CWE:
 

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Common Weakness Enumeration (CWE)

CVE
Szczegóły
Opis
2022-05-18
Medium
CVE-2019-25061

Vendor: Random password generator project
Software: Random passw...
 

 
The random_password_generator (aka RandomPasswordGenerator) gem through 1.0.0 for Ruby uses Kernel#rand to generate passwords, which, due to its cyclic nature, can facilitate password prediction.

 
2022-04-08
Medium
CVE-2022-26852

Vendor: DELL
Software: Emc powersca...
 

 
Dell PowerScale OneFS, versions 8.2.x-9.3.x, contain a predictable seed in pseudo-random number generator. A remote unauthenticated attacker could potentially exploit this vulnerability, leading to an account compromise.

 
2022-01-28
Medium
CVE-2016-3735

Vendor: Piwigo
Software: Piwigo
 

 
Piwigo is image gallery software written in PHP. When a criteria is not met on a host, piwigo defaults to usingmt_rand in order to generate password reset tokens. mt_rand output can be predicted after recovering the seed used to generate it. This low an unauthenticated attacker to take over an account providing they know an administrators email address in order to be able to request password reset.

 
2022-01-19
Medium
CVE-2021-42810

Vendor: Thalesgroup
Software: Safenet auth...
 

 
A flaw in the previous versions of the product may allow an authenticated attacker the ability to execute code as a privileged user on a system where the agent is installed.

 
2021-10-11
Medium
CVE-2021-41117

Vendor: Keypair project
Software: Keypair
 

 
keypair is a a RSA PEM key generator written in javascript. keypair implements a lot of cryptographic primitives on its own or by borrowing from other libraries where possible, including node-forge. An issue was discovered where this library was generating identical RSA keys used in SSH. This would mean that the library is generating identical P, Q (and thus N) values which, in practical terms, is impossible with RSA-2048 keys. Generating identical values, repeatedly, usually indicates an issue with poor random number generation, or, poor handling of CSPRNG output. Issue 1: Poor random number generation (`GHSL-2021-1012`). The library does not rely entirely on a platform provided CSPRNG, rather, it uses it's own counter-based CMAC approach. Where things go wrong is seeding the CMAC implementation with "true" random data in the function `defaultSeedFile`. In order to seed the AES-CMAC generator, the library will take two different approaches depending on the JavaScript execution environment. In a browser, the library will use [`window.crypto.getRandomValues()`](https://github.com/juliangruber/keypair/blob/87c62f255baa12c1ec4f98a91600f82af80be6db/index.js#L971). However, in a nodeJS execution environment, the `window` object is not defined, so it goes down a much less secure solution, also of which has a bug in it. It does look like the library tries to use node's CSPRNG when possible unfortunately, it looks like the `crypto` object is null because a variable was declared with the same name, and set to `null`. So the node CSPRNG path is never taken. However, when `window.crypto.getRandomValues()` is not available, a Lehmer LCG random number generator is used to seed the CMAC counter, and the LCG is seeded with `Math.random`. While this is poor and would likely qualify in a security bug in itself, it does not explain the extreme frequency in which duplicate keys occur. The main flaw: The output from the Lehmer LCG is encoded incorrectly. The specific [line][https://github.com/juliangruber/keypair/blob/87c62f255baa12c1ec4f98a91600f82af80be6db/index.js#L1008] with the flaw is: `b.putByte(String.fromCharCode(next & 0xFF))` The [definition](https://github.com/juliangruber/keypair/blob/87c62f255baa12c1ec4f98a91600f82af80be6db/index.js#L350-L352) of `putByte` is `util.ByteBuffer.prototype.putByte = function(b) {this.data += String.fromCharCode(b);};`. Simplified, this is `String.fromCharCode(String.fromCharCode(next & 0xFF))`. The double `String.fromCharCode` is almost certainly unintentional and the source of weak seeding. Unfortunately, this does not result in an error. Rather, it results most of the buffer containing zeros. Since we are masking with 0xFF, we can determine that 97% of the output from the LCG are converted to zeros. The only outputs that result in meaningful values are outputs 48 through 57, inclusive. The impact is that each byte in the RNG seed has a 97% chance of being 0 due to incorrect conversion. When it is not, the bytes are 0 through 9. In summary, there are three immediate concerns: 1. The library has an insecure random number fallback path. Ideally the library would require a strong CSPRNG instead of attempting to use a LCG and `Math.random`. 2. The library does not correctly use a strong random number generator when run in NodeJS, even though a strong CSPRNG is available. 3. The fallback path has an issue in the implementation where a majority of the seed data is going to effectively be zero. Due to the poor random number generation, keypair generates RSA keys that are relatively easy to guess. This could enable an attacker to decrypt confidential messages or gain authorized access to an account belonging to the victim.

 
2021-03-03
Medium
CVE-2020-28597

Vendor: Epignosishq
Software: Efront
 

 
A predictable seed vulnerability exists in the password reset functionality of Epignosis EfrontPro 5.2.21. By predicting the seed it is possible to generate the correct password reset 1-time token. An attacker can visit the password reset supplying the password reset token to reset the password of an account of their choice.

 
2020-10-29
Medium
CVE-2020-11616

Updating...
 

 
NVIDIA DGX servers, all BMC firmware versions prior to 3.38.30, contain a vulnerability in the AMI BMC firmware in which the Pseudo-Random Number Generator (PRNG) algorithm used in the JSOL package that implements the IPMI protocol is not cryptographically strong, which may lead to information disclosure.

 
2020-10-27
Medium
CVE-2020-10256

Vendor: 1password
Software: Command-line
 

 
An issue was discovered in beta versions of the 1Password command-line tool prior to 0.5.5 and in beta versions of the 1Password SCIM bridge prior to 0.7.3. An insecure random number generator was used to generate various keys. An attacker with access to the user's encrypted data may be able to perform brute-force calculations of encryption keys and thus succeed at decryption.

 
2020-06-03
Medium
CVE-2020-7010

Vendor: Elastic
Software: Elastic clou...
 

 
Elastic Cloud on Kubernetes (ECK) versions prior to 1.1.0 generate passwords using a weak random number generator. If an attacker is able to determine when the current Elastic Stack cluster was deployed they may be able to more easily brute force the Elasticsearch credentials generated by ECK.

 
2019-12-10
Medium
CVE-2012-1577

Vendor: Dietlibc project
Software: Dietlibc
 

 
lib/libc/stdlib/random.c in OpenBSD returns 0 when seeded with 0.

 

 


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