PRNGs are algorithms that produce sequences of numbers that only approximate true randomness. While they are suitable for applications like simulations or modeling, they are not appropriate for security-sensitive contexts because their outputs can be predictable if the internal state is known.

In contrast, cryptographically secure pseudorandom number generators (CSPRNGs) are designed to be secure against prediction attacks. CSPRNGs use cryptographic algorithms to ensure that the generated sequences are not only random but also unpredictable, even if part of the sequence or the internal state becomes known. This unpredictability is crucial for security-related tasks such as generating encryption keys, tokens, or any other values that must remain confidential and resistant to guessing attacks.

For example, the use of non-cryptographic PRNGs has led to vulnerabilities such as:

* https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2013-6386[CVE-2013-6386]
* https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2006-3419[CVE-2006-3419]
* https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2008-4102[CVE-2008-4102]

When software generates predictable values in a context requiring unpredictability, it may be possible for an attacker to guess the next value that will be generated, and use this guess to impersonate another user or access sensitive information. Therefore, it is critical to use CSPRNGs in any security-sensitive application to ensure the robustness and security of the system.
