A linear congruential generator is a pseudo-random number generator that produces each new value from the previous one using a linear equation and modular arithmetic. It is simple and fast, but its output can be predictable if the parameters or seed are known, so it is unsuitable for security-sensitive randomness.
How a Linear Congruential Generator Works
A linear congruential generator, or LCG, is one of the simplest pseudo-random number generator designs. Each output value is computed from the previous state by applying a linear recurrence and reducing the result modulo a fixed number. That design makes LCGs easy to implement, fast to run, and attractive in performance-sensitive code.
The simplicity is also the reason they are rarely suitable when unpredictability matters. An LCG has a small internal state compared with modern cryptographic generators, and if the parameters are weak or the seed is exposed, the sequence can be reconstructed or predicted. For that reason, the term is usually discussed as a basic randomness algorithm rather than a security primitive.
Why LCG Output Can Be Predictable
LCGs are deterministic systems: given the same seed and parameters, they always produce the same sequence. That is useful for testing, simulations, and repeatable workloads, but it means the generator does not create true randomness. If an attacker learns enough consecutive outputs, they may be able to infer the internal state and predict future values.
Predictability is especially problematic when the generated values are used for session identifiers, password reset tokens, salts, API keys, or other security-sensitive material. In those contexts, output quality is not just a mathematical property, it becomes part of access control, confidentiality, and trust. A fast generator that is easy to guess is operationally convenient but security-poor.
Where Linear Congruential Generators Are Used
LCGs still appear in non-security contexts where speed, portability, and reproducibility are more important than adversarial resistance. Common examples include simple simulations, game mechanics, sampling utilities, and legacy software that predates modern randomness libraries. Their behaviour is easy to reproduce across environments, which is sometimes a feature rather than a flaw.
The problem is not that LCGs are inherently broken for every purpose, but that their suitability depends on the use case. A generator that is acceptable for modelling or shuffling may be a poor choice for any workflow that depends on secrecy, unpredictability, or resistance to manipulation. In modern systems, security-sensitive randomness should come from a cryptographically secure source instead.
LCGs in Modern Security Design
In current security architecture, LCGs are best treated as a legacy or educational algorithm, not as a source of trust for authentication, token generation, key material, or defensive randomness. If an application uses an LCG anywhere near security boundaries, the review question is not only whether it is “random enough”, but whether deterministic output could be observed, replayed, correlated, or brute-forced.
That distinction matters because predictable randomness often creates downstream failures that are hard to spot at first. Weak randomness can make identifiers guessable, weaken protocol behaviour, and reduce the effective entropy of security controls that assume unique or unforgeable values. In practice, the generator’s role in the system matters more than the algorithm’s elegance.
Risk and Threat Considerations
LCGs become risky when developers use them for values that attackers can observe or influence. The main danger is not random failure, but deterministic predictability: if the state or parameters are recoverable, an attacker can infer future outputs and turn a supposedly random mechanism into a reliable attack surface.
Failure mechanism: The generator repeats a mathematically predictable sequence, so exposed outputs can reveal enough information to reconstruct the state or narrow the possible next values. That can undermine tokens, identifiers, shuffles, and other controls that depend on unpredictability.
Impact: Predictable values can lead to session hijacking, token forgery, enumeration, weaker access controls, and compromise of any workflow that assumes randomness provides security.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
NIST SP 800-53 Rev 5, CIS Controls v8, OWASP ASVS and NIST CSF 2.0 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST SP 800-53 Rev 5 | IA-5 — Authenticator Management | LCG output used in security tokens or identifiers can weaken authenticator lifecycle and uniqueness. |
| SC-12 — Cryptographic Key Establishment and Management | Weak randomness can undermine security material and any process that depends on unpredictable values. | |
| Recommendation — Use IA-5 to manage authenticator generation and rotation with secure randomness sources. Use SC-12 to ensure cryptographic material and related entropy sources are generated with approved methods. | ||
| CIS Controls v8 | CIS-16 — Application Software Security | Applications should avoid insecure randomness when implementing security-sensitive functions. |
| Recommendation — Use CIS-16 to review software for insecure random number use in security-critical code paths. | ||
| OWASP ASVS | V11 — Cryptography | Predictable generators are unsuitable where applications require secure randomness for cryptographic purposes. |
| Recommendation — Apply V11 to require cryptographically secure randomness for sensitive application functions. | ||
| NIST CSF 2.0 | PR.DS-10 — Confidentiality Protection | Predictable outputs can expose values meant to remain secret or unguessable. |
| Recommendation — Protect data values whose secrecy or unpredictability affects security outcomes. | ||
Practitioner Guidance
Why practitioners should care: An LCG is fine when reproducibility is the goal, but it is the wrong tool when unpredictability is a security requirement. The key judgement is whether the output ever crosses from simulation into trust decisions, identity material, or security-sensitive state.
What to watch for: Legacy code, home-grown randomness, or “quick” utility functions often hide LCGs in places where teams assume randomness is already handled. Review any use of pseudo-random values that affect authentication, authorization, cryptographic material, or externally visible identifiers.
Practitioner takeaway: Use an LCG only for non-security purposes where deterministic behaviour is acceptable, and replace it with a cryptographically secure generator wherever the output can affect security outcomes.
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Reviewed and updated by the NHIMG editorial team on November 3, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org