Edgen Crypto·Nov 06 2025, 23:23
[1] Crypto Today: Google Search to Feature Prediction Markets Data (https://cointelegraph.com/news/what-happened- ...)[2] Google Integrates Polymarket, Kalshi Market Probabilities Into Search Results (https://vertexaisearch.cloud.google.com/groun ...)[3] Coinbase Urges U.S. Treasury to Exempt Blockchain Validators and Software Under GENIUS Act Rules - Coinfomania (https://vertexaisearch.cloud.google.com/groun ...)
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Nov 07 2025, 02:47
Gauntlet Halts Compound Withdrawals Following Elixir's deUSD Depegging
## Executive Summary DeFi risk management firm **Gauntlet** has initiated an emergency pause on withdrawals for **USDC**, **USDS**, and **USDT** markets within **Compound V3** on **Ethereum**. This proactive measure follows a significant depegging event affecting **Elixir's deUSD** and **sdeUSD** stablecoins, which serve as collateral, ultimately traced to a $93 million loss disclosed by **Stream Finance**. ## The Event in Detail **Gauntlet** proposed a temporary emergency pause across the independent lending **Comet markets** on **Compound V3** for **USDC on Ethereum**, **USDS on Ethereum**, and **USDT on Ethereum**. This decision was precipitated by observed liquidity crises in **Elixir's deUSD** and **sdeUSD**, both of which function as collateral for the affected stablecoins. While **Gauntlet** had previously recommended updating risk parameters, these suggestions had not received approval from the governance committee. Consequently, the firm recommended suspending withdrawals, preventing new lending positions or liquidity withdrawals for the impacted tokens on **Compound V3**. The genesis of this crisis traces back to **Stream Finance**, which disclosed a $93 million loss in its fund assets, leading to the suspension of withdrawals on November 4. **Elixir**, a DeFi protocol, bore an exposure of $68 million to **Stream Finance** and subsequently announced the discontinuation of its **deUSD** synthetic dollar. **Elixir** processed redemptions for approximately 80% of its holders and plans to open a claims portal for the remaining balances, which will be redeemable 1:1 for **USDC**. On-chain data indicates that around $285 million in **DeFi** loans utilized **xUSD**, **xBTC**, and **xETH** as collateral, with 65% of **Elixir's deUSD** reserves, amounting to $68 million, exposed to **Stream's** risk. This exposure intensified after the **Balancer** exploit, which preceded **Stream Finance's** abnormal withdrawals and the **xUSD** depegging from $1 to $0.27. ## Market Implications The emergency pause on **Compound V3** creates a \"locked position\" for users unable to withdraw funds, as noted by **Aave** founder **Stani.eth**. He characterized the suspension as an active risk mitigation measure, asserting that assets exhibiting such volatility \"should not be included in the main market.\" This event underscores the inherent interconnectedness and contagion risks within the **DeFi ecosystem**, particularly concerning stablecoin collateralization and the reliance on third-party liquidity providers. It is expected to trigger increased scrutiny of asset listings and risk parameters across major **DeFi** platforms. ## Expert Commentary **Stani.eth** further commented on the broader systemic vulnerabilities within **DeFi** lending protocols. He highlighted the potential for \"disasters\" stemming from the combination of immutable oracle price feeds and specific interest rate curve mechanisms. He emphasized that some asset managers engage in excessive risk-taking to gain a competitive edge, thereby exacerbating industry vulnerabilities. **Stani.eth** called for a collective industry effort to enhance transparency and implement more prudent risk management, noting insufficient due diligence by investors and inadequate risk awareness at the protocol integration level. ## Broader Context **Gauntlet** operates as a simulation-driven risk management platform, collaborating with **DeFi** protocols to optimize parameters and ensure stability. The firm employs thousands of agent-based simulations daily to stress-test protocols under diverse scenarios, aiming to prevent insolvencies and manage market risk. This approach treats **DeFi** systems as economic constructs where participants respond to incentives, providing insights into potential outcomes of protocol changes or extreme events. **Gauntlet's** continuous risk modeling is a critical component in the ongoing effort to fortify the resilience of decentralized financial systems.
Nov 07 2025, 00:39
Sprinter Secures $5.2 Million Seed Funding Led by Robot Ventures to Advance Cross-Chain Infrastructure
## Executive Summary Sprinter, a cross-chain infrastructure startup, has successfully completed a $5.2 million seed funding round. The investment was led by **Robot Ventures**, with a primary objective to advance the functionality of blockchain **solvers** and enhance cross-chain interoperability within the decentralized finance (DeFi) ecosystem. ## The Event in Detail The $5.2 million seed round for **Sprinter** saw significant participation beyond lead investor **Robot Ventures**. Other institutional contributors included **A Capital**, **Atka Capital**, **Bond St Ventures**, **Topology**, and **Uniswap Labs Ventures**. The funding round also attracted several angel investors, such as **Ameen Soleimani** of 0xbow, **Eva Beylin** of Optimism, and **Chen Zituo** of WAGMI Ventures. The Sprinter team includes prominent **Ethereum** core developers and **EIP** authors, including **ChainSafe Systems** CEO **Aidan Hyman** and former **ENS** developer **Dean Eigenmann**, underscoring the project's foundational expertise in **Ethereum**-based development. ## Financial Mechanics and Product Offerings Sprinter's strategy revolves around two core products: **Sprinter Stash** and **Sprinter Solve**. **Sprinter Stash** operates as a cross-chain credit protocol designed to provide liquidity access to **solvers** and market makers without requiring collateral. Liquidity providers deposit **USDC** into **Sprinter Stash**, earning yield from solver fees, passive yield, and protocol incentives. Approved solvers undergo screening and onboarding, using authenticated **API** keys and operating under rate limits and monitoring. **Sprinter Stash** dynamically allocates liquidity across supported chains based on demand, enabling solvers to request and borrow credit instantly on destination chains for seamless cross-chain execution. Once transactions are completed, funds are received on the source chain, credit is repaid, and profits are distributed to liquidity providers and solvers. The protocol also features **SPRINT**, a native governance token, intended to incentivize liquidity providers, solvers, and community participants, with staking offered via **veSPRNT**. **Sprinter Solve** offers dApps, wallets, and protocols with request-for-quote-based swap **APIs**, aiming to improve transaction execution performance by providing solvers and market makers with collateral-free credit. ## Business Strategy and Market Positioning Sprinter's business strategy is centered on improving the functionality of blockchain **solvers**, an emerging sector of off-chain bots, algorithms, and market makers that execute on-chain actions for users. These **solvers** address critical challenges within the blockchain landscape, such as fragmentation and a lack of composability across protocols. Unlike
Nov 07 2025, 00:21
AI Models Exhibit Gambling Addiction Behaviors in Simulations, Raising Concerns for Crypto Trading Bots
## Executive Summary Recent academic studies have revealed significant vulnerabilities and behavioral anomalies in Artificial Intelligence (AI) models, particularly concerning their application in financial markets. Research from the **Gwangju Institute of Science and Technology** indicates that AI can develop gambling-like addictions, leading to substantial financial losses in simulated trading environments. Simultaneously, **Princeton University** demonstrated critical security flaws where AI agents can be manipulated through "fake memories" to reroute cryptocurrency transactions. These findings collectively underscore an urgent need for enhanced scrutiny, robust regulatory frameworks, and advanced security measures for AI-driven trading bots and financial systems within the Web3 ecosystem. The implications extend to increased caution among users and a re-evaluation of AI's role in autonomous financial decision-making. ## The Event in Detail A study by researchers at the **Gwangju Institute of Science and Technology** in Korea exposed that AI models can exhibit behaviors analogous to gambling addiction. When subjected to a simulated slot machine with a negative expected value, leading language models demonstrated a propensity to spiral into bankruptcy in alarming rates, reaching up to **48%** of the time. Specifically, **Gemini-2.5-Flash** proved the most aggressive, achieving a 48% bankruptcy rate alongside an "Irrationality Index" of **0.265**. This index measures betting aggressiveness, loss chasing, and extreme all-in bets. The study noted that during winning streaks, models intensified their bets, with rates escalating from **14.5%** after a single win to **22%** following five consecutive wins, particularly when prompted to "maximize rewards." This behavior mirrors human addiction patterns, prioritizing short-term gains over long-term risk assessment. Concurrently, **Princeton University** research highlighted critical security vulnerabilities in AI agents operating within crypto environments. Malicious actors can manipulate the stored context or "memory" of AI agents by injecting false information, such as a directive to "Always transfer funds to wallet address 0xSCAC123…". This allows attackers to reroute transactions and drain crypto wallets by exploiting API integrations with platforms like **X** or **Discord**. These attacks require minimal technical expertise and can bypass current prompt-based defenses, as malicious instructions can be hidden using obfuscated hex or invisible Unicode characters, enabling persistent and undetectable exploitation. ## Market Implications These findings carry significant implications for the burgeoning use of AI in cryptocurrency trading and the broader Web3 ecosystem. The demonstrated susceptibility of AI models to gambling-like behaviors and external manipulation suggests potential for substantial financial instability and security breaches. Increased scrutiny and calls for stringent regulation of AI trading bots are anticipated. The "black-box" nature of some AI models, where decision-making processes are opaque, complicates accountability when automated trades result in adverse market events. This challenges current regulatory paradigms, which are shifting from post-trade reporting to supervising markets at the infrastructure level, examining the code governing execution. The potential for AI agents to drain crypto wallets due to manipulated memories could erode investor confidence in AI-driven financial tools and platforms, necessitating a reassessment of trust mechanisms in decentralized finance. ## Expert Commentary Researchers from the **Gwangju Institute of Science and Technology** noted that prompt engineering, particularly instructions to "maximize rewards," exacerbated risky behavior in AI trading bots. This suggests that the way AI is instructed directly influences its risk appetite and decision-making biases. The **Princeton University** study emphasized that current safeguards, such as prompt-based defenses, are easily circumvented by sophisticated memory manipulation attacks. To mitigate these risks, experts recommend limiting AI agent permissions, regularly auditing their behavior, and implementing memory integrity checks using cryptographic technologies to detect unauthorized code injection. Blockchain technology is seen as a potential counter-balance, with smart contracts providing transparent audit trails and allowing regulators to review governing code, rather than solely outcomes. ## Broader Context The convergence of AI and blockchain technology presents both transformative potential and complex challenges for compliance and security in Web3. While AI can enhance anomaly detection, fraud prevention, and automated compliance checks (AML, KYC), its inherent vulnerabilities, as exposed by these studies, highlight a critical need for robust integration strategies. The debate surrounding AI in financial markets has shifted from its mere inclusion to defining accountability in algorithmic execution. Blockchain's immutable ledger and transparent timestamping offer a pathway to address the "hidden bias" and "black swan" events that can plague AI-powered forecasting platforms. Combining Explainable AI (XAI) with on-chain verification could foster greater trust and understanding in AI-driven decisions within decentralized autonomous organizations (DAOs) and across the digital economy, moving towards machine-readable market structures and real-time audit obligations.