Why Token Approvals Break DeFi (and How I Learned to Simulate Transactions First)

Whoa! My first sentence is a gut-punch. Seriously? Token approvals are tiny and silent until they aren’t. At first I shrugged—approvals felt like plumbing: boring, behind-the-scenes, something you set and forget. But after a string of near-misses on testnets and a real wallet scare that made my stomach drop, I started treating approvals like hot coals—careful, deliberate, and always checked.

Here’s the thing. Approvals let smart contracts move your ERC-20 tokens. That’s the whole point. Yet that same permission model opens doors that are easy to leave ajar. My instinct said “set it to zero after use,” but reality is messier—contracts expect allowances, UX nudges users to grant infinite approvals, and gas costs make revoking seem annoying. So you end up with a bunch of long-lived permissions floating around.

On one hand this is convenient for DEX aggregators and gas-optimized UX. On the other, it’s a huge attack surface when beetle-sized bugs or malicious contracts slip in. Initially I thought revokes were a silver bullet, but then realized many DeFi flows require non-zero allowances to function, and frequent revokes break composability or cost money. Actually, wait—let me rephrase that: revoking is good hygiene but not a panacea, and revoking too aggressively can backfire when protocols require repeated approvals for multi-step flows.

Let me break down the most common approval patterns you’ll see and why each matters for simulation. Short version: approve(), increaseAllowance(), decreaseAllowance(), permit() (EIP-2612). Each behaves slightly different when simulated against a contract. The differences matter when you’re anticipating reverts, slippage, or inter-contract calls that sweep balances. Also, approvals are not atomic with other ops unless the contract explicitly batches them, so simulating the entire flow is crucial.

Why simulate approvals before you hit send

Okay, so check this out—if you’re building or interacting with complex DeFi flows, simulation is the difference between a close call and a real loss. Simulating saves you from wasted gas on failed transactions, and it helps you see intended state changes without exposing funds. Hmm… sounds obvious, but people skip it. I get it—UX is impatient and the network demand is high—but simulating isn’t optional for advanced users.

Simulations let you answer questions like: will the contract call transferFrom succeed? Will the token’s transfer hook revert? Could an intermediate contract drain allowance unexpectedly? On a technical level, you use eth_call against the target contract on a forked state or use tools that replay the tx on a snapshot. That reveals reverts, return values, and token side-effects without broadcasting anything. For many DeFi protocols, the simulation must include the approval and the following operation in the same context to be meaningful; otherwise you miss race conditions.

Practical tip: always simulate with the exact nonce, gas price, and calldata you plan to send. Small changes in gas or chain state can change outcomes—really. In one instance I simulated a swap that would have looked fine on a block snapshot, but mempool re-ordering and a state-changing front-run would have made the on-chain result different. So simulation is necessary but not sufficient; you need context-aware checks too.

Here’s what bugs me about most tutorials: they gloss over token quirks. Some tokens return boolean on transfer; others revert on failure; a few just emit events and lie. That inconsistency breaks naïve simulations. When you simulate, make sure your tooling interprets token behavior correctly—otherwise you’ll have false positives. I’m biased toward tooling that does low-level traces, not just high-level success/fail, because traces show internal calls and allowance transfers.

Build flows you can simulate atomically. For example, instead of approving and then calling a swap in two separate on-chain transactions, bundle them where possible, or use a contract that performs both steps in one. That reduces window-of-exploit. On one project I added a small relay contract to batch approve+swap, and that cut my exposure window to nearly zero—cost a few more lines of solidity, saved my users headaches.

There are grader questions you should always run: will the spender end up with more allowance than expected? Are approvals being forwarded to delegates? Is there any fallback function that could accept tokens silently? Simulate with edge-case balances: very low balances, maximal allowances, and non-standard token decimals. These tests catch weirdness early.

Tools and approaches that actually work

Start local if you can. Fork mainnet and run your intended tx sequence with the exact state and block numbers you care about. This is the most faithful reproduction. Why? Because you can inspect storage, run traces, and modify mempool context. But it’s heavier and sometimes slow. Still, it’s the gold standard for high-risk operations.

Next, use RPC simulation via providers that support dry-run or trace methods. Some tooling layers add helpful abstractions—transaction simulation UIs, replay engines, and mempool-viewing tools. These can save time and surface complexities. I’m not going to list every tool here, but if you want a practical wallet that surfaces simulation and safety features, try rabby—it shows approvals, can simulate flows, and gives immediate UX cues before you sign.

Workflows I recommend: fork + unit tests for contract devs; live-simulate with a snapshot for complex interactions; and use a secure wallet that warns you about large or infinite approvals for end-users. Also, whenever possible, prefer permit() signatures—EIP-2612 reduces on-chain approve() steps by letting users sign an allowance off-chain, which lowers attack surface and saves gas. Though, permit has its own caveats: replay protection, domain separators, and implementation bugs can all bite you.

On one hand, infinite approvals are convenient. On the other hand, they concentrate risk: one compromised spender drains everything. I used to tell people “approve once, forget it” because of UX friction. Now I say: “approve minimally, simulate, and rotate.” That sounds like extra work, but with automated scripts it’s manageable.