Post-Mortem of the Venus THE Attack: How to Profit in a Fleeting Window?

marsbitОпубліковано о 2026-03-16Востаннє оновлено о 2026-03-16

Анотація

Approximately two hours ago, Venus Protocol's THE token was exploited using a classic Mango Markets-style price manipulation attack. The attacker targeted THE, a low-liquidity collateral asset, by depositing it, borrowing other assets, and using those to buy more THE, artificially inflating its price. Once the time-weighted average oracle updated, the inflated price allowed further leveraged borrowing. To bypass THE's borrowing cap, the attacker performed a "donation attack" by transferring THE directly to the vTHE contract, increasing the recognized collateral value. After the first manipulation phase, THE's price stabilized around $0.50. The attacker attempted to further amplify gains by continuing to buy THE, but mounting sell pressure limited price increases and pushed their health factor near 1.0, risking liquidation. The collateral, nominally valued around $30M, had extremely low liquidity, making large-scale liquidation at inflated prices impossible. Recognizing the situation, the writer opened a short position on THE with high leverage, anticipating a price collapse due to overvaluation, illiquidity, and forced selling. After liquidation, THE price plummeted to ~$0.24, below its pre-attack level, resulting in a ~$15K profit for the writer. Venus Protocol was left with ~$2M in bad debt. The attacker likely gained little or lost funds, though may have profited from off-chain positions. The event highlights that nominal collateral value in DeFi does not equal realizabl...

Two hours ago, VenuV's THE token was hit with a classic Mango Markets-style price manipulation attack.

The attacker targeted the low-liquidity collateral THE:

· First, collateralized THE

· Borrowed other assets

· Used the borrowed assets to buy more THE

· Pushed THE price higher

· Waited for the time-weighted average oracle price to update, then obtained higher collateral value and continued the cycle of borrowing.

Due to THE's extremely poor on-chain liquidity, its price was driven from $0.27 to nearly $5. The oracle price subsequently updated to around $0.5 (time-weighted average), giving the attacker room to further amplify leverage.

More critically, THE itself has a supply cap.


Normally, this would limit the attacker's ability to expand their position. But they used a classic old trick to bypass it: the Compound fork donation attack. After depositing a large amount of THE, they directly transferred THE to the vTHE contract, "donating" to further inflate the collateral value recognized by the system and break through the cap.

Attack transaction: 0x4f477e941c12bbf32a58dc12db7bb0cb4d31d41ff25b2457e6af3c15d7f5663f

After the first wave of the attack, THE's price stabilized around $0.5.

At this point, the attacker could have walked away with the borrowed assets. But they clearly wanted to maximize profits, so they continued to use the borrowed assets to buy THE, attempting another pump.

The problem arose: Although the price was abnormally high, selling pressure from the market became extremely intense. The attacker kept buying but could barely push the price higher. Eventually, they almost exhausted their borrowing capacity, and their position's health factor dropped close to 1, nearing liquidation.

By then, the situation was very clear: The attacker's collateral, including their pre-prepared assets and THE bought during the attack, had a nominal value of about 30M. But the core issue with this collateral was—there was simply not enough liquidity to absorb it. Once liquidation began, this THE would be dumped onto the market. And no one in the market could possibly absorb such a large volume at these inflated prices.

So what did I do?

When liquidation started, I directly opened a short position on THE. And this was a position where relatively higher leverage could be applied.

The reason was simple: High valuation, low liquidity, massive passive selling pressure, no buyers.

The outcome was unsurprising: After the liquidation ended, THE's price fell all the way back to around $0.24, even lower than the pre-attack price, because original holders also sold during the process.

I closed my short position here, profiting about 15K.

In the end, Venus was left with about 2M in bad debt.

As for how much the attacker actually profited, I haven't done a complete analysis; but judging from the operations of some addresses, they likely made little to no profit, or even blew themselves up. However, the attacker might still have profited from off-chain perp positions (just like our operation).

Venus's ~2M bad debt address: https://debank.com/profile/0x1a35bd28efd46cfc46c2136f878777d69ae16231

This incident once again demonstrates:


In DeFi, "nominal collateral value" does not equal "liquidation value". When the collateral itself lacks liquidity, the system sees 30M, but the market might only be able to realize a fraction of that.

In 2023, I published a paper titled 'Unmasking Role-Play Attack Strategies in Exploiting Decentralized Finance (DeFi) Systems' which provides a detailed mathematical model of this type of attack. Interested readers can refer to: https://dl.acm.org/doi/10.1145/3605768.3623545

Пов'язані питання

QWhat was the core strategy used by the attacker in the Venus THE exploit?

AThe attacker used a price manipulation strategy similar to the Mango Markets attack. They deposited the low-liquidity collateral THE, borrowed other assets, used those assets to buy more THE to drive its price up, waited for the time-weighted average oracle price to update to reflect the inflated value, and then repeated the cycle to gain higher borrowing power.

QHow did the attacker bypass the supply cap limitation on THE?

AThe attacker used a 'donation attack' by directly transferring THE tokens to the vTHE contract after a large deposit. This 'donation' artificially increased the total supply recognized by the system, allowing them to further inflate the collateral value and bypass the supply cap.

QWhy did the author of the article decide to open a short position on THE?

AThe author opened a short position because the attacker's collateral (THE tokens) had an extremely high nominal value but very low liquidity. They anticipated that once liquidation began, the massive sell pressure from the forced selling of these tokens would cause the price to crash dramatically, as there would be no market to absorb such a large volume at the inflated price.

QWhat was the final outcome for the attacker and the Venus protocol?

AThe attacker likely made little to no profit and may have even lost money from their on-chain maneuvers, though they might have profited from off-chain perpetual positions. The Venus protocol was left with approximately $2 million in bad debt.

QWhat key DeFi concept does this event highlight according to the article?

AThe event highlights that 'nominal collateral value' is not the same as 'liquidation value.' When collateral itself lacks sufficient liquidity, the value the system calculates can be vastly higher than the amount the market can actually realize during a liquidation event.

Пов'язані матеріали

From South Korea to the United States: Blue-Collar Jobs Are Becoming Increasingly Popular, Thanks to AI

AI is reshaping the labor market's value proposition. The traditional four-year college degree is losing its appeal as a guaranteed career path, while skilled blue-collar trades like electricians, welders, and plumbers are experiencing historic demand and wage premiums. This shift is driven by dual pressures: AI's displacement of certain white-collar roles and a booming need for physical infrastructure and data center construction. Data confirms the trend. In the U.S., vocational school revenue surged, and a significant portion of recent layoffs are AI-related. Surveys show a majority of Gen Z adults plan to pursue blue-collar work, citing better job security against AI automation. Vocational education interest has exploded recently. Experts cite a psychological shift as younger generations seek tangible, AI-resistant careers and avoid high student debt. In many cases, salaries for skilled trades now match or exceed those requiring a bachelor's degree. In South Korea, semiconductor vocational high schools boast near-total employment, with graduates securing high-paying roles at companies like Samsung. The shortage is structural, exacerbated by a retiring baby boomer workforce and massive infrastructure projects. Companies like JPMorgan Chase, Meta, and Lowe's are investing heavily in training programs. However, overcoming historical stigma and a "perception gap" around trade careers remains a key challenge to closing the talent gap.

marsbit1 год тому

From South Korea to the United States: Blue-Collar Jobs Are Becoming Increasingly Popular, Thanks to AI

marsbit1 год тому

Qualcomm: AI Hype Subsides, When Will Smartphones Emerge from the Gloom?

Qualcomm reported its Q3 FY2026 results (ending June 2026), with revenue of $9.95B, down 4% YoY but above expectations. Gross margin declined to 53.1%, pressured by rising costs across manufacturing and memory. Key business segments showed mixed performance: Handset revenue fell 19.6% YoY to $5.09B, dragged by an 11% decline in non-Apple Android shipments and weaker high-end mix. Conversely, Automotive revenue surged 61% to $1.59B, and IoT grew 9% to $1.83B. Core operating profit dropped 41% YoY due to margin compression and higher expenses. Management's Q4 FY2026 guidance projects revenue of $9.7B-$10.5B, in line with consensus, but Non-GAAP EPS guidance of $2.05-$2.25 fell short of expectations. Amidst persistent weakness in its core handset market, Qualcomm is pursuing growth in AI, focusing on Edge AI (smartphones, PCs, automotive) and Data Center AI. Its data center strategy includes four pillars: AI accelerators (e.g., AI200), commercial CPUs (Dragonfly C1000), custom silicon, and connectivity solutions. While these initiatives initially boosted its stock, concerns over AI capital expenditure sustainability have since erased those gains. The company targets $5B in data center revenue for FY2027 and $15B for FY2029. The report concludes that with the traditional handset business still under pressure, the data center opportunity is currently viewed as a longer-term option, and a more conservative valuation based on core operations may be warranted until AI contributions materialize.

marsbit1 год тому

Qualcomm: AI Hype Subsides, When Will Smartphones Emerge from the Gloom?

marsbit1 год тому

From TPU to Self-Evolving Agents: How Jeff Dean Predicts the Next Step in AI

At the 2026 YC Startup School, Jeff Dean outlined his vision for AI's next phase, shifting focus from simply scaling models to building intelligent, autonomous systems. He believes AI's progress is no longer just about creating smarter models, but about integrating them into systems capable of long-term, iterative work, automated experimentation, and continuous learning. This evolution moves the competition from "who has the bigger model" to "who can best organize intelligence." Dean suggests AI capabilities are now comparable to a junior engineer, enabling the automation of complex workflows. However, the true challenge and opportunity lie in managing these AI "workers" at scale. He emphasizes the importance of **context engineering**—structuring tools, memory, and feedback loops—over raw model power. For startups, this means building deep expertise in niche domains where general models currently fail (near 0-1% success rates), leveraging proprietary data, specialized tools, and domain-specific evaluators. A recurring theme is re-examining fundamental constraints. Dean's past work, like moving Google's search index to memory or creating the TPU, stemmed from questioning outdated assumptions about hardware and cost. He sees similar inflection points today, particularly in **specialized inference hardware** to drastically reduce latency and energy consumption for real-time Agent operation. Notably, he points out that in modern AI systems, the dominant cost is often not computation but **data movement**. Reliable, long-running Agents require robust system design, borrowing concepts from distributed computing like checkpointing, state management, and parallel exploration to handle failures and maintain progress over days or weeks. As AI automates execution, the scarcest human skills will shift to **defining clear specifications**, **judging what problems are worth solving** (taste), and designing effective feedback loops. Ultimately, Dean's framework prioritizes understanding the problem deeply, identifying the true bottlenecks, and systematically building closed-loop systems where AI can not only perform tasks but also improve AI itself.

marsbit1 год тому

From TPU to Self-Evolving Agents: How Jeff Dean Predicts the Next Step in AI

marsbit1 год тому

Торгівля

Спот
活动图片