1. Historical Review: What Really Happened?

In the early morning of August 26, XPL experienced a dramatic “roller coaster” episode on Hyperliquid that lasted just a few minutes:

05:36: Massive buy orders swept out the order book, with single trades ranging from tens of thousands up to several hundred thousand US dollars, sharply driving up the XPL price.

05:36–05:55: Internal matching dominated price discovery, causing the marked price to surge far more than CEX references, which led many short positions to cross below maintenance margin. The system triggered liquidations: liquidation orders directly hit the order book, forming a positive feedback loop of “sweep book → liquidation → further sweeping,” and relentlessly pushing the XPL price upward.

05:55: The price shot up to its peak, climbing nearly +200% in less than 15 minutes. Whales took profits, earning over $16 million in a single minute, while some short accounts were liquidated for millions of dollars in only a few minutes.

05:56: Market depth recovered, the price quickly retreated, and the XPL perpetual market returned to “normal”—but a whole cohort of short traders had already been wiped out. Almost simultaneously, ETH perpetuals on the Lighter platform experienced a flash crash, briefly plunging to $5,100.

This highlights a point: this was not an isolated platform issue, but a concentrated exposure of structural risk across DeFi perpetual contracts.

2. What Were the Consequences?

Whales made a fortune, shorts suffered devastating losses—even low-leverage hedgers got hit.

Many assumed that 1x leverage hedging was “risk-free.” But in this event, even 1x leveraged shorts with substantial collateral were liquidated during the flash move, losing millions of dollars. This led some users to say: “I’ll never touch these isolated markets again.” However, the truth is much more complicated.

3. The Core Problem: Structural Flaws in the Order Book Model

After the XPL incident, many discussions focused on “single oracle dependence” or the “lack of position limits.” But these miss the real core of the issue.

Perpetual protocols can be built in several ways:

Orderbook (order book-driven)

Peer-to-Pool (liquidity pool as counterparty)

And hybrid AMM/order book models

The issue this time stemmed from the order book model, whose structural flaws include:

Effective Depth & Token Distribution

1. An order book may look deep, but real effective depth depends on how tokens are distributed.
2. If tokens are concentrated among a few large players, even a small price nudge can trigger cascading effects.

Price Anchoring Relies on Internal Transactions

1. In thin markets, order book trades directly dictate the mark price.
2. Even with oracles, if spot anchors are not robust, this reliance is a weakness.

Liquidations Create a Positive Feedback Loop with the Order Book

1. Liquidation orders must enter the order book → push prices further → cause more liquidations.
2. In illiquid markets, this results in an “inevitable stampede”—not an isolated accident.

As for “position caps for individual users,” these do little in practice, since positions can be split across multiple sub-accounts or wallets. The risk remains systemic. Flash moves like these aren’t a result of malicious manipulation—they’re baked into the order book model whenever liquidity is thin.

4. At the Core: What Are Perpetual Contracts Actually Solving?

When you say, “I’m bullish on ETH,” what’s actually happening beneath the surface?

- If it’s spot trading, you spend 1,000 USDT to buy ETH. If ETH goes up, you profit. If it goes down, you lose.

- If it’s a perpetual contract, you post 1,000 USDT as margin to open a 10x long, controlling a $10,000 position. That amplifies both reward and risk.

This leads to two central questions:

Where does the money come from?

Your gains necessarily come from counterparties (shorts) or the LP-supplied pool.

Who sets the price?

Traditional markets: The order book directly reflects trade activity—more buying drives the price up, in line with market trends.

On-chain perpetuals: Most protocols (such as GMX) don’t run their own matching engines, but rely on CEX oracle prices.

5. The Oracle Model’s Problems

Oracles generally relay prices from CEX spot trades, which means on-chain volume doesn’t feed back into price discovery.

While oracle delays exist, the real issue is:

You can open a $100 million on-chain position, but there’s no matching volume on the spot market.

In other words, on-chain trading demand can’t influence prices, letting systemic risk “accumulate” internally.

That’s the opposite of the order book model: There, price feedback is too quick and easily manipulated; here, oracle-based pricing lags, deferring risk and creating room for hidden build-up.

6. Basis and Funding Rate

This raises another crucial question: how is the basis (spread between spot and perpetual) corrected?

In traditional markets, if there are far more longs than shorts, contract prices exceed spot prices.

Perpetuals use funding rates to balance things:

If longs dominate → funding rate turns positive, longs pay shorts;

If shorts dominate → funding rate turns negative, shorts pay longs.

In theory, this should bring contract prices back into line with spot prices.

But on-chain perps are trickier: If the spot market lacks depth, even sky-high funding rates may not close the basis. For thinly-traded assets, on-chain contracts can diverge from spot for long periods, becoming almost a parallel “shadow market.”

7. The Mirage of On-Chain Depth

Many believe that only obscure tokens can be manipulated, and blue-chip assets are safe. In reality, true on-chain spot depth is far lower than people expect.

Consider the top three tokens in major ecosystems:

- On Arbitrum, major tokens other than ETH often have order book depth of just a few million dollars within a 0.5% price band.

- Even on leading DEXes like Uniswap, tokens such as UNI can’t absorb tens of millions of dollars in sudden impact trades.

What does this mean?

Effective depth is often much lower than posted liquidity. When holdings are concentrated, true market resilience is weaker still.

In this context, price manipulation is not hard. Even top-three tokens can be pushed up or down dramatically in extreme conditions.

Put simply: structural risk in on-chain perps is not a “niche exception”—it’s business as usual across the ecosystem.

8. The Direction of Next-Generation Protocol Design

The XPL incident makes clear: the issue isn’t a bug in a single platform, but an underlying tension between order book designs and on-chain liquidity constraints.

So, what should “next-gen Perp protocols” address? At least three promising directions:

1. Proactive risk management: Before each open, swap, liquidity change, or position adjustment, simulate post-trade market health. If risk exceeds thresholds, limit or adjust proactively—instead of waiting to liquidate after a maintenance margin breach.
2. Spot Pool Synchronization: Current on-chain models either feed back too fast (order book) or too slow (oracle). A better path links contract positions with spot pools, using spot market depth to buffer or diffuse risk. This avoids both slow risk accumulation and sudden cascades.

3. LP-Centric Protection: In any model, LPs are the most vulnerable. Next-generation protocols must embed LP risk controls directly into their logic, keeping LP risk transparent and manageable—rather than leaving LPs as passive last-resort backstops.

4. Practical Exploration & New Opportunities

Finding the right direction is easier than making it work—but new attempts are emerging:

Proactive risk management: Simulate market health before executing any trade, screening out risk in advance.

Integrating contracts with spot pools: Tie positions to spot liquidity for real-time feedback and to avoid risk build-up or abrupt cascades.

LP-Centric Protection: Bake LP risk management into the protocol layer, not as a reactive safety net.

At the same time, let’s not overlook a broader market reality:

The perpetual contract market generates over $30 billion in fees and revenue every year. Historically, almost all of this went to centralized exchanges and professional market makers. If next-gen protocols leverage AMM technology to pool market-making roles, more regular participants can share in these rewards. This is not just an innovation in risk management, but a complete overhaul of incentive systems.

Several new projects are already exploring these models. For example, AZEx, leveraging the Uniswap v4 Hook mechanism, is experimenting with the integration of “pre-trade risk controls, dynamic funding rates, and emergency market suspension” with LP profit sharing through pooled liquidity.

Next week, AZEx will open its testnet—interested readers can get updates here: [https://x.com/azex_io].

10. Conclusion

The XPL flash event reminds us: risk isn’t visible just in price charts—it’s coded deep within protocol design.

Most DeFi perpetuals today still rely on order books. As long as liquidity is thin and token concentration is high, similar incidents will keep happening.

The real challenge for next-generation Perp protocols isn’t UI, points, or rebates. It’s whether we can design a protocol where price discovery, risk management, and LP protection reinforce each other, breaking the cycle of stampedes in extreme markets—and whether we can return $30 billion in annual revenue from the hands of a few to a much broader set of participants.

Next-generation protocols must tackle both risk and reward redistribution. Whoever succeeds in both will have the chance to redefine the future of the DeFi perpetuals market.

Disclaimer:

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