Security and Accuracy: Positive Technologies Audits the National Lottery’s Number Generator

No Critical Vulnerabilities Found

The audit showed that the number sequences produced by the system are fully random and unpredictable. No critical vulnerabilities were identified in the generation application. As a result, the generator meets strict security standards and is being used correctly within the lottery draw mechanism. These findings carry significant weight.

For Russian citizens, they strengthen trust in state-run lotteries by guaranteeing the integrity of the draws. For the government, the audit demonstrates the use of independent IT assessments in critical public processes and supports greater transparency in government services. For the information security sector, this case sets an important precedent: applying high security standards to nontraditional use cases such as random number generation in lottery systems that rely on cryptographic sources. It underscores the national scale of the project and its role in building trust in Russia’s digital mechanisms.

Domestic Development and Broader Potential

Although the project’s direct export value is limited by its focus on a domestic government system, the experience of independently verifying random number generators (RNGs) may be in demand in international cybersecurity markets. This is especially relevant for financial services, online gaming, and digital public services, where fairness and unpredictability in data generation are mission-critical.

Within Russia, the role of independent cybersecurity audits for government digital services is expected to grow. The use of cryptographically secure RNG solutions is an accelerating trend and is likely to become mandatory in public systems, given their close ties to cryptography. Past attacks on RNGs and documented lottery fraud cases highlight the critical need for rigorous verification of these algorithms to prevent future threats.

Random number generators are widely used across industries – from finance and telecom to scientific research and gaming. However, they can become a weak link in security if number generation does not meet strict standards. Violations create real threats. For example, predictability in cryptographic key material used for payment encryption can allow attackers to compromise transactions. If patterns are found in session identifiers or authentication tokens, an attacker may gain unauthorized access to services, including corporate systems. Our experts conduct comprehensive security testing of such systems, analyzing every component – from the generator’s hardware and software foundations to how they interact and how data is ultimately delivered to the client

Pavel Petrov

Senior Client Manager, Positive Technologies

Why Correct RNG Operation Matters

Globally, there have been high-profile cases of lottery fraud. One involved Eddie Tipton, the former security director of the Multi-State Lottery Association, who created code that allowed winning numbers to be predicted. The scheme operated from 2005 but drew law enforcement attention only in 2012, when fraudsters repeatedly attempted to claim a $16.5 million prize from a Hot Lotto ticket dated December 29, 2010. In 2015, Tipton pleaded guilty to fraud charges and was sentenced to 10 years in prison.

A random number generator (RNG) is a complex algorithm that continuously produces sequences of numbers that are pseudorandom. Fairness requirements for RNGs include verification of correct algorithmic behavior during software licensing. To do this, developers submit their products for audits to independent laboratories such as eCOGRA, iTech Labs, and GLI.

Some U.S. state lotteries, including Arizona, use hardware-based RNGs to generate number combinations, which raises questions about potential vulnerabilities. To reduce risk, RNG providers focus on hardening security controls and restricting access to these systems.

Vulnerabilities in RNGs and attacks against them represent a class of weaknesses that can undermine system security if outputs become predictable. These vulnerabilities include insufficient entropy, leakage of internal state, and predictable output sequences. Assessing RNG quality requires advanced statistical testing to ensure correct operation in critical applications. Common test suites include SmallCrush, Crush, and BigCrush.

Toward Transparent Digital RNG Algorithms

The project demonstrates the practical application of information security audits to digital components of government mechanisms such as lotteries. This approach strengthens public trust in draw systems and reduces reputational risks for operators, making independent RNG verification a core quality standard in the digital economy.

Over the next three to five years, requirements for transparency in digital RNG algorithms are expected to increase, particularly in the public sector and financial services. Regular independent cybersecurity audits are likely to become standard practice, as RNG predictability can introduce serious security risks. Similar reviews may eventually be embedded in regulatory requirements across all sectors where the integrity of data generation is critical, including gaming platforms, financial transactions, and digital signatures.