> For the complete documentation index, see [llms.txt](https://docs.urani.trade/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.urani.trade/urani-protocol/technical-considerations/multidimensional-market/on-solana/understanding-slippage.md).

# Understanding Slippage

## Slippage and MEV

Setting a low slippage tolerance is the simplest defense against toxic-MEV. Since searchers exploit slippage for price manipulation, transactions with lower slippage tolerance could provide less opportunity for toxic-MEV exploitation from trades.

Additionally, slippage modeling generally incorporates statistics into a smart order routing algorithm to deliver the optimal trade route.

Nevertheless, these solutions are incomplete. Even with optimal slippage, MEV bots may still derive value from your trades.

***

## On Solana

In [**Solana's Token Swap Program**](https://github.com/solana-labs/solana-program-library/tree/master/token-swap), a generic swap involves the input `amount_in`, which represents the amount of token the user wants to trade, and `minimum_amount_out`, which denotes the minimum amount of output tokens necessary to avoid excessive slippage:

```rust
/// Swap instruction data
pub struct Swap {
    /// SOURCE amount to transfer, output to DESTINATION is based on the exchange rate
    pub amount_in: u64,
    /// Minimum amount of DESTINATION token to output, prevents excessive slippage
    pub minimum_amount_out: u64,
}
```

The minimum amount of output tokens is connected to the actual number of output tokens by the slippage, ensuring that:

$$
\min(N\_{out}^A) = N\_{out}^A - S^A \times N\_{out}^A
$$

The slippage can be derived as follows:

$$
S^A = \frac{N\_{out}^A - \min(N\_{out}^A)}{N\_{out}^A} %
$$