Every encrypted message, every secure session, and every digital signature depends on two things: a strong cryptographic algorithm and unpredictable random numbers. Most conversations about cryptographic security focus on algorithms. This article focuses on the other half, the one that quietly determines whether everything else actually works: entropy.
We break down how modern cryptographic systems produce random numbers, why the combination of hardware entropy and software generators is the strongest approach, and how well-designed libraries make it straightforward to plug in the entropy source that fits your security requirements. Then we go further: we explain why generating good randomness is not enough, and why the ability to verify entropy quality, locally and in real time, is what separates assumed security from proven security.
The Two Pillars of Cryptographic Security
Modern cryptographic protocols, from online shopping to private messaging, stand on two independent but equally important foundations.
The first is mathematical complexity. Cryptographic algorithms rely on problems that are easy to set up but extremely hard to reverse. The classic example is prime factorization: multiplying two large primes together takes milliseconds, but finding those prime factors from the product can take longer than the age of the universe with today’s computers.
The second is randomness. Cryptographic protocols use random numbers to generate secret keys, initialization vectors, nonces, and other parameters. These values must be unpredictable. If an attacker can guess or reproduce them, the mathematical complexity behind the algorithm becomes irrelevant.
Think of it this way: you can install the strongest lock in the world, but if someone can predict the key, the lock does nothing. Random numbers are the keys. If they are weak, the whole system is weak.
Where Randomness Goes Wrong
These two pillars work together, but they also depend on each other. A strong algorithm can be completely undermined if the randomness feeding it is weak or predictable. We refer to this impredictability as “entropy”. Secret keys, initialization vectors, nonces, and ephemeral parameters all need to be generated in a way that no adversary can guess or reproduce. If the entropy is compromised or insufficient, the resulting values become predictable, and the security guarantees collapse.
This is not a theoretical concern. History is full of examples where randomness with weak entropy guarantees led to damaging breaches. In 2008, a Debian OpenSSL bug reduced the effective key space of their algorithms from 340,282,366,920,938,463,463,374,607,431,768,211,456 potential keys to just 32,767 possible keys, leaving millions of systems exposed for nearly two years before it was discovered. In 2010, Sony reused the same random number in PlayStation 3’s digital signature scheme, allowing hackers to extract the private signing key entirely. In 2024, Meta researchers demonstrated that even NIST-approved post-quantum algorithms (like ML-KEM/Kyber) are vulnerable to secret recovery when seeded with low-quality entropy.
The pattern is clear: when randomness is unreliable, everything built on top of it fails. And with post-quantum cryptography now entering production, the consequences of unreliable randomness are getting larger, not smaller.
How Cryptographic Systems Generate Random Numbers Today
Most cryptographic applications get their random numbers by calling the operating system’s entropy generation routines. These are usually based on pseudo-random number generators (PRNGs), and sometimes specifically on cryptographically secure PRNGs (CSPRNGs).
Here is how it works, for example, on your computer. The operating system (Windows, Mac, Linux) collects entropy from multiple sources that seem unpredictable: things like timing variations in hardware operations or electronic noise. It uses that information to seed a CSPRNG. Once seeded, the CSPRNG uses a deterministic algorithm to expand that small amount of initial entropy into a large stream of numbers that appear random and are suitable for cryptographic use.
These mechanisms have proven to be well-engineered and reliable. But they rely on the fact that their internal state cannot be guessed. If the internal state of a PRNG becomes known or predictable, the entire sequence of generated values can be reconstructed. That opens a path for an attacker to break the security assumptions of the system and compromise the encryption.
Critically, CSPRNGs cannot produce more unpredictability than what their seed provides. If the seed is weak, the output is weak, no matter how sophisticated the algorithm. They stretch entropy but they do not strengthen it.
The Winning Strategy: Combining External Entropy with Internal PRNGs
For these reasons, combining an external hardware entropy source with the operating system’s internal PRNG is often the best approach. By periodically reseeding the PRNG with the output of a truly unpredictable random source, like a QRNG, the system preserves the speed benefits of deterministic generators while keeping the underlying state difficult to predict or reconstruct.
This is the approach used in modern post-quantum cryptography (PQC) environments. It also strengthens overall security, because an external attacker would need to compromise both the entropy source and the PRNG to predict the output.
Modular Entropy in Practice: NVIDIA’s cuPQC
This modularity, the ability to choose and integrate an external entropy source, is an important design feature in cryptographic implementations. NVIDIA’s cuPQC, a GPU-accelerated PQC library, provides an excellent example of how this works in practice.
In its implementation, cuPQC defines the entropy generation function in a header file. Any developer can adapt it to their own preferences. By default, cuPQC reads random data from the system’s CSPRNG (accessible through the /dev/urandom file in a Linux environment). If a developer wants to use a different source, like a hardware RNG, they only need to modify the root from which the entropy is extracted.
The code below shows the default get_entropy function in cuPQC. It reads from /dev/urandom, allocates GPU memory, and copies the entropy to the device for use in cryptographic operations. The key point is that this function is designed to be replaceable.
Default entropy function in NVIDIA’s cuPQC, reading from /dev/urandom. This function is designed to be easily replaced with an alternative entropy source such as a hardware RNG.
Why Modular Entropy Design Matters
This design choice has two distinct advantages.
Stronger security through layered unpredictability. Adding an independent entropy source creates an additional layer of defense against attackers who learn how to manipulate or exploit a specific type of generator. If one component becomes compromised or weakened, the presence of independent entropy inputs limits the damage.
Flexibility to match the application’s requirements. Different systems prioritize different things: throughput, reproducibility, confidence in the unpredictability of the source, energy efficiency, cost, or ease of deployment. A low-power embedded device might work best with a lightweight hardware noise generator. A high-security environment might rely on a specialized entropy device. Large-scale server infrastructure might combine multiple sources to balance performance and security.
Modularity Is Not Enough: You Also Need Verification
Having a modular entropy design is a smart engineering practice. Choosing a strong entropy source is even better. But there is a deeper question that most cryptographic implementations still leave unanswered: how do you know your entropy is actually good, right now, in production?
Most systems treat entropy as a black box. They trust the operating system’s entropy pool. They trust the hardware noise source. They trust that the CSPRNG was properly seeded. But trust without evidence is assumption, and assumptions are where security breaks down. Static certifications and periodic audits tell you that a system was designed correctly at a point in time. They do not tell you whether the entropy source feeding your keys is properly working at the moment those keys are generated.
The real standard should be higher: entropy sources that are verified locally, at the very source itself, continuously. That means generating entropy next to where it is consumed, not pulling it from a pool where quality cannot be confirmed. It means measuring entropy in real time against health checks and online, tailored criteria, not relying on a certificate that was issued months ago. And it means making sure that quality data is visible to operators and governance layers, so security teams can detect degradation before it propagates into cryptographic failures.
This is the same logic that drives Zero Trust in network security: never assume, always verify. The same thinking shall apply to entropy sources and randomness generators. If entropy cannot be verified at its source, it cannot be trusted, no matter how sophisticated the technology behind it looks, or the quality claims made on its behalf.
Where Quside Fits In
Quside’s locally-verifiable quantum random number generators are built for exactly this kind of integration, and locally verifiable entropy (LoVE®) is at the core of how they work.
Our Garnet Series PCIe modules deliver up to 1 Gb/s of quantum-sourced randomness with embedded metrology and real-time min-entropy estimates. Beyond meeting the current RNG standards, such as NIST SP800-90B, for entropy generation, Quside’s entropy sources go beyond just generating compliant randomness. It is about proving its origin and quality, locally and continuously. Every Quside QRNG monitors its own entropy output and provides on-the-fly min-entropy bounds that operators can observe and act on.
This makes Quside QRNGs ideal for reseeding CSPRNGs in high-performance crpytographic environments, including those built on libraries like cuPQC. Instead of relying on /dev/urandom alone, developers can point their entropy function to a Quside device and gain hardware-verified, observable randomness at the speed their application demands.
In the RNG maturity model developed by Deloitte in collaboration with Quside, this approach represents Level 4: quantified uncertainty with real-time observability and continuous quality assessment. Level 4 RNGs do not just generate randomness; they prove it. They measure entropy in real time, model the unpredictability of the physical process, and flag anomalies immediately.
Most cryptographic systems today operate at Level 2 (opportunistic software-based entropy) or Level 3 (engineered hardware TRNGs without runtime verification). The gap between Level 3 and Level 4 is the gap between trust by reputation and trust by evidence. Quside calls this principle LOVE, Locally Verified Entropy, and it is the standard our products are built to meet.
Key Takeaways
- Cryptographic security depends equally on strong algorithms and unpredictable randomness. Unreliable randomness has caused some of the most damaging security breaches in recent history, and every cryptographic algorithm (including post-quantum ones) is equally vulnerable to it.
- Modern systems combine PRNGs (for speed and reproducibility) with external entropy sources (for genuine unpredictability). This hybrid approach is the standard in PQC environments.
- Well-designed cryptographic libraries make it easy to swap in your entropy source of choice. As an example, NVIDIA’s cuPQC reads from /dev/urandom by default, but replacing it with another randomness generator takes minimal code changes.
- Generating good entropy is necessary but not sufficient. If you cannot verify that your enntropy is trustworthy, at the source, in real time, you are building on an assumption, not a foundation.
- Among verifiable sources, Quside’s QRNGs deliver the highest assurance of entropy quality, with real-time locally verifiable entropy monitoring, NIST SP800-90B certification, and throughput designed for integration into even the most demanding cryptographic workloads.
What to Do Next
If your systems generate cryptographic keys, session tokens, or digital signatures, the quality and verifiability of your randomness directly determine the strength of your security. Start by auditing your entropy sources. Ask the questions that matter: where is your entropy generated? How is its quality verified? Who can observe and validate that verification?
If you cannot answer those questions, you have a blind spot, and it is at the foundation of your security stack.
Get in touch to learn how Quside’s QRNG technology can strengthen your cryptographic infrastructure with locally verified entropy.
