Pi Network Temporarily Halts Wallet Payment Requests Amid Rising Scams

TheNewsCryptoPublicado a 2025-12-31Actualizado a 2025-12-31

Resumen

Pi Network has temporarily disabled its wallet payment request feature due to widespread scams. Scammers were impersonating official accounts and targeting high-balance wallets, tricking users into approving fraudulent requests, resulting in irreversible losses. Over a million PI tokens have been stolen this year, with one wallet receiving more than 838,000 tokens. The Pi Core Team clarified that the issue stems from user approval, not a system vulnerability, and emphasized that this is a common risk across crypto wallets. The token’s value dropped 10% following the incident but has since shown slight recovery. Recent updates include standardized KYC and testnet DEX improvements to enhance security and liquidity.

The Pi network, which is the mobile-first Cryptocurrency and web3 blockchain, recently posted a tweet on DEC 30 that the team has warned that scammers are exploiting the Payment request feature to steal Pi tokens from users’ balances. In the Pi network, the wallet balances are publicly visible, and the attackers are targeting the high-balance wallets and sending the payment request to the wallet as an impersonation of official Pi accounts or community moderators.

Once the user approves the payment request, the Tokens will be instantly transferred to the scammers’ account and will be gone forever. No reversal or recovery will be done. The Community reports indicate the scams are widespread, and one scammer’s wallet has received more than 838,000 PI tokens. Overall, more than a million PI tokens have been lost throughout the year. This created a panic situation among the users, and even the experienced users were tricked because the request looked legitimate.

Pi Core Team Denies System Vulnerability, Addresses Market and User Impact

After this incident happened, many users called it a wallet flaw, but the Core team clarified that the problem is from the human approval, not the protocol Flaw. They say that the wallet never sends funds on its own, and the transfer only happens after the user’s approval. This behavior exists in all major Crypto wallets. So to stop this scam, the PI network has disabled all payment requests network-wide. This stops the scams instantly and prevents further user losses. This makes the PI token safe, and it was the fastest way to contain the damage.

The token is currently trading at $0.204 with a market cap of 1.7 billion. The Token has dropped to 10% due to this issue. Recently, the token has updated its ecosystem by adding Standardized KYC to cut the verification backlogs by 50% and speed up mainnet migration for millions of users. Another update is from the testnet DEX, which makes the trading safer and easier by using Pi as the main trading Pair and improving the liquidity. This makes the token regain its momentum, and it has increased by 0.7% in 24 hours.

Highlighted Crypto News:

XDC Network Momentum Test: Will It Break Resistance or Slip Back to Lows?

TagsAltcoinCryptocurrencyPi Network

Preguntas relacionadas

QWhy did the Pi Network temporarily halt wallet payment requests?

AThe Pi Network temporarily halted wallet payment requests to stop scammers who were exploiting the feature to steal Pi tokens by impersonating official accounts, preventing further user losses.

QHow were the scammers able to steal Pi tokens from users?

AScammers targeted high-balance wallets, sent payment requests while impersonating official Pi accounts or community moderators, and stole tokens once users approved these deceptive transactions.

QWhat was the Pi Core Team's response to the incident being called a 'wallet flaw'?

AThe Pi Core Team clarified that the issue was due to human approval of fraudulent requests, not a protocol flaw, emphasizing that transfers only occur after user authorization, similar to other crypto wallets.

QHow much Pi token value was reported lost due to these scams?

ACommunity reports indicated that over a million Pi tokens were lost throughout the year, with one scammer's wallet receiving more than 838,000 Pi tokens.

QWhat recent updates has the Pi Network implemented to improve its ecosystem?

AThe Pi Network recently added Standardized KYC to reduce verification backlogs by 50% and introduced updates to the testnet DEX, using Pi as the main trading pair to enhance safety and liquidity.

Lecturas Relacionadas

OpenAI Post-Training Engineer Weng Jiayi Proposes a New Paradigm Hypothesis for Agentic AI

OpenAI engineer Weng Jiayi's "Heuristic Learning" experiments propose a new paradigm for Agentic AI, suggesting that intelligent agents can improve not just by training neural networks, but also by autonomously writing and refining code based on environmental feedback. In the experiment, a coding agent (powered by Codex) was tasked with developing and maintaining a programmatic strategy for the Atari game Breakout. Starting from a basic prompt, the agent iteratively wrote code, ran the game, analyzed logs and video replays to identify failures, and then modified the code. Through this engineering loop of "code-run-debug-update," it evolved a pure Python heuristic strategy that achieved a perfect score of 864 in Breakout and performed competitively with deep reinforcement learning (RL) algorithms in MuJoCo control tasks like Ant and HalfCheetah. This approach, termed Heuristic Learning (HL), contrasts with Deep RL. In HL, experience is captured in readable, modifiable code, tests, logs, and configurations—a software system—rather than being encoded solely into opaque neural network weights. This offers potential advantages in explainability, auditability for safety-critical applications, easier integration of regression tests to combat catastrophic forgetting, and more efficient sample use in early learning stages, as demonstrated in broader tests on 57 Atari games. However, the blog acknowledges clear limitations. Programmatic strategies struggle with tasks requiring long-horizon planning or complex perception (e.g., Montezuma's Revenge), areas where neural networks excel. The future vision is a hybrid architecture: specialized neural networks for fast perception (System 1), HL systems for rules, safety, and local recovery (also System 1), and LLM agents providing high-level feedback and learning from the HL system's data (System 2). The core proposition is that in the era of capable coding agents, a significant portion of an AI's learned experience could be maintained as an auditable, evolving software system.

marsbitHace 23 min(s)

OpenAI Post-Training Engineer Weng Jiayi Proposes a New Paradigm Hypothesis for Agentic AI

marsbitHace 23 min(s)

Your Claude Will Dream Tonight, Don't Disturb It

This article explores the recent phenomenon of AI companies increasingly using anthropomorphic language—like "thinking," "memory," "hallucination," and now "dreaming"—to describe machine learning processes. Focusing on Anthropic's newly announced "Dreaming" feature for its Claude Agent platform, the piece explains that this function is essentially an automated, offline batch processing of an agent's operational logs. It analyzes past task sessions to identify patterns, optimize future actions, and consolidate learnings into a persistent memory system, akin to a form of reinforcement learning and self-correction. The article draws parallels to similar features in other AI agent systems like Hermes Agent and OpenClaw, which also implement mechanisms for reviewing historical data, extracting reusable "skills," and strengthening long-term memory. It notes a key difference from human dreaming: these AI "dreams" still consume computational resources and user tokens. Further context is provided by discussing the technical challenges of managing AI "memory" or context, highlighting the computational expense of large context windows and innovations like Subquadratic's new model claiming drastically longer contexts. The core critique argues that this strategic use of human-centric vocabulary does more than market products; it subtly reshapes user perception. By framing algorithms with terms associated with consciousness, companies blur the line between tool and autonomous entity. This linguistic shift can influence user expectations, tolerance for errors, and even perceptions of responsibility when systems fail, potentially diverting scrutiny from the companies and engineers behind the technology. The article concludes by speculating that terms like "daydreaming" for predictive task simulation might be next, continuing this trend of embedding the idea of an "inner life" into computational processes.

marsbitHace 25 min(s)

Your Claude Will Dream Tonight, Don't Disturb It

marsbitHace 25 min(s)

Trading

Spot
Futuros
活动图片