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What happened: four payments, more than $2,000 torched on fees
Why this is cropping up now: vibe coding meets fee fields that do exactly what you tell them
Two failure modes show up repeatedly in XRPL automation:
- Unit confusion: XRPL transaction fees are specified in drops, not XRP. If a script treats a drop value as an XRP value (or vice versa), the "Fee" field can be set to something absurdly high.
- Runaway automation: AI-generated scripts can loop, retry aggressively, or spam endpoints, which can lead developers to overcompensate by manually bumping fees, or to accidentally sign transactions with whatever fee the code last computed.
Vet also warned that poorly controlled scripts can generate complex request patterns that overload public infrastructure and contribute to spammy behaviour, even when the author thinks they are just "testing something quickly." [4]
What it means for XRPL users: the network is not "taking" your XRP, your software is offering it
XRPL fees are burned, not paid to validators, so once a transaction with a huge fee lands on-ledger, that value is effectively gone. The ledger is doing what it is designed to do: execute the transaction you signed with the parameters you provided.
That nuance matters because it reframes the risk:
- This is not a market risk problem, it is an operational security problem.
- The biggest danger zone is custom code, bots, and integrations that sign transactions programmatically.
- Public RPC usage, rate limits, and retries can turn a small logic mistake into repeated losses very quickly.
How to not get clipped: practical guardrails before you run AI-written XRPL code
If you are shipping scripts that sign XRPL transactions, treat fee safety as a hard requirement:
- Set a fee cap in your signing logic (refuse to sign above a conservative threshold).
- Simulate and preflight: calculate expected fee, verify units (drops vs XRP), and log the exact "Fee" field before signing.
- Use Testnet/Devnet first, especially for new automation.
- Add circuit breakers: stop after N failures, stop after N transactions per minute, stop if fees deviate from baseline.
- Prefer trusted libraries and wallets that implement sensible defaults, rather than rolling your own transaction builder from an LLM prompt.
What to watch next
- More flagged transactions: whether additional high-fee burns appear from similar bot patterns.
- Wallet UX responses: fee limit warnings or "are you sure?" prompts for extreme fees.
- Infrastructure strain: reports of public node throttling or spikes in spammy traffic tied to AI bot scripts.
- Community guidance: best-practice templates for safe fee calculation and signing flows on XRPL.




