Securing the Unpredictable: Random Number Technology In Web3 Games

Every player wants to beat the odds. But few actually do. Have you ever wondered how slot machines in casinos can be beaten? Or how to win in Web3 games that rely on chance? In this article, we explore the role randomness plays in Web3, whether it’s possible to beat random number generators (RNGs), and how to go about cracking RNGs.

In the vast expanse of Web3, randomness isn’t just a happenstance. It’s a pivotal element in various scenarios, influencing outcomes that range from gaming experiences to critical blockchain functionalities.

Where Randomness Plays a Role in Web3

In Web3, randomness operates as a fundamental force influencing various aspects. One prominent domain is within the gaming sphere, where the chance of unveiling rare in-game items at specific locations is contingent upon the mechanisms of random number generators.

These random encounters often dictate the rewarding experience of players upon defeating opponents.

Lottery draws a quintessential example of randomness, requiring an unequivocally transparent and verifiably random process, especially within the Web3 ecosystem, where fairness and openness are paramount. If not, the lottery draw may potentially favor only specific groups or players.

Additionally, the random selection of block producers through RNGs is crucial in certain layer-one blockchains. If this selection process lacks verifiable randomness and exhibits discernible patterns, block producers might exploit this to their advantage, strategically producing blocks that serve their interests. Such actions compromise the network’s security, underlining the critical role of truly random and unbiased selection mechanisms in Web3 blockchain operations.

While randomness plays such an important role in Web3 gaming and the Autonomous World, it’s crucial to differentiate between pseudo-random number generators (PRNGs), which use algorithms to produce sequences of random numbers, and true random number generators (TRNGs) that use physical processes for higher security.

Why Predicting Random Numbers Is Possible

Predicting numbers generated by pseudo-random number generators (PRNGs) might be feasible due to specific vulnerabilities and patterns inherent in their algorithms. The generation process of PRNGs follows patterns that can be exploited if the initial seed (starting point) and state (current condition) of the generator are known.

Let’s dive deeper into what a “seed” and “state” mean in this context. The seed, a critical input, initiates the random number generation process. It can be something as simple as the current time. The state refers to the current condition of the generator, which evolves with each number generated. Knowing the initial state allows for the prediction of subsequent numbers, presenting a significant security risk in scenarios where unpredictability is crucial.

How RNG Works

Moreover, the use of centralized sources for RNGs introduces additional vulnerabilities. Centralized systems become single points of failure, making them prime targets for malicious attacks. Historical incidents have demonstrated that brute force attacks can compromise these systems.

Attackers can inundate the central server with numerous random guesses and make incremental adjustments to fine-tune their predictions, ultimately increasing their accuracy in guessing the next random number generated.

These vulnerabilities emphasize the need for more secure and decentralized RNG solutions in various applications, especially within the Web3 landscape, where data integrity and randomness are of utmost importance.

Exploiting RNG Vulnerabilities: Theoretical Methods

Finding the Seed Through Hypothesis Testing

The initial step in exploiting an RNG is to uncover the seed, which is the starting point for number generation. This process involves formulating and testing various hypotheses about potential seed sources. For example, if the hypothesis for the seed is the time of RNG operation, then generating numbers at consistent times and analyzing the outputs for patterns could confirm or refute this hypothesis. Advanced statistical techniques can be applied to detect subtle patterns that might not be immediately apparent, thereby increasing the accuracy of seed prediction.

State Analysis and Reverse Engineering

Beyond identifying the seed, understanding the state transitions within the RNG algorithm is crucial. By analyzing how the state changes with each number generated, it’s possible to reverse-engineer the RNG algorithm. This method involves sophisticated mathematical and computational techniques, including algorithmic analysis and potentially cryptographic reverse engineering, depending on the complexity of the RNG.

Leveraging Machine Learning for Pattern Recognition

Machine learning, a subset of artificial intelligence (AI), excels at deciphering complex patterns in large datasets. This capability extends beyond simple pattern recognition, enabling the algorithm to learn and adapt without explicit programming. A prominent example of this is Google’s AlphaGo. This AI system, trained on the board game Go, mastered the game by learning from vast quantities of game data, eventually outperforming world champions by anticipating their moves.

This principle is directly applicable to analyzing RNG outputs. Machine learning algorithms can process extensive datasets of RNG-generated numbers to detect underlying patterns, including subtle irregularities and algorithmic resets that might not be apparent through traditional analysis methods.

By training on a sufficiently large dataset, machine learning models can identify the cyclical nature of these RNGs, including their reset points and output patterns. Understanding these patterns allows for the prediction, and potentially the manipulation, of future RNG outputs. This capability is particularly crucial when RNGs are used in environments where predictability can lead to security vulnerabilities or unfair advantages, such as in gaming or cryptographic applications.

Photo by Miguel Á. Padriñán: https://www.pexels.com/photo/dominoes-585293/

Cryptographic Analysis

Cryptographic analysis of RNGs is a multi-faceted approach that involves scrutinizing every aspect of their cryptographic design and implementation. This process starts with a thorough examination of the entropy sources, which are the raw materials for randomness. Entropy quality, its collection method, and the rate at which it’s gathered are crucial factors; low-quality entropy or predictable collection methods can weaken the RNG’s output.

Next, the cryptographic algorithm itself is examined for resistance to known attacks. This includes statistical analysis to detect any biases or patterns that emerge over time, and testing the algorithm against known cryptanalytic techniques like differential cryptanalysis or linear cryptanalysis. The goal here is to ensure that the algorithm doesn’t produce outputs that are disproportionately influenced by certain bits of the seed or state.

Moreover, the implementation of the RNG in software or hardware can introduce additional vulnerabilities. This part of the analysis involves code audits to find bugs or oversights that might compromise randomness. Flaws like insufficient mixing of entropy, predictable incrementation of state variables, or improper seeding can be exploited by attackers. Cryptographic analysis also extends to the operational environment of the RNG, checking for side-channel vulnerabilities like timing attacks or power analysis, where an attacker could infer internal states by observing indirect characteristics of the system.

Verifiably Random and Tamper-Proof RNGs

The process begins when the Randcast Adapter Contract receives a randomness request from a DApp or Web3 game. It then initiates a BLS-TSS (Boneh-Lynn-Shacham Threshold Signature Scheme) task, sending an on-chain event to the ARPA Network, which consists of multiple nodes capable of performing these tasks.

The network acknowledges this event and completes the task, subsequently returning the signature as the random seed to the Randcast Adapter Contract. This seed is then transformed into the desired randomness type—be it a dice roll, shuffled array, or any other form—and is utilized in the subsequent DApp or Web3 game logic.

How Randcast is different

Randcast’s approach is particularly vital in scenarios where reliable randomness is essential. In the deterministic environment of blockchain, where smart contract outcomes hinge on input, generating a random number can be easily manipulated. For example, using a block hash or timestamp as a randomness source could be exploited by miners for their benefit. Randcast circumvents this issue by generating random numbers through a decentralized network, ensuring transparency and verifiability in the process.

A crucial aspect of Randcast’s design is its resilience against tampering. Traditional single-entity randomness generation poses the risk of manipulation. Randcast circumvents this problem by utilizing the collective capabilities of the ARPA Network. Through BLS Threshold signature tasks, it ensures that no individual node can influence the final randomness outcome. This decentralized approach not only enhances security but also upholds the integrity and impartiality of the randomness generated, making Randcast an indispensable tool in the Web3 developer’s arsenal.

Reference links

• https://www.roulettephysics.com/how-to-beat-rng-roulette-slot-machines-other-casino-games/
• https://research.nccgroup.com/2021/10/15/cracking-random-number-generators-using-machine-learning-part-1-xorshift128/
• https://lemire.me/blog/2017/08/22/cracking-random-number-generators-xoroshiro128/
• https://crypto.stackexchange.com/questions/24646/can-machine-learning-analyze-random-number-generator
• https://www.reddit.com/r/askscience/comments/56r419/is_it_possible_to_find_the_algorithm_for_a_random/?rdt=57618
• https://lcamtuf.coredump.cx/oldtcp/tcpseq/print.html#tcpseq

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