Random number generation
Generating random numbers is a common task in many projects. While you may have seen the random() function in FunC documentation, note that its result can be easily predicted unless you use additional techniques.
How can someone predict a random number?
Computers struggle to generate truly random information because they strictly follow user instructions. To address this, developers have created methods for generating pseudo-random numbers.
These algorithms typically require a seed value to produce a sequence of pseudo-random numbers. You will always get the same result if you run the same program with the same seed multiple times. In TON, the seed varies for each block.
To predict the result of the random() function in a smart contract, one would need to know the current seed of the block, which is impossible unless you are a validator.
There are multiple approaches to generate random values, each offering different trade-offs between speed, security, and decentralization guarantees.
Below we outline three fundamental approaches:
Approach 1: randomize_lt
Mechanism: Generates randomness using block logical time (lt) and blockchain entropy.
Security model:
- ✅ Safe against user manipulation
- ❌ Vulnerable to colluding validators (could theoretically predict/influence values)
Speed: Fast (single-block operation)
Use cases:
- Non-critical applications, for example gaming & NFTs
- Scenarios where validator trust is assumed
Approach 2: Block skipping
Mechanism: Uses entropy from skipped blocks in blockchain history.
Security model:
- ✅ Resistant to user manipulation
- ⚠️ Not fully secure against determined validators (may influence block inclusion timing)
Speed: Slow (requires multiple blocks to finalize)
Use cases:
- Medium-stakes applications, for example lottery systems
- Scenarios with partial trust in validator set
Approach 3: Commit-reveal scheme
Mechanism:
- Commit phase: Participants submit hashed secrets
- Reveal phase: Secrets are disclosed and combined to generate final randomness
Security model:
- ✅ Cryptographically secure when properly implemented
- ✅ Resilient to both users and validators
- ⚠️ Requires protocol-level verification of commitments
Speed: Very slow (multi-phase, multi-block process)
Use cases:
- High-value applications, for example decentralized auctions
- Systems requiring Byzantine fault tolerance
Key considerations
| Factor | randomize_lt | Block skipping | Commit-reveal |
|---|---|---|---|
| Speed | Fast | Moderate | Slow |
| User resistance | High | High | Highest |
| Validator resistance | Low | Medium | Highest |
| Implementation complexity | Low | Medium | High |
No method is universally perfect – choose based on:
- Value-at-risk in your application
- Required time-to-finality
- Trust assumptions about validators
Always audit implementations through formal verification where possible.