Tarun Chitra's Critical Analysis Reveals How Outdated ADL Algorithm Cost Hyperliquid $650 Million

The October 10 crypto market crash triggered widespread discussions about exchange safety mechanisms, and Tarun Chitra, CEO of risk management firm Gauntlet, has brought renewed attention to a systemic flaw in how platforms handle trader losses. His findings expose a troubling gap between current industry practices and optimal risk management—one that could have prevented a devastating loss cascade.

The October Liquidation Crisis and Its Unexpected Aftermath

When the market crashed on October 10, $1.9 billion in positions faced liquidation across major platforms. However, the aftermath revealed something more concerning than typical market volatility. Jinse Finance’s coverage highlighted that the true cost to profitable traders far exceeded what basic liquidation alone would have caused.

The crisis serves as a case study for understanding how a single mechanism can amplify losses beyond their natural market level. Tarun Chitra’s investigation into the incident demonstrated that the damage extended far beyond insolvent traders—it reached profitable ones as well.

Understanding Auto-Deleveraging: The “Backstop” That Became a Problem

Auto-Deleveraging (ADL) functions as what Tarun Chitra defines as an “ultimate backstop” mechanism. When exchanges face bad debt from positions they cannot cover, the system automatically reduces the position sizes of profitable traders to compensate—essentially spreading the loss from underwater positions to those still in profit.

The mechanism itself isn’t new. This sequential algorithmic approach has remained largely unchanged for over a decade, adopted across multiple perpetual contract platforms including Hyperliquid and Lighter. The longevity of this system created a false sense of security, even as market conditions evolved dramatically.

What Tarun Chitra’s analysis reveals is the scale of this transfer: over $650 million was deleveraged from profitable traders’ positions on Hyperliquid alone. To contextualize this figure, the bad debt these traders were absorbing amounted to just $23 million—meaning profitable traders paid approximately 28 times the actual debt their exchange needed to cover.

Why Current Algorithms Fall Short: Tarun Chitra’s Finding

The core problem identified by Tarun Chitra lies not in the concept of Auto-Deleveraging itself, but in the outdated queue-based algorithm executing it. The existing system makes deletion decisions based on sequential ordering rather than sophisticated optimization, creating inefficiencies that cascade into massive collateral damage.

Traders positioned favorably before the crash found their profits systematically eliminated to cover losses they didn’t create. This wasn’t market-driven liquidation—it was a mechanical wealth transfer executed by an aging algorithm.

A Path Forward: Gauntlet’s Proposed Solution

Tarun Chitra and Gauntlet have detailed a comprehensive alternative in a 95-page technical report, presenting modernized ADL algorithms designed to minimize impact on profitable positions while still protecting exchanges from insolvency.

These new approaches would allow platforms to meet their debt obligations without triggering the indiscriminate deleveraging that characterized the October 10 aftermath. The solution represents years of research into optimal risk distribution—addressing precisely what Tarun Chitra identified as the fundamental flaw in current systems.

For perpetual contract platforms, the choice is clear: continue using decade-old algorithms that prioritize simplicity over efficiency, or adopt frameworks that Tarun Chitra’s research has shown can drastically reduce collateral damage to profitable traders. The $650 million loss on Hyperliquid alone suggests the industry cannot afford the former approach much longer.