyPI | GHSA-r6pg-pjwc-j585 | Null pointer dereference in `SparseFillEmptyRows` | ### Impact
An attacker can trigger a null pointer dereference in the implementation of `tf.raw_ops.SparseFillEmptyRows`:
```python
import tensorflow as tf
indices = tf.constant([], shape=[0, 0], dtype=tf.int64)
values = tf.constant([], shape=[0], dtype=tf.int64)
dense_shape = tf.constant([], shape=[0], dtype=tf.int64)
default_value = 0
tf.raw_ops.SparseFillEmptyRows(
indices=indices, values=values, dense_shape=dense_shape,
default_value=default_value)
```
This is because of missing [validation](https://github.com/tensorflow/tensorflow/blob/fdc82089d206e281c628a93771336bf87863d5e8/tensorflow/core/kernels/sparse_fill_empty_rows_op.cc#L230-L231) that was covered under a `TODO`. If the `dense_shape` tensor is empty, then `dense_shape_t.vec<>()` would cause a null pointer dereference in the implementation of the op:
```cc
template <typename T, typename Tindex>
struct SparseFillEmptyRows<CPUDevice, T, Tindex> {
Status operator()(OpKernelContext* context, const Tensor& default_value_t,
const Tensor& indices_t, const Tensor& values_t,
const Tensor& dense_shape_t,
typename AsyncOpKernel::DoneCallback done) {
...
const auto dense_shape = dense_shape_t.vec<Tindex>();
...
}
}
```
### Patches
We have patched the issue in GitHub commit [faa76f39014ed3b5e2c158593b1335522e573c7f](https://github.com/tensorflow/tensorflow/commit/faa76f39014ed3b5e2c158593b1335522e573c7f).
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.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by Yakun Zhang and Ying Wang of Baidu X-Team. | {'CVE-2021-29565'} | 2022-03-03T05:14:21.468966Z | 2021-05-21T14:25:11Z | LOW | null | {'CWE-476'} | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-r6pg-pjwc-j585', 'https://github.com/tensorflow/tensorflow/commit/faa76f39014ed3b5e2c158593b1335522e573c7f', 'https://nvd.nist.gov/vuln/detail/CVE-2021-29565'} | null |
PyPI | GHSA-3xg5-6c3j-vp8x | Improper Restriction of XML External Entity Reference in Quokka | XML External Entities (XXE) in Quokka v0.4.0 allows remote attackers to execute arbitrary code via the component 'quokka/utils/atom.py'. | {'CVE-2020-18703'} | 2022-03-23T20:30:06.318898Z | 2021-08-30T16:23:34Z | CRITICAL | null | {'CWE-611'} | {'https://nvd.nist.gov/vuln/detail/CVE-2020-18703', 'https://github.com/rochacbruno/quokka/issues/676', '://github.com/rochacbruno/quokka'} | null |
PyPI | PYSEC-2020-246 | null | Plone before 5.2.3 allows XXE attacks via a feature that is explicitly only available to the Manager role. | {'CVE-2020-28734', 'GHSA-wq6x-g685-w5f2'} | 2021-08-27T03:22:11.436437Z | 2020-12-30T19:15:00Z | null | null | null | {'https://github.com/advisories/GHSA-wq6x-g685-w5f2', 'https://dist.plone.org/release/5.2.3/RELEASE-NOTES.txt', 'https://www.misakikata.com/codes/plone/python-en.html', 'https://github.com/plone/Products.CMFPlone/issues/3209'} | null |
PyPI | GHSA-7pxj-m4jf-r6h2 | Missing validation during checkpoint loading | ### Impact
An attacker can trigger undefined behavior, integer overflows, segfaults and `CHECK`-fail crashes if they can change saved checkpoints from outside of TensorFlow.
This is because the checkpoints loading infrastructure is missing validation for invalid file formats.
### Patches
We have patched the issue in GitHub commits [b619c6f865715ca3b15ef1842b5b95edbaa710ad](https://github.com/tensorflow/tensorflow/commit/b619c6f865715ca3b15ef1842b5b95edbaa710ad), [e8dc63704c88007ee4713076605c90188d66f3d2](https://github.com/tensorflow/tensorflow/commit/e8dc63704c88007ee4713076605c90188d66f3d2), [368af875869a204b4ac552b9ddda59f6a46a56ec](https://github.com/tensorflow/tensorflow/commit/368af875869a204b4ac552b9ddda59f6a46a56ec), and [abcced051cb1bd8fb05046ac3b6023a7ebcc4578](https://github.com/tensorflow/tensorflow/commit/abcced051cb1bd8fb05046ac3b6023a7ebcc4578).
These fixes will be included in TensorFlow 2.7.0. We will also cherrypick these commits on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions. | {'CVE-2021-41203'} | 2022-03-03T05:12:50.093057Z | 2021-11-10T19:12:46Z | HIGH | null | {'CWE-345'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-41203', 'https://github.com/tensorflow/tensorflow/commit/b619c6f865715ca3b15ef1842b5b95edbaa710ad', 'https://github.com/tensorflow/tensorflow/commit/368af875869a204b4ac552b9ddda59f6a46a56ec', 'https://github.com/tensorflow/tensorflow', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-7pxj-m4jf-r6h2', 'https://github.com/tensorflow/tensorflow/commit/abcced051cb1bd8fb05046ac3b6023a7ebcc4578', 'https://github.com/tensorflow/tensorflow/commit/e8dc63704c88007ee4713076605c90188d66f3d2'} | null |
PyPI | PYSEC-2021-59 | null | The urllib3 library 1.26.x before 1.26.4 for Python omits SSL certificate validation in some cases involving HTTPS to HTTPS proxies. The initial connection to the HTTPS proxy (if an SSLContext isn't given via proxy_config) doesn't verify the hostname of the certificate. This means certificates for different servers that still validate properly with the default urllib3 SSLContext will be silently accepted. | {'GHSA-5phf-pp7p-vc2r', 'CVE-2021-28363'} | 2021-03-23T16:47:00Z | 2021-03-15T18:15:00Z | null | null | null | {'https://github.com/urllib3/urllib3/security/advisories/GHSA-5phf-pp7p-vc2r', 'https://github.com/urllib3/urllib3/commit/8d65ea1ecf6e2cdc27d42124e587c1b83a3118b0', 'https://pypi.org/project/urllib3/1.26.4/', 'https://github.com/urllib3/urllib3/commits/main'} | null |
PyPI | PYSEC-2021-616 | null | TensorFlow is an open source platform for machine learning. In affected versions the implementation of `ParallelConcat` misses some input validation and can produce a division by 0. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range. | {'GHSA-7v94-64hj-m82h', 'CVE-2021-41207'} | 2021-12-09T06:35:08.527679Z | 2021-11-05T22:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-7v94-64hj-m82h', 'https://github.com/tensorflow/tensorflow/commit/f2c3931113eaafe9ef558faaddd48e00a6606235'} | null |
PyPI | PYSEC-2021-246 | null | TensorFlow is an end-to-end open source platform for machine learning. Incomplete validation in `SparseAdd` results in allowing attackers to exploit undefined behavior (dereferencing null pointers) as well as write outside of bounds of heap allocated data. The implementation(https://github.com/tensorflow/tensorflow/blob/656e7673b14acd7835dc778867f84916c6d1cac2/tensorflow/core/kernels/sparse_add_op.cc) has a large set of validation for the two sparse tensor inputs (6 tensors in total), but does not validate that the tensors are not empty or that the second dimension of `*_indices` matches the size of corresponding `*_shape`. This allows attackers to send tensor triples that represent invalid sparse tensors to abuse code assumptions that are not protected by validation. 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. | {'GHSA-cjc7-49v2-jp64', 'CVE-2021-29609'} | 2021-08-27T03:22:40.807777Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/41727ff06111117bdf86b37db198217fd7a143cc', 'https://github.com/tensorflow/tensorflow/commit/6fd02f44810754ae7481838b6a67c5df7f909ca3', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cjc7-49v2-jp64'} | null |
PyPI | PYSEC-2021-753 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation of `tf.raw_ops.ResourceScatterDiv` is vulnerable to a division by 0 error. The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/resource_variable_ops.cc#L865) uses a common class for all binary operations but fails to treat the division by 0 case separately. We have patched the issue in GitHub commit 4aacb30888638da75023e6601149415b39763d76. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'CVE-2021-37642', 'GHSA-ch4f-829c-v5pw'} | 2021-12-09T06:35:35.943696Z | 2021-08-12T18:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-ch4f-829c-v5pw', 'https://github.com/tensorflow/tensorflow/commit/4aacb30888638da75023e6601149415b39763d76'} | null |
PyPI | PYSEC-2021-303 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation of SVDF in TFLite is [vulnerable to a null pointer error](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/lite/kernels/svdf.cc#L300-L313). The [`GetVariableInput` function](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/lite/kernels/kernel_util.cc#L115-L119) can return a null pointer but `GetTensorData` assumes that the argument is always a valid tensor. Furthermore, because `GetVariableInput` calls [`GetMutableInput`](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/lite/kernels/kernel_util.cc#L82-L90) which might return `nullptr`, the `tensor->is_variable` expression can also trigger a null pointer exception. We have patched the issue in GitHub commit 5b048e87e4e55990dae6b547add4dae59f4e1c76. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'CVE-2021-37681', 'GHSA-7xwj-5r4v-429p'} | 2021-08-27T03:22:46.881278Z | 2021-08-12T22:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/5b048e87e4e55990dae6b547add4dae59f4e1c76', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-7xwj-5r4v-429p'} | null |
PyPI | GHSA-p75f-g7gx-2r7p | Exposure of Sensitive Information to an Unauthorized Actor in Products.PluggableAuthService ZODBRoleManager | ### Impact
_What kind of vulnerability is it? Who is impacted?_
Information disclosure vulnerability - everyone can list the names of roles defined in the ZODB Role Manager plugin if the site uses this plugin.
### Patches
_Has the problem been patched? What versions should users upgrade to?_
The problem has been fixed in version 2.6.0. Depending on how you have installed Products.PluggableAuthService, you should change the buildout version pin to 2.6.0 and re-run the buildout, or if you used pip simply do `pip install "Products.PluggableAuthService>=2.6.0"`
### Workarounds
_Is there a way for users to fix or remediate the vulnerability without upgrading?_
There is no workaround. Users are encouraged to upgrade.
### References
_Are there any links users can visit to find out more?_
- [GHSA-p75f-g7gx-2r7p](https://github.com/zopefoundation/Products.PluggableAuthService/security/advisories/GHSA-p75f-g7gx-2r7p)
- [Products.PluggableAuthService on PyPI](https://github.com/zopefoundation/Products.PluggableAuthService)
### For more information
If you have any questions or comments about this advisory:
* Open an issue in the [Products.PluggableAuthService issue tracker](https://github.com/zopefoundation/Products.PluggableAuthService/issues)
* Email us at [security@plone.org](mailto:security@plone.org) | {'CVE-2021-21336'} | 2022-03-03T05:13:33.578356Z | 2021-03-08T20:38:35Z | LOW | null | {'CWE-200'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-21336', 'http://www.openwall.com/lists/oss-security/2021/05/22/1', 'http://www.openwall.com/lists/oss-security/2021/05/21/1', 'https://github.com/zopefoundation/Products.PluggableAuthService/security/advisories/GHSA-p75f-g7gx-2r7p', 'https://pypi.org/project/Products.PluggableAuthService/', 'https://github.com/zopefoundation/Products.PluggableAuthService/commit/2dad81128250cb2e5d950cddc9d3c0314a80b4bb'} | null |
PyPI | PYSEC-2019-200 | null | python-requests-Kerberos through 0.5 does not handle mutual authentication | {'GHSA-wh37-37xw-54hr', 'CVE-2014-8650'} | 2021-08-27T03:22:19.338890Z | 2019-12-15T22:15:00Z | null | null | null | {'https://github.com/advisories/GHSA-wh37-37xw-54hr', 'https://bugzilla.redhat.com/show_bug.cgi?id=CVE-2014-8650', 'http://www.openwall.com/lists/oss-security/2014/11/07/1', 'http://www.securityfocus.com/bid/70909', 'https://security-tracker.debian.org/tracker/CVE-2014-8650'} | null |
PyPI | PYSEC-2015-13 | null | CRLF injection vulnerability in Kallithea before 0.3 allows remote attackers to inject arbitrary HTTP headers and conduct HTTP response splitting attacks via the came_from parameter to _admin/login. | {'CVE-2015-5285'} | 2021-07-05T00:01:22.184837Z | 2015-10-29T20:59:00Z | null | null | null | {'http://www.zeroscience.mk/en/vulnerabilities/ZSL-2015-5267.php', 'https://kallithea-scm.org/security/cve-2015-5285.html', 'https://www.exploit-db.com/exploits/38424/', 'http://packetstormsecurity.com/files/133897/Kallithea-0.2.9-HTTP-Response-Splitting.html'} | null |
PyPI | PYSEC-2020-303 | null | In TensorFlow before 1.15.2 and 2.0.1, converting a string (from Python) to a tf.float16 value results in a segmentation fault in eager mode as the format checks for this use case are only in the graph mode. This issue can lead to denial of service in inference/training where a malicious attacker can send a data point which contains a string instead of a tf.float16 value. Similar effects can be obtained by manipulating saved models and checkpoints whereby replacing a scalar tf.float16 value with a scalar string will trigger this issue due to automatic conversions. This can be easily reproduced by tf.constant("hello", tf.float16), if eager execution is enabled. This issue is patched in TensorFlow 1.15.1 and 2.0.1 with this vulnerability patched. TensorFlow 2.1.0 was released after we fixed the issue, thus it is not affected. Users are encouraged to switch to TensorFlow 1.15.1, 2.0.1 or 2.1.0. | {'GHSA-977j-xj7q-2jr9', 'CVE-2020-5215'} | 2021-12-09T06:34:45.123200Z | 2020-01-28T22:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/5ac1b9e24ff6afc465756edf845d2e9660bd34bf', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-977j-xj7q-2jr9', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.0.1', 'https://github.com/tensorflow/tensorflow/releases/tag/v1.15.2'} | null |
PyPI | GHSA-x7wr-283h-5h2v | Out-of-bounds Read in Onionshare | Between September 26, 2021 and October 8, 2021, [Radically Open Security](https://www.radicallyopensecurity.com/) conducted a penetration test of OnionShare 2.4, funded by the Open Technology Fund's [Red Team lab](https://www.opentech.fund/labs/red-team-lab/). This is an issue from that penetration test.
- Vulnerability ID: OTF-014
- Vulnerability type: Out-of-bounds Read
- Threat level: Elevated
## Description:
The desktop application was found to be vulnerable to denial of service via an undisclosed vulnerability in the QT image parsing.
## Technical description:
Prerequisites:
- Onion address is known
- Public service or authentication is valid
- Desktop application is used
- History is displayed
The rendering of images found in OTF-001 (page 25) could be elevated to a Denial of Service, which requires only very few bytes to be sent as a path parameter to any of the Onionshare functions. Roughly 20 bytes lead to 2GB memory consumption and this can be triggered multiple times. To be abused, this vulnerability requires rendering in the history tab, so some user interaction is required. The issue is in the process of disclosure to the QT security mailing list. More details will be provided after a fixed QT build has been deployed.
## Impact:
An adversary with knowledge of the Onion service address in public mode or with authentication in private mode can perform a Denial of Service attack, which quickly results in out-of-memory for the server. This requires the desktop application with rendered history, therefore the impact is only elevated.
## Recommendation:
- Monitor for upstream fix
- Fix OTF-001 (page 25) as a workaround | {'CVE-2022-21688'} | 2022-03-03T05:13:38.640706Z | 2022-01-21T23:20:29Z | HIGH | null | {'CWE-125'} | {'https://nvd.nist.gov/vuln/detail/CVE-2022-21688', 'https://github.com/onionshare/onionshare/security/advisories/GHSA-x7wr-283h-5h2v', 'https://github.com/onionshare/onionshare/releases/tag/v2.5', 'https://github.com/onionshare/onionshare'} | null |
PyPI | PYSEC-2020-23 | null | An issue was found in Apache Airflow versions 1.10.10 and below. A stored XSS vulnerability was discovered in the Chart pages of the the "classic" UI. | {'GHSA-j38c-25fj-mr84', 'CVE-2020-9485'} | 2020-07-21T18:38:00Z | 2020-07-17T00:15:00Z | null | null | null | {'https://github.com/advisories/GHSA-j38c-25fj-mr84', 'https://lists.apache.org/thread.html/r7255cf0be3566f23a768e2a04b40fb09e52fcd1872695428ba9afe91%40%3Cusers.airflow.apache.org%3E'} | null |
PyPI | GHSA-p44j-xrqg-4xrr | URL Redirection to Untrusted Site ('Open Redirect') in Products.PluggableAuthService | ### Impact
_What kind of vulnerability is it? Who is impacted?_
Open redirect vulnerability - a maliciously crafted link to the login form and login functionality could redirect the browser to a different website.
### Patches
_Has the problem been patched? What versions should users upgrade to?_
The problem has been fixed in version 2.6.1. Depending on how you have installed Products.PluggableAuthService, you should change the buildout version pin to `2.6.1` and re-run the buildout, or if you used `pip` simply do `pip install "Products.PluggableAuthService>=2.6.1"`
### Workarounds
_Is there a way for users to fix or remediate the vulnerability without upgrading?_
There is no workaround. Users are encouraged to upgrade.
### References
_Are there any links users can visit to find out more?_
- [GHSA-p44j-xrqg-4xrr](https://github.com/zopefoundation/Products.PluggableAuthService/security/advisories/GHSA-p44j-xrqg-4xrr)
- [Products.PluggableAuthService on PyPI](https://pypi.org/project/Products.PluggableAuthService/)
- [OWASP page on open redirects](https://cheatsheetseries.owasp.org/cheatsheets/Unvalidated_Redirects_and_Forwards_Cheat_Sheet.html)
### For more information
If you have any questions or comments about this advisory:
* Open an issue in the [Products.PluggableAuthService issue tracker](https://github.com/zopefoundation/Products.PluggableAuthService/issues)
* Email us at [security@plone.org](mailto:security@plone.org) | {'CVE-2021-21337'} | 2022-03-03T05:13:28.916575Z | 2021-03-08T21:06:23Z | LOW | null | {'CWE-601'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-21337', 'http://packetstormsecurity.com/files/162911/Products.PluggableAuthService-2.6.0-Open-Redirect.html', 'https://pypi.org/project/Products.PluggableAuthService/', 'https://github.com/zopefoundation/Products.PluggableAuthService/commit/7eead067898852ebd3e0f143bc51295928528dfa', 'https://github.com/zopefoundation/Products.PluggableAuthService/security/advisories/GHSA-p44j-xrqg-4xrr'} | null |
PyPI | PYSEC-2022-131 | null | Tensorflow is an Open Source Machine Learning Framework. The implementations of `Sparse*Cwise*` ops are vulnerable to integer overflows. These can be used to trigger large allocations (so, OOM based denial of service) or `CHECK`-fails when building new `TensorShape` objects (so, assert failures based denial of service). We are missing some validation on the shapes of the input tensors as well as directly constructing a large `TensorShape` with user-provided dimensions. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range. | {'GHSA-rrx2-r989-2c43', 'CVE-2022-23567'} | 2022-03-09T00:18:26.570322Z | 2022-02-03T12:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/1b54cadd19391b60b6fcccd8d076426f7221d5e8', 'https://github.com/tensorflow/tensorflow/blob/master/tensorflow/security/advisory/tfsa-2021-198.md', 'https://github.com/tensorflow/tensorflow/commit/e952a89b7026b98fe8cbe626514a93ed68b7c510', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-rrx2-r989-2c43', 'https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/sparse_dense_binary_op_shared.cc'} | null |
PyPI | PYSEC-2021-587 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can trigger a denial of service via a segmentation fault in `tf.raw_ops.MaxPoolGrad` caused by missing validation. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/maxpooling_op.cc) misses some validation for the `orig_input` and `orig_output` tensors. The fixes for CVE-2021-29579 were incomplete. We have patched the issue in GitHub commit 136b51f10903e044308cf77117c0ed9871350475. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'GHSA-7ghq-fvr3-pj2x', 'CVE-2021-37674'} | 2021-12-09T06:35:05.322708Z | 2021-08-12T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/blob/master/tensorflow/security/advisory/tfsa-2021-068.md', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-7ghq-fvr3-pj2x', 'https://github.com/tensorflow/tensorflow/commit/136b51f10903e044308cf77117c0ed9871350475'} | null |
PyPI | PYSEC-2014-86 | null | The (1) make_nonce, (2) generate_nonce, and (3) generate_verifier functions in SimpleGeo python-oauth2 uses weak random numbers to generate nonces, which makes it easier for remote attackers to guess the nonce via a brute force attack. | {'CVE-2013-4347'} | 2021-08-27T03:22:09.666793Z | 2014-05-20T14:55:00Z | null | null | null | {'http://www.openwall.com/lists/oss-security/2013/09/12/7', 'https://github.com/simplegeo/python-oauth2/issues/9', 'http://www.securityfocus.com/bid/62388', 'https://github.com/simplegeo/python-oauth2/pull/146'} | null |
PyPI | PYSEC-2021-568 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can trigger a read from outside of bounds of heap allocated data by sending invalid arguments to `tf.raw_ops.ResourceScatterUpdate`. The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/resource_variable_ops.cc#L919-L923) has an incomplete validation of the relationship between the shapes of `indices` and `updates`: instead of checking that the shape of `indices` is a prefix of the shape of `updates` (so that broadcasting can happen), code only checks that the number of elements in these two tensors are in a divisibility relationship. We have patched the issue in GitHub commit 01cff3f986259d661103412a20745928c727326f. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'GHSA-7fvx-3jfc-2cpc', 'CVE-2021-37655'} | 2021-12-09T06:35:03.682408Z | 2021-08-12T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-7fvx-3jfc-2cpc', 'https://github.com/tensorflow/tensorflow/commit/01cff3f986259d661103412a20745928c727326f'} | null |
PyPI | PYSEC-2014-69 | null | python-keystoneclient before 0.2.4, as used in OpenStack Keystone (Folsom), does not properly check expiry for PKI tokens, which allows remote authenticated users to (1) retain use of a token after it has expired, or (2) use a revoked token once it expires. | {'CVE-2013-2104'} | 2021-07-25T23:34:51.958825Z | 2014-01-21T18:55:00Z | null | null | null | {'http://www.ubuntu.com/usn/USN-1851-1', 'https://bugs.launchpad.net/python-keystoneclient/+bug/1179615', 'http://rhn.redhat.com/errata/RHSA-2013-0944.html', 'http://www.openwall.com/lists/oss-security/2013/05/28/7', 'http://lists.opensuse.org/opensuse-updates/2013-06/msg00198.html', 'http://www.ubuntu.com/usn/USN-1875-1'} | null |
PyPI | PYSEC-2021-138 | null | An issue was discovered in Pillow before 8.2.0. There is an out-of-bounds read in J2kDecode, in j2ku_gray_i. | {'GHSA-rwv7-3v45-hg29', 'CVE-2021-25288'} | 2021-08-27T03:22:10.437557Z | 2021-06-02T16:15:00Z | null | null | null | {'https://github.com/advisories/GHSA-rwv7-3v45-hg29', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/MQHA5HAIBOYI3R6HDWCLAGFTIQP767FL/', 'https://github.com/python-pillow/Pillow/pull/5377#issuecomment-833821470', 'https://pillow.readthedocs.io/en/stable/releasenotes/8.2.0.html#cve-2021-25287-cve-2021-25288-fix-oob-read-in-jpeg2kdecode'} | null |
PyPI | PYSEC-2019-191 | null | An issue was discovered in OpenStack Nova before 17.0.12, 18.x before 18.2.2, and 19.x before 19.0.2. If an API request from an authenticated user ends in a fault condition due to an external exception, details of the underlying environment may be leaked in the response, and could include sensitive configuration or other data. | {'CVE-2019-14433'} | 2021-08-27T03:22:09.327110Z | 2019-08-09T19:15:00Z | null | null | null | {'https://access.redhat.com/errata/RHSA-2019:2631', 'https://launchpad.net/bugs/1837877', 'https://access.redhat.com/errata/RHSA-2019:2652', 'https://usn.ubuntu.com/4104-1/', 'http://www.openwall.com/lists/oss-security/2019/08/06/6', 'https://security.openstack.org/ossa/OSSA-2019-003.html', 'https://access.redhat.com/errata/RHSA-2019:2622'} | null |
PyPI | PYSEC-2012-17 | null | Tweepy does not verify that the server hostname matches a domain name in the subject's Common Name (CN) or subjectAltName field of the X.509 certificate, which allows man-in-the-middle attackers to spoof SSL servers via an arbitrary valid certificate, related to use of the Python httplib library. | {'CVE-2012-5825'} | 2021-08-27T03:22:49.526995Z | 2012-11-04T22:55:00Z | null | null | null | {'http://www.cs.utexas.edu/~shmat/shmat_ccs12.pdf', 'https://exchange.xforce.ibmcloud.com/vulnerabilities/79831'} | null |
PyPI | GHSA-jggw-2q6g-c3m6 | Out-of-bounds Read in OpenCV | An out-of-bounds read was discovered in OpenCV before 4.1.1 (OpenCV-Python before 4.1.0.25). Specifically, variable coarsest_scale is assumed to be greater than or equal to finest_scale within the calc()/ocl_calc() functions in dis_flow.cpp. However, this is not true when dealing with small images, leading to an out-of-bounds read of the heap-allocated arrays Ux and Uy. | {'CVE-2019-19624'} | 2022-03-03T05:13:13.001034Z | 2021-10-12T22:22:58Z | MODERATE | null | {'CWE-125'} | {'https://github.com/opencv/opencv-python/releases/tag/25', 'https://access.redhat.com/security/cve/cve-2019-19624', 'https://github.com/opencv/opencv/issues/14554', 'https://nvd.nist.gov/vuln/detail/CVE-2019-19624', 'https://github.com/opencv/opencv/commit/d1615ba11a93062b1429fce9f0f638d1572d3418', 'https://github.com/opencv/opencv-python'} | null |
PyPI | GHSA-wp3c-xw9g-gpcg | Lack of validation in `SparseDenseCwiseMul` | ### Impact
Due to lack of validation in `tf.raw_ops.SparseDenseCwiseMul`, an attacker can trigger denial of service via `CHECK`-fails or accesses to outside the bounds of heap allocated data:
```python
import tensorflow as tf
indices = tf.constant([], shape=[10, 0], dtype=tf.int64)
values = tf.constant([], shape=[0], dtype=tf.int64)
shape = tf.constant([0, 0], shape=[2], dtype=tf.int64)
dense = tf.constant([], shape=[0], dtype=tf.int64)
tf.raw_ops.SparseDenseCwiseMul(
sp_indices=indices, sp_values=values, sp_shape=shape, dense=dense)
```
Since the [implementation](https://github.com/tensorflow/tensorflow/blob/38178a2f7a681a7835bb0912702a134bfe3b4d84/tensorflow/core/kernels/sparse_dense_binary_op_shared.cc#L68-L80) only validates the rank of the input arguments but no [constraints between dimensions](https://www.tensorflow.org/api_docs/python/tf/raw_ops/SparseDenseCwiseMul), an attacker can abuse them to trigger internal `CHECK` assertions (and cause program termination, denial of service) or to write to memory outside of bounds of heap allocated tensor buffers.
### Patches
We have patched the issue in GitHub commit [7ae2af34087fb4b5c8915279efd03da3b81028bc](https://github.com/tensorflow/tensorflow/commit/7ae2af34087fb4b5c8915279efd03da3b81028bc).
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.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by Yakun Zhang and Ying Wang of Baidu X-Team. | {'CVE-2021-29567'} | 2022-03-03T05:13:36.442445Z | 2021-05-21T14:25:16Z | LOW | null | {'CWE-617'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-29567', 'https://github.com/tensorflow/tensorflow/commit/7ae2af34087fb4b5c8915279efd03da3b81028bc', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-wp3c-xw9g-gpcg'} | null |
PyPI | PYSEC-2020-138 | null | In Tensorflow before version 2.4.0, an attacker can pass an invalid `axis` value to `tf.quantization.quantize_and_dequantize`. This results in accessing a dimension outside the rank of the input tensor in the C++ kernel implementation. However, dim_size only does a DCHECK to validate the argument and then uses it to access the corresponding element of an array. Since in normal builds, `DCHECK`-like macros are no-ops, this results in segfault and access out of bounds of the array. The issue is patched in eccb7ec454e6617738554a255d77f08e60ee0808 and TensorFlow 2.4.0 will be released containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved. | {'GHSA-rrfp-j2mp-hq9c', 'CVE-2020-15265'} | 2021-09-01T08:19:35.574576Z | 2020-10-21T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-rrfp-j2mp-hq9c', 'https://github.com/tensorflow/tensorflow/issues/42105', 'https://github.com/tensorflow/tensorflow/commit/eccb7ec454e6617738554a255d77f08e60ee0808'} | null |
PyPI | PYSEC-2021-211 | null | TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.MaxPool3DGradGrad` exhibits undefined behavior by dereferencing null pointers backing attacker-supplied empty tensors. The implementation(https://github.com/tensorflow/tensorflow/blob/72fe792967e7fd25234342068806707bbc116618/tensorflow/core/kernels/pooling_ops_3d.cc#L679-L703) fails to validate that the 3 tensor inputs are not empty. If any of them is empty, then accessing the elements in the tensor results in dereferencing a null pointer. 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. | {'GHSA-828x-qc2p-wprq', 'CVE-2021-29574'} | 2021-08-27T03:22:34.535736Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/a3d9f9be9ac2296615644061b40cefcee341dcc4', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-828x-qc2p-wprq'} | null |
PyPI | GHSA-xvwv-6wvx-px9x | Moderate severity vulnerability that affects plone | By linking to a specific url in Plone 2.5-5.1rc1 with a parameter, an attacker could send you to his own website. On its own this is not so bad: the attacker could more easily link directly to his own website instead. But in combination with another attack, you could be sent to the Plone login form and login, then get redirected to the specific url, and then get a second redirect to the attacker website. (The specific url can be seen by inspecting the hotfix code, but we don't want to make it too easy for attackers by spelling it out here.) | {'CVE-2017-1000484'} | 2022-03-03T05:13:04.754722Z | 2019-01-04T17:47:21Z | MODERATE | null | {'CWE-601'} | {'https://github.com/advisories/GHSA-xvwv-6wvx-px9x', 'https://nvd.nist.gov/vuln/detail/CVE-2017-1000484', 'https://github.com/plone/Plone', 'https://plone.org/security/hotfix/20171128/an-open-redirection-when-calling-a-specific-url'} | null |
PyPI | GHSA-c84h-w6cr-5v8q | Markdown-supplied Shell Command Execution | ### Impact
lookatme versions prior to 2.3.0 automatically loaded the built-in "terminal" and "file_loader" extensions. Users that use lookatme to render untrusted markdown may have malicious shell commands automatically run on their system.
### Patches
Users should upgrade to lookatme versions 2.3.0 or above.
### Workarounds
The `lookatme/contrib/terminal.py` and `lookatme/contrib/file_loader.py` files may be manually deleted. Additionally, it is always recommended to be aware of what is being rendered with lookatme.
### References
* https://github.com/d0c-s4vage/lookatme/pull/110
* https://github.com/d0c-s4vage/lookatme/releases/tag/v2.3.0
### For more information
If you have any questions or comments about this advisory:
* Open an issue in [d0c-s4vage/lookatme](https://github.com/d0c-s4vage/lookatme) | {'CVE-2020-15271'} | 2022-03-22T20:47:04.232825Z | 2020-10-27T17:59:54Z | CRITICAL | null | {'CWE-78'} | {'https://pypi.org/project/lookatme/#history', 'https://github.com/d0c-s4vage/lookatme/pull/110', 'https://github.com/d0c-s4vage/lookatme/security/advisories/GHSA-c84h-w6cr-5v8q', 'https://github.com/d0c-s4vage/lookatme/commit/72fe36b784b234548d49dae60b840c37f0eb8d84', 'https://nvd.nist.gov/vuln/detail/CVE-2020-15271', 'https://github.com/d0c-s4vage/lookatme/releases/tag/v2.3.0', 'https://github.com/d0c-s4vage/lookatme'} | null |
PyPI | PYSEC-2021-641 | null | 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. | {'CVE-2021-29515', 'GHSA-hc6c-75p4-hmq4'} | 2021-12-09T06:35:17.530281Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/a7116dd3913c4a4afd2a3a938573aa7c785fdfc6', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hc6c-75p4-hmq4'} | null |
PyPI | PYSEC-2019-4 | null | In Ansible, all Ansible Engine versions up to ansible-engine 2.8.5, ansible-engine 2.7.13, ansible-engine 2.6.19, were logging at the DEBUG level which lead to a disclosure of credentials if a plugin used a library that logged credentials at the DEBUG level. This flaw does not affect Ansible modules, as those are executed in a separate process. | {'CVE-2019-14846'} | 2021-03-26T22:15:00Z | 2019-10-08T19:15:00Z | null | null | null | {'http://lists.opensuse.org/opensuse-security-announce/2020-04/msg00021.html', 'https://access.redhat.com/errata/RHSA-2020:0756', 'https://bugzilla.redhat.com/show_bug.cgi?id=CVE-2019-14846', 'https://access.redhat.com/errata/RHSA-2019:3203', 'https://lists.debian.org/debian-lts-announce/2021/01/msg00023.html', 'https://github.com/ansible/ansible/pull/63366', 'https://lists.debian.org/debian-lts-announce/2020/05/msg00005.html', 'https://access.redhat.com/errata/RHSA-2019:3201', 'http://lists.opensuse.org/opensuse-security-announce/2020-04/msg00026.html', 'https://access.redhat.com/errata/RHSA-2019:3207', 'https://access.redhat.com/errata/RHSA-2019:3202'} | null |
PyPI | PYSEC-2020-211 | null | packet.py in pyrad before 2.1 uses weak random numbers to generate RADIUS authenticators and hash passwords, which makes it easier for remote attackers to obtain sensitive information via a brute force attack. | {'CVE-2013-0294'} | 2021-07-05T00:01:25.072912Z | 2020-01-28T16:15:00Z | null | null | null | {'https://bugzilla.redhat.com/show_bug.cgi?id=911682', 'http://lists.fedoraproject.org/pipermail/package-announce/2013-September/116567.html', 'http://lists.fedoraproject.org/pipermail/package-announce/2013-September/115705.html', 'http://lists.fedoraproject.org/pipermail/package-announce/2013-September/115677.html', 'https://github.com/wichert/pyrad/commit/38f74b36814ca5b1a27d9898141126af4953bee5', 'https://exchange.xforce.ibmcloud.com/vulnerabilities/82133', 'http://www.openwall.com/lists/oss-security/2013/02/15/13', 'http://www.securityfocus.com/bid/57984'} | null |
PyPI | PYSEC-2010-7 | null | Race condition in the FTPHandler class in ftpserver.py in pyftpdlib before 0.5.1 allows remote attackers to cause a denial of service (daemon outage) by establishing and then immediately closing a TCP connection, leading to the accept function having an unexpected return value of None, a different vulnerability than CVE-2010-3494. | {'CVE-2009-5010'} | 2021-07-05T00:01:24.754790Z | 2010-10-19T20:00:00Z | null | null | null | {'http://bugs.python.org/issue6706', 'http://www.openwall.com/lists/oss-security/2010/09/11/2', 'http://www.openwall.com/lists/oss-security/2010/09/09/6', 'http://www.openwall.com/lists/oss-security/2010/09/24/3', 'http://code.google.com/p/pyftpdlib/source/diff?spec=svn439&r=439&format=side&path=/trunk/pyftpdlib/ftpserver.py', 'http://code.google.com/p/pyftpdlib/source/browse/trunk/HISTORY', 'https://bugs.launchpad.net/zodb/+bug/135108', 'http://code.google.com/p/pyftpdlib/source/detail?r=439', 'http://code.google.com/p/pyftpdlib/issues/detail?id=91', 'http://www.openwall.com/lists/oss-security/2010/09/22/3'} | null |
PyPI | PYSEC-2017-96 | null | The tlslite library before 0.4.9 for Python allows remote attackers to trigger a denial of service (runtime exception and process crash). | {'CVE-2015-3220'} | 2021-08-27T03:22:47.903181Z | 2017-06-13T16:29:00Z | null | null | null | {'https://bugzilla.redhat.com/show_bug.cgi?id=1254215', 'https://groups.google.com/forum/#!topic/tlslite-dev/MoWE7B0A4iU', 'https://github.com/trevp/tlslite/commit/aca8d4f898b436ff6754e1a9ab96cae976c8a853'} | null |
PyPI | GHSA-5hx2-qx8j-qjqm | Overflow/crash in `tf.image.resize` when size is large | ### Impact
If `tf.image.resize` is called with a large input argument then the TensorFlow process will crash due to a `CHECK`-failure caused by an overflow.
```python
import tensorflow as tf
import numpy as np
tf.keras.layers.UpSampling2D(
size=1610637938,
data_format='channels_first',
interpolation='bilinear')(np.ones((5,1,1,1)))
```
The number of elements in the output tensor is too much for the `int64_t` type and the overflow is detected via a `CHECK` statement. This aborts the process.
### Patches
We have patched the issue in GitHub commit [e5272d4204ff5b46136a1ef1204fc00597e21837](https://github.com/tensorflow/tensorflow/commit/e5272d4204ff5b46136a1ef1204fc00597e21837) (merging [#51497](https://github.com/tensorflow/tensorflow/pull/51497)).
The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported externally via a [GitHub issue](https://github.com/tensorflow/tensorflow/issues/46914). | {'CVE-2021-41199'} | 2022-03-03T05:14:00.716577Z | 2021-11-10T19:33:24Z | MODERATE | null | {'CWE-190'} | {'https://github.com/tensorflow/tensorflow/commit/e5272d4204ff5b46136a1ef1204fc00597e21837', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-5hx2-qx8j-qjqm', 'https://github.com/tensorflow/tensorflow', 'https://nvd.nist.gov/vuln/detail/CVE-2021-41199', 'https://github.com/tensorflow/tensorflow/issues/46914'} | null |
PyPI | PYSEC-2017-79 | null | An exploitable vulnerability exists in the YAML parsing functionality in the read_yaml_file method in io_utils.py in django_make_app 0.1.3. A YAML parser can execute arbitrary Python commands resulting in command execution. An attacker can insert Python into loaded YAML to trigger this vulnerability. | {'CVE-2017-16764', 'GHSA-9pv8-q5rx-c8gq'} | 2021-08-25T04:29:58.287263Z | 2017-11-10T09:29:00Z | null | null | null | {'https://github.com/illagrenan/django-make-app/issues/5', 'https://github.com/advisories/GHSA-9pv8-q5rx-c8gq', 'https://joel-malwarebenchmark.github.io/blog/2017/11/12/cve-2017-16764-vulnerability-in-django-make-app/'} | null |
PyPI | GHSA-prr5-pfr8-q9f3 | Moderate severity vulnerability that affects Plone | ftp.py in Plone before 4.2.3 and 4.3 before beta 1 allows remote attackers to read hidden folder contents via unspecified vectors. | {'CVE-2012-5503'} | 2022-03-03T05:13:20.156336Z | 2018-07-23T19:52:35Z | MODERATE | null | null | {'https://github.com/advisories/GHSA-prr5-pfr8-q9f3', 'https://plone.org/products/plone/security/advisories/20121106/19', 'https://plone.org/products/plone-hotfix/releases/20121106', 'https://github.com/plone/Products.CMFPlone/blob/4.2.3/docs/CHANGES.txt', 'http://www.openwall.com/lists/oss-security/2012/11/10/1', 'https://nvd.nist.gov/vuln/detail/CVE-2012-5503', 'https://github.com/plone/Products.CMFPlone'} | null |
PyPI | PYSEC-2021-354 | null | furlongm openvpn-monitor through 1.1.3 allows Authorization Bypass to disconnect arbitrary clients. | {'CVE-2021-31606'} | 2021-09-30T23:26:26.851532Z | 2021-09-27T06:15:00Z | null | null | null | {'http://packetstormsecurity.com/files/164274/OpenVPN-Monitor-1.1.3-Authorization-Bypass-Denial-Of-Service.html', 'https://github.com/furlongm/openvpn-monitor/releases'} | null |
PyPI | GHSA-m6gj-h9gm-gw44 | Incorrect Default Permissions | An issue was discovered in Django 2.2 before 2.2.16, 3.0 before 3.0.10, and 3.1 before 3.1.1 (when Python 3.7+ is used). FILE_UPLOAD_DIRECTORY_PERMISSIONS mode was not applied to intermediate-level directories created in the process of uploading files. It was also not applied to intermediate-level collected static directories when using the collectstatic management command. | {'CVE-2020-24583'} | 2022-03-03T05:14:06.757335Z | 2021-03-18T20:30:13Z | HIGH | null | {'CWE-276'} | {'https://groups.google.com/forum/#!topic/django-announce/zFCMdgUnutU', 'https://usn.ubuntu.com/4479-1/', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/ZCRPQCBTV3RZHKVZ6K6QOAANPRZQD3GI/', 'https://www.openwall.com/lists/oss-security/2020/09/01/2', 'https://nvd.nist.gov/vuln/detail/CVE-2020-24583', 'https://www.djangoproject.com/weblog/2020/sep/01/security-releases/', 'https://groups.google.com/forum/#!topic/django-announce/Gdqn58RqIDM', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/F2ZHO3GZCJMP3DDTXCNVFV6ED3W64NAU/', 'https://github.com/django/django/commit/8d7271578d7b153435b40fe40236ebec43cbf1b9', 'https://docs.djangoproject.com/en/dev/releases/security/', 'https://www.oracle.com/security-alerts/cpujan2021.html', 'https://security.netapp.com/advisory/ntap-20200918-0004/', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/OLGFFLMF3X6USMJD7V5F5P4K2WVUTO3T/'} | null |
PyPI | PYSEC-2021-704 | null | TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.FractionalAvgPoolGrad` is vulnerable to a heap buffer overflow. The implementation(https://github.com/tensorflow/tensorflow/blob/dcba796a28364d6d7f003f6fe733d82726dda713/tensorflow/core/kernels/fractional_avg_pool_op.cc#L216) fails to validate that the pooling sequence arguments have enough elements as required by the `out_backprop` tensor shape. 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-2021-29578', 'GHSA-6f89-8j54-29xf'} | 2021-12-09T06:35:28.203971Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-6f89-8j54-29xf', 'https://github.com/tensorflow/tensorflow/commit/12c727cee857fa19be717f336943d95fca4ffe4f'} | null |
PyPI | PYSEC-2020-1 | null | A flaw was found in Ansible Engine affecting Ansible Engine versions 2.7.x before 2.7.17 and 2.8.x before 2.8.11 and 2.9.x before 2.9.7 as well as Ansible Tower before and including versions 3.4.5 and 3.5.5 and 3.6.3 when using modules which decrypts vault files such as assemble, script, unarchive, win_copy, aws_s3 or copy modules. The temporary directory is created in /tmp leaves the s ts unencrypted. On Operating Systems which /tmp is not a tmpfs but part of the root partition, the directory is only cleared on boot and the decryp emains when the host is switched off. The system will be vulnerable when the system is not running. So decrypted data must be cleared as soon as possible and the data which normally is encrypted ble. | {'CVE-2020-10685', 'GHSA-77g3-3j5w-64w4'} | 2020-06-13T04:15:00Z | 2020-05-11T14:15:00Z | null | null | null | {'https://github.com/ansible/ansible/pull/68433', 'https://github.com/advisories/GHSA-77g3-3j5w-64w4', 'https://bugzilla.redhat.com/show_bug.cgi?id=CVE-2020-10685', 'https://security.gentoo.org/glsa/202006-11'} | null |
PyPI | PYSEC-2014-90 | null | The shell_quote function in python-gnupg 0.3.5 does not properly quote strings, which allows context-dependent attackers to execute arbitrary code via shell metacharacters in unspecified vectors, as demonstrated using "$(" command-substitution sequences, a different vulnerability than CVE-2014-1928. NOTE: this vulnerability exists because of an incomplete fix for CVE-2013-7323. | {'GHSA-r3vr-prwv-86g9', 'CVE-2014-1927'} | 2021-08-27T03:22:18.134121Z | 2014-10-25T21:55:00Z | null | null | null | {'https://github.com/advisories/GHSA-r3vr-prwv-86g9', 'http://secunia.com/advisories/56616', 'https://code.google.com/p/python-gnupg/issues/detail?id=98', 'http://seclists.org/oss-sec/2014/q1/294', 'http://secunia.com/advisories/59031', 'http://www.debian.org/security/2014/dsa-2946', 'http://seclists.org/oss-sec/2014/q1/245', 'https://code.google.com/p/python-gnupg/'} | null |
PyPI | PYSEC-2021-591 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions TensorFlow and Keras can be tricked to perform arbitrary code execution when deserializing a Keras model from YAML format. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/python/keras/saving/model_config.py#L66-L104) uses `yaml.unsafe_load` which can perform arbitrary code execution on the input. Given that YAML format support requires a significant amount of work, we have removed it for now. We have patched the issue in GitHub commit 23d6383eb6c14084a8fc3bdf164043b974818012. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'CVE-2021-37678', 'GHSA-r6jx-9g48-2r5r'} | 2021-12-09T06:35:05.654112Z | 2021-08-12T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/23d6383eb6c14084a8fc3bdf164043b974818012', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-r6jx-9g48-2r5r'} | null |
PyPI | PYSEC-2022-127 | null | Tensorflow is an Open Source Machine Learning Framework. In multiple places, TensorFlow uses `tempfile.mktemp` to create temporary files. While this is acceptable in testing, in utilities and libraries it is dangerous as a different process can create the file between the check for the filename in `mktemp` and the actual creation of the file by a subsequent operation (a TOC/TOU type of weakness). In several instances, TensorFlow was supposed to actually create a temporary directory instead of a file. This logic bug is hidden away by the `mktemp` function usage. We have patched the issue in several commits, replacing `mktemp` with the safer `mkstemp`/`mkdtemp` functions, according to the usage pattern. Users are advised to upgrade as soon as possible. | {'GHSA-wc4g-r73w-x8mm', 'CVE-2022-23563'} | 2022-03-09T00:18:26.055433Z | 2022-02-04T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-wc4g-r73w-x8mm'} | null |
PyPI | PYSEC-2019-168 | null | A flaw was found in IPA, all 4.6.x versions before 4.6.7, all 4.7.x versions before 4.7.4 and all 4.8.x versions before 4.8.3, in the way that FreeIPA's batch processing API logged operations. This included passing user passwords in clear text on FreeIPA masters. Batch processing of commands with passwords as arguments or options is not performed by default in FreeIPA but is possible by third-party components. An attacker having access to system logs on FreeIPA masters could use this flaw to produce log file content with passwords exposed. | {'CVE-2019-10195'} | 2021-08-25T04:30:10.435825Z | 2019-11-27T08:15:00Z | null | null | null | {'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/WLFL5XDCJ3WT6JCLCQVKHZBLHGW7PW4T/', 'https://www.freeipa.org/page/Releases/4.8.3', 'https://access.redhat.com/errata/RHBA-2019:4268', 'https://www.freeipa.org/page/Releases/4.6.7', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/67SEUWJAJ5RMH5K4Q6TS2I7HIMXUGNKF/', 'https://www.freeipa.org/page/Releases/4.7.4', 'https://bugzilla.redhat.com/show_bug.cgi?id=CVE-2019-10195', 'https://access.redhat.com/errata/RHSA-2020:0378'} | null |
PyPI | PYSEC-2020-35 | null | Django 1.11 before 1.11.28, 2.2 before 2.2.10, and 3.0 before 3.0.3 allows SQL Injection if untrusted data is used as a StringAgg delimiter (e.g., in Django applications that offer downloads of data as a series of rows with a user-specified column delimiter). By passing a suitably crafted delimiter to a contrib.postgres.aggregates.StringAgg instance, it was possible to break escaping and inject malicious SQL. | {'GHSA-hmr4-m2h5-33qx', 'CVE-2020-7471'} | 2020-06-19T03:15:00Z | 2020-02-03T12:15:00Z | null | null | null | {'https://seclists.org/bugtraq/2020/Feb/30', 'https://www.openwall.com/lists/oss-security/2020/02/03/1', 'https://security.gentoo.org/glsa/202004-17', 'https://www.djangoproject.com/weblog/2020/feb/03/security-releases/', 'https://github.com/advisories/GHSA-hmr4-m2h5-33qx', 'https://docs.djangoproject.com/en/3.0/releases/security/', 'http://www.openwall.com/lists/oss-security/2020/02/03/1', 'https://usn.ubuntu.com/4264-1/', 'https://www.debian.org/security/2020/dsa-4629', 'https://github.com/django/django/commit/eb31d845323618d688ad429479c6dda973056136', 'https://security.netapp.com/advisory/ntap-20200221-0006/', 'https://groups.google.com/forum/#!topic/django-announce/X45S86X5bZI', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/4A2AP4T7RKPBCLTI2NNQG3T6MINDUUMZ/'} | null |
PyPI | PYSEC-2019-187 | null | Matrix Synapse before 0.34.0.1, when the macaroon_secret_key authentication parameter is not set, uses a predictable value to derive a secret key and other secrets which could allow remote attackers to impersonate users. | {'CVE-2019-5885'} | 2021-08-27T03:22:06.392030Z | 2019-03-21T16:01:00Z | null | null | null | {'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/VMCLO5PUPBA756UKY72PKUWL4RRM4W6K/', 'https://matrix.org/blog/2019/01/15/further-details-on-critical-security-update-in-synapse-affecting-all-versions-prior-to-0-34-1-cve-2019-5885/', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/32Y6KD3OAHCG5P33HC2QEX3NUZOSXCGZ/', 'https://matrix.org/blog/2019/01/10/critical-security-update-synapse-0-34-0-1-synapse-0-34-1-1/'} | null |
PyPI | GHSA-vfj6-275q-4pvm | graphite.composer.views.send_email vulnerable to SSRF | ### Impact
send_email in graphite-web/webapp/graphite/composer/views.py in Graphite through 1.1.5 is vulnerable to SSRF. The vulnerable SSRF endpoint can be used by an attacker to have the Graphite web server request any resource. The response to this SSRF request is encoded into an image file and then sent to an e-mail address that can be supplied by the attacker. Thus, an attacker can exfiltrate any information. Email will be send through SMTP server configured in Graphite, by default it's 'localhost'
### Patches
Problem was patched in Graphite-web 1.1.6. Also patches was released for graphite-web [1.0.x](https://github.com/graphite-project/graphite-web/pull/2501) and [0.9.x](https://github.com/graphite-project/graphite-web/pull/2500), and we'll discuss releases of non-supported branches later.
### Workarounds
You can manually remove function `send_email` from file `webapp/graphite/composer/views.py`. This function are not in use and will not affect your Graphite installation.
### References
For more information check this graphite-web Github issue #2008
### For more information
If you have any questions or comments about this advisory:
* Add comment in [issue #2008](https://github.com/graphite-project/graphite-web/issues/2008) | {'CVE-2017-18638'} | 2022-03-03T05:12:40.339114Z | 2019-10-25T13:55:20Z | HIGH | null | {'CWE-918'} | {'https://github.com/graphite-project/graphite-web/commit/71726a0e41a5263f49b973a7b856505a5b931c1f', 'https://nvd.nist.gov/vuln/detail/CVE-2017-18638', 'https://github.com/graphite-project/graphite-web/issues/2008', 'https://lists.debian.org/debian-lts-announce/2019/10/msg00030.html', 'https://github.com/graphite-project/graphite-web/security/advisories/GHSA-vfj6-275q-4pvm', 'https://www.youtube.com/watch?v=ds4Gp4xoaeA', 'https://blog.orange.tw/2017/07/how-i-chained-4-vulnerabilities-on.html#second-bug-internal-graphite-ssrf', 'https://github.com/graphite-project/graphite-web/pull/2499', 'https://github.com/graphite-project/graphite-web'} | null |
PyPI | PYSEC-2017-80 | null | mistune.py in Mistune 0.7.4 allows XSS via an unexpected newline (such as in java\nscript:) or a crafted email address, related to the escape and autolink functions. | {'CVE-2017-15612'} | 2021-08-25T04:57:34.565130Z | 2017-10-19T08:29:00Z | null | null | null | {'https://github.com/lepture/mistune/pull/140'} | null |
PyPI | PYSEC-2020-207 | null | A flaw was found in Ansible Engine, all versions 2.7.x, 2.8.x and 2.9.x prior to 2.7.17, 2.8.9 and 2.9.6 respectively, when using ansible_facts as a subkey of itself and promoting it to a variable when inject is enabled, overwriting the ansible_facts after the clean. An attacker could take advantage of this by altering the ansible_facts, such as ansible_hosts, users and any other key data which would lead into privilege escalation or code injection. | {'CVE-2020-10684', 'GHSA-p62g-jhg6-v3rq'} | 2021-07-02T02:41:34.761872Z | 2020-03-24T14:15:00Z | null | null | null | {'https://bugzilla.redhat.com/show_bug.cgi?id=CVE-2020-10684', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/WQVOQD4VAIXXTVQAJKTN7NUGTJFE2PCB/', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/MRRYUU5ZBLPBXCYG6CFP35D64NP2UB2S/', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/DKPA4KC3OJSUFASUYMG66HKJE7ADNGFW/', 'https://github.com/advisories/GHSA-p62g-jhg6-v3rq', 'https://security.gentoo.org/glsa/202006-11'} | null |
PyPI | PYSEC-2021-18 | null | CERN Indico before 2.3.4 can use an attacker-supplied Host header in a password reset link. | {'GHSA-wgpj-7c2j-vfjm', 'CVE-2021-30185'} | 2021-04-15T14:13:00Z | 2021-04-07T14:15:00Z | null | null | null | {'https://www.shorebreaksecurity.com/blog/', 'https://github.com/indico/indico/releases/tag/v2.3.4', 'https://github.com/advisories/GHSA-wgpj-7c2j-vfjm'} | null |
PyPI | PYSEC-2021-657 | null | 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. | {'CVE-2021-29531', 'GHSA-3qxp-qjq7-w4hf'} | 2021-12-09T06:35:20.083523Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-3qxp-qjq7-w4hf', 'https://github.com/tensorflow/tensorflow/commit/26eb323554ffccd173e8a79a8c05c15b685ae4d1'} | null |
PyPI | GHSA-6c7v-2f49-8h26 | Cleartext Transmission of Sensitive Information in Django | An issue was discovered in Django 1.11 before 1.11.22, 2.1 before 2.1.10, and 2.2 before 2.2.3. An HTTP request is not redirected to HTTPS when the SECURE_PROXY_SSL_HEADER and SECURE_SSL_REDIRECT settings are used, and the proxy connects to Django via HTTPS. In other words, django.http.HttpRequest.scheme has incorrect behavior when a client uses HTTP. | {'CVE-2019-12781'} | 2022-03-03T05:13:44.142429Z | 2019-07-03T20:37:25Z | MODERATE | null | {'CWE-319'} | {'https://groups.google.com/forum/#!topic/django-announce/Is4kLY9ZcZQ', 'https://www.djangoproject.com/weblog/2019/jul/01/security-releases/', 'https://seclists.org/bugtraq/2019/Jul/10', 'https://usn.ubuntu.com/4043-1/', 'http://www.openwall.com/lists/oss-security/2019/07/01/3', 'https://nvd.nist.gov/vuln/detail/CVE-2019-12781', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/5VXXWIOQGXOB7JCGJ3CVUW673LDHKEYL/', 'http://lists.opensuse.org/opensuse-security-announce/2019-08/msg00006.html', 'https://www.debian.org/security/2019/dsa-4476', 'https://security.netapp.com/advisory/ntap-20190705-0002/', 'http://lists.opensuse.org/opensuse-security-announce/2019-08/msg00025.html', 'https://docs.djangoproject.com/en/dev/releases/security/', 'http://www.securityfocus.com/bid/109018'} | null |
PyPI | PYSEC-2021-207 | null | TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.MaxPoolGradWithArgmax` can cause reads outside of bounds of heap allocated data if attacker supplies specially crafted inputs. The implementation(https://github.com/tensorflow/tensorflow/blob/ef0c008ee84bad91ec6725ddc42091e19a30cf0e/tensorflow/core/kernels/maxpooling_op.cc#L1016-L1017) uses the same value to index in two different arrays but there is no guarantee that the sizes are identical. 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-2021-29570', 'GHSA-545v-42p7-98fq'} | 2021-08-27T03:22:33.847369Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-545v-42p7-98fq', 'https://github.com/tensorflow/tensorflow/commit/dcd7867de0fea4b72a2b34bd41eb74548dc23886'} | null |
PyPI | PYSEC-2011-11 | null | Cross-site scripting (XSS) vulnerability in Django 1.1.x before 1.1.4 and 1.2.x before 1.2.5 might allow remote attackers to inject arbitrary web script or HTML via a filename associated with a file upload. | {'CVE-2011-0697', 'GHSA-8m3r-rv5g-fcpq'} | 2021-07-15T02:22:08.384566Z | 2011-02-14T21:00:00Z | null | null | null | {'http://www.vupen.com/english/advisories/2011/0372', 'http://www.djangoproject.com/weblog/2011/feb/08/security/', 'https://bugzilla.redhat.com/show_bug.cgi?id=676359', 'http://openwall.com/lists/oss-security/2011/02/09/6', 'http://www.securityfocus.com/bid/46296', 'http://www.debian.org/security/2011/dsa-2163', 'http://www.vupen.com/english/advisories/2011/0441', 'http://lists.fedoraproject.org/pipermail/package-announce/2011-February/054207.html', 'http://secunia.com/advisories/43230', 'http://secunia.com/advisories/43382', 'http://www.vupen.com/english/advisories/2011/0388', 'http://www.mandriva.com/security/advisories?name=MDVSA-2011:031', 'http://secunia.com/advisories/43426', 'http://secunia.com/advisories/43297', 'https://github.com/advisories/GHSA-8m3r-rv5g-fcpq', 'http://www.ubuntu.com/usn/USN-1066-1', 'http://www.vupen.com/english/advisories/2011/0429', 'http://www.vupen.com/english/advisories/2011/0439', 'http://lists.fedoraproject.org/pipermail/package-announce/2011-February/054208.html'} | null |
PyPI | PYSEC-2021-712 | null | TensorFlow is an end-to-end open source platform for machine learning. Optimized pooling implementations in TFLite fail to check that the stride arguments are not 0 before calling `ComputePaddingHeightWidth`(https://github.com/tensorflow/tensorflow/blob/3f24ccd932546416ec906a02ddd183b48a1d2c83/tensorflow/lite/kernels/pooling.cc#L90). Since users can craft special models which will have `params->stride_{height,width}` be zero, this will result in a division by zero. 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-2021-29586', 'GHSA-26j7-6w8w-7922'} | 2021-12-09T06:35:29.712146Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/5f7975d09eac0f10ed8a17dbb6f5964977725adc', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-26j7-6w8w-7922'} | null |
PyPI | PYSEC-2021-342 | null | A Hardcoded JWT Secret Key in metadata.py in AdaptiveScale LXDUI through 2.1.3 allows attackers to gain admin access to the host system. | {'CVE-2021-40494'} | 2021-09-26T23:32:34.569818Z | 2021-09-03T02:15:00Z | null | null | null | {'https://github.com/AdaptiveScale/lxdui/pull/353'} | null |
PyPI | GHSA-q492-f7gr-27rp | Improper Restriction of Operations within the Bounds of a Memory Buffer in Google TensorFlow | Invalid memory access and/or a heap buffer overflow in the TensorFlow XLA compiler in Google TensorFlow before 1.7.1 could cause a crash or read from other parts of process memory via a crafted configuration file. | {'CVE-2018-10055'} | 2022-03-03T05:13:54.535296Z | 2019-04-30T15:37:31Z | HIGH | null | {'CWE-119'} | {'https://nvd.nist.gov/vuln/detail/CVE-2018-10055', 'https://github.com/tensorflow/tensorflow/blob/master/tensorflow/security/advisory/tfsa-2018-006.md'} | null |
PyPI | PYSEC-2015-4 | null | Django before 1.4.18, 1.6.x before 1.6.10, and 1.7.x before 1.7.3 allows remote attackers to spoof WSGI headers by using an _ (underscore) character instead of a - (dash) character in an HTTP header, as demonstrated by an X-Auth_User header. | {'CVE-2015-0219'} | 2021-07-05T00:01:19.325962Z | 2015-01-16T16:59:00Z | null | null | null | {'https://www.djangoproject.com/weblog/2015/jan/13/security/', 'http://lists.fedoraproject.org/pipermail/package-announce/2015-January/148485.html', 'http://advisories.mageia.org/MGASA-2015-0026.html', 'http://secunia.com/advisories/62285', 'http://secunia.com/advisories/62718', 'http://www.mandriva.com/security/advisories?name=MDVSA-2015:036', 'http://secunia.com/advisories/62309', 'http://lists.opensuse.org/opensuse-updates/2015-04/msg00001.html', 'http://lists.fedoraproject.org/pipermail/package-announce/2015-January/148608.html', 'http://lists.fedoraproject.org/pipermail/package-announce/2015-January/148696.html', 'http://lists.opensuse.org/opensuse-updates/2015-09/msg00035.html', 'http://www.ubuntu.com/usn/USN-2469-1', 'http://www.mandriva.com/security/advisories?name=MDVSA-2015:109'} | null |
PyPI | PYSEC-2020-62 | null | A XSS vulnerability was discovered in python-lxml's clean module. The module's parser didn't properly imitate browsers, which caused different behaviors between the sanitizer and the user's page. A remote attacker could exploit this flaw to run arbitrary HTML/JS code. | {'GHSA-pgww-xf46-h92r', 'CVE-2020-27783'} | 2021-03-30T21:15:00Z | 2020-12-03T17:15:00Z | null | null | null | {'https://advisory.checkmarx.net/advisory/CX-2020-4286', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/JKG67GPGTV23KADT4D4GK4RMHSO4CIQL/', 'https://www.debian.org/security/2020/dsa-4810', 'https://github.com/advisories/GHSA-pgww-xf46-h92r', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/TMHVKRUT22LVWNL3TB7HPSDHJT74Q3JK/', 'https://bugzilla.redhat.com/show_bug.cgi?id=1901633', 'https://lists.debian.org/debian-lts-announce/2020/12/msg00028.html'} | null |
PyPI | GHSA-jhq9-wm9m-cf89 | CHECK-failure in `UnsortedSegmentJoin` | ### Impact
An attacker can cause a denial of service by controlling the values of `num_segments` tensor argument for `UnsortedSegmentJoin`:
```python
import tensorflow as tf
inputs = tf.constant([], dtype=tf.string)
segment_ids = tf.constant([], dtype=tf.int32)
num_segments = tf.constant([], dtype=tf.int32)
separator = ''
tf.raw_ops.UnsortedSegmentJoin(
inputs=inputs, segment_ids=segment_ids,
num_segments=num_segments, separator=separator)
```
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:
```cc
const Tensor& num_segments_tensor = context->input(2);
auto num_segments = num_segments_tensor.scalar<NUM_SEGMENTS_TYPE>()();
```
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.
### Patches
We have patched the issue in GitHub commit [704866eabe03a9aeda044ec91a8d0c83fc1ebdbe](https://github.com/tensorflow/tensorflow/commit/704866eabe03a9aeda044ec91a8d0c83fc1ebdbe).
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.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by Ying Wang and Yakun Zhang of Baidu X-Team. | {'CVE-2021-29552'} | 2022-03-03T05:14:20.674010Z | 2021-05-21T14:23:48Z | LOW | null | {'CWE-617'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-29552', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-jhq9-wm9m-cf89', 'https://github.com/tensorflow/tensorflow/commit/704866eabe03a9aeda044ec91a8d0c83fc1ebdbe'} | null |
PyPI | GHSA-98gj-wwxm-cj3h | Moderate severity vulnerability that affects mistune | Cross-site scripting (XSS) vulnerability in the _keyify function in mistune.py in Mistune before 0.8.1 allows remote attackers to inject arbitrary web script or HTML by leveraging failure to escape the "key" argument. | {'CVE-2017-16876'} | 2022-03-03T05:13:01.434198Z | 2019-01-04T17:47:50Z | MODERATE | null | {'CWE-79'} | {'https://github.com/lepture/mistune', 'https://github.com/advisories/GHSA-98gj-wwxm-cj3h', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/NUR3GMHQBMA3UC4PFMCK6GCLOQC4LQQC/', 'https://github.com/lepture/mistune/commit/5f06d724bc05580e7f203db2d4a4905fc1127f98', 'https://github.com/lepture/mistune/blob/master/CHANGES.rst', 'https://nvd.nist.gov/vuln/detail/CVE-2017-16876', 'https://bugzilla.redhat.com/show_bug.cgi?id=1524596'} | null |
PyPI | GHSA-2v5j-q74q-r53f | django-helpdesk is vulnerable to Cross-site Scripting | django-helpdesk is vulnerable to Improper Neutralization of Input During Web Page Generation ('Cross-site Scripting'). | {'CVE-2021-3994'} | 2022-03-03T05:13:24.793404Z | 2021-12-03T20:42:26Z | HIGH | null | {'CWE-79'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-3994', 'https://github.com/django-helpdesk/django-helpdesk', 'https://huntr.dev/bounties/be7f211d-4bfd-44fd-91e8-682329906fbd', 'https://github.com/django-helpdesk/django-helpdesk/releases/tag/0.3.2', 'https://github.com/django-helpdesk/django-helpdesk/commit/a22eb0673fe0b7784f99c6b5fd343b64a6700f06'} | null |
PyPI | PYSEC-2021-196 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can access data outside of bounds of heap allocated array in `tf.raw_ops.UnicodeEncode`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/472c1f12ad9063405737679d4f6bd43094e1d36d/tensorflow/core/kernels/unicode_ops.cc) assumes that the `input_value`/`input_splits` pair specify a valid sparse 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. | {'CVE-2021-29559', 'GHSA-59q2-x2qc-4c97'} | 2021-08-27T03:22:31.940947Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/51300ba1cc2f487aefec6e6631fef03b0e08b298', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-59q2-x2qc-4c97'} | null |
PyPI | PYSEC-2022-170 | null | mitmproxy is an interactive, SSL/TLS-capable intercepting proxy. In mitmproxy 7.0.4 and below, a malicious client or server is able to perform HTTP request smuggling attacks through mitmproxy. This means that a malicious client/server could smuggle a request/response through mitmproxy as part of another request/response's HTTP message body. While mitmproxy would only see one request, the target server would see multiple requests. A smuggled request is still captured as part of another request's body, but it does not appear in the request list and does not go through the usual mitmproxy event hooks, where users may have implemented custom access control checks or input sanitization. Unless mitmproxy is used to protect an HTTP/1 service, no action is required. The vulnerability has been fixed in mitmproxy 8.0.0 and above. There are currently no known workarounds. | {'GHSA-gcx2-gvj7-pxv3', 'CVE-2022-24766'} | 2022-03-29T18:37:43.309818Z | 2022-03-21T19:15:00Z | null | null | null | {'https://mitmproxy.org/posts/releases/mitmproxy8/', 'https://github.com/mitmproxy/mitmproxy/security/advisories/GHSA-gcx2-gvj7-pxv3', 'https://github.com/mitmproxy/mitmproxy/commit/b06fb6d157087d526bd02e7aadbe37c56865c71b'} | null |
PyPI | PYSEC-2021-529 | null | TensorFlow is an end-to-end open source platform for machine learning. The TFLite implementation of concatenation is vulnerable to an integer overflow issue(https://github.com/tensorflow/tensorflow/blob/7b7352a724b690b11bfaae2cd54bc3907daf6285/tensorflow/lite/kernels/concatenation.cc#L70-L76). An attacker can craft a model such that the dimensions of one of the concatenation input overflow the values of `int`. TFLite uses `int` to represent tensor dimensions, whereas TF uses `int64`. Hence, valid TF models can trigger an integer overflow when converted to TFLite format. 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-2021-29601', 'GHSA-9c84-4hx6-xmm4'} | 2021-12-09T06:34:59.076380Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/4253f96a58486ffe84b61c0415bb234a4632ee73', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9c84-4hx6-xmm4'} | null |
PyPI | PYSEC-2016-15 | null | The utils.http.is_safe_url function in Django before 1.8.10 and 1.9.x before 1.9.3 allows remote attackers to redirect users to arbitrary web sites and conduct phishing attacks or possibly conduct cross-site scripting (XSS) attacks via a URL containing basic authentication, as demonstrated by http://mysite.example.com\@attacker.com. | {'CVE-2016-2512'} | 2021-07-15T02:22:10.137209Z | 2016-04-08T15:59:00Z | null | null | null | {'http://www.ubuntu.com/usn/USN-2915-3', 'http://www.securitytracker.com/id/1035152', 'https://github.com/django/django/commit/c5544d289233f501917e25970c03ed444abbd4f0', 'http://rhn.redhat.com/errata/RHSA-2016-0506.html', 'http://rhn.redhat.com/errata/RHSA-2016-0505.html', 'http://www.ubuntu.com/usn/USN-2915-1', 'http://www.securityfocus.com/bid/83879', 'http://www.debian.org/security/2016/dsa-3544', 'http://www.ubuntu.com/usn/USN-2915-2', 'http://www.oracle.com/technetwork/topics/security/bulletinapr2016-2952098.html', 'http://rhn.redhat.com/errata/RHSA-2016-0504.html', 'https://www.djangoproject.com/weblog/2016/mar/01/security-releases/', 'http://rhn.redhat.com/errata/RHSA-2016-0502.html'} | null |
PyPI | PYSEC-2014-28 | null | ZPublisher.HTTPRequest._scrubHeader in Zope 2 before 2.13.19, as used in Plone before 4.3 beta 1, allows remote attackers to inject arbitrary HTTP headers via a linefeed (LF) character. | {'CVE-2012-5486', 'GHSA-77hv-8796-8ccp'} | 2021-07-25T23:34:43.396566Z | 2014-09-30T14:55:00Z | null | null | null | {'https://plone.org/products/plone-hotfix/releases/20121106', 'https://bugs.launchpad.net/zope2/+bug/930812', 'https://github.com/advisories/GHSA-77hv-8796-8ccp', 'https://plone.org/products/plone/security/advisories/20121106/02', 'http://www.openwall.com/lists/oss-security/2012/11/10/1', 'http://rhn.redhat.com/errata/RHSA-2014-1194.html'} | null |
PyPI | PYSEC-2021-179 | null | 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. | {'CVE-2021-29542', 'GHSA-4hrh-9vmp-2jgg'} | 2021-08-27T03:22:28.937409Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/ba424dd8f16f7110eea526a8086f1a155f14f22b', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4hrh-9vmp-2jgg'} | null |
PyPI | PYSEC-2021-483 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a denial of service via a FPE runtime error in `tf.raw_ops.FusedBatchNorm`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/828f346274841fa7505f7020e88ca36c22e557ab/tensorflow/core/kernels/fused_batch_norm_op.cc#L295-L297) performs a division based on the last dimension of the `x` tensor. Since this is controlled by the user, an attacker can trigger 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-2021-29555', 'GHSA-r35g-4525-29fq'} | 2021-12-09T06:34:51.920437Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-r35g-4525-29fq', 'https://github.com/tensorflow/tensorflow/commit/1a2a87229d1d61e23a39373777c056161eb4084d'} | null |
PyPI | GHSA-663j-rjcr-789f | CSV injection in shuup | “Shuup” application in versions 0.4.2 to 2.10.8 is affected by the “Formula Injection” vulnerability. A customer can inject payloads in the name input field in the billing address while buying a product. When a store administrator accesses the reports page to export the data as an Excel file and opens it, the payload gets executed. | {'CVE-2021-25962'} | 2022-03-03T05:13:34.774281Z | 2021-09-30T20:50:07Z | HIGH | null | {'CWE-1236'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-25962', 'https://github.com/shuup/shuup', 'https://github.com/shuup/shuup/commit/0a2db392e8518410c282412561461cd8797eea51', 'https://www.whitesourcesoftware.com/vulnerability-database/CVE-2021-25962'} | null |
PyPI | PYSEC-2021-250 | null | TensorFlow is an end-to-end open source platform for machine learning. Incomplete validation in `tf.raw_ops.CTCLoss` allows an attacker to trigger an OOB read from heap. The fix will be included in TensorFlow 2.5.0. We will also cherrypick these commits 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. | {'GHSA-vvg4-vgrv-xfr7', 'CVE-2021-29613'} | 2021-08-27T03:22:41.522961Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-vvg4-vgrv-xfr7', 'https://github.com/tensorflow/tensorflow/commit/14607c0707040d775e06b6817325640cb4b5864c', 'https://github.com/tensorflow/tensorflow/commit/4504a081af71514bb1828048363e6540f797005b'} | null |
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