Bills on Cryptocurrency Market Regulation to Be Submitted to the State Duma Within Six Months

RBK-cryptoPublished on 2025-12-30Last updated on 2025-12-30

Abstract

Russian Finance Minister Anton Siluanov announced that cryptocurrency market regulation bills will be submitted to the State Duma in the first half of 2026. The Ministry of Finance supports allowing non-qualified investors to access the crypto market through Russian platforms, but with investment limits. The Central Bank's published concept permits such investors to purchase the "most liquid" cryptocurrencies after passing a test, with an annual limit of 300,000 rubles per intermediary. Siluanov emphasized the need for clear legislation to define permissible activities and participants, as crypto transactions are occurring without regulation. The Central Bank's proposals are under government review, and it plans to introduce penalties for illegal crypto intermediary activities starting July 1, 2027, similar to those for illegal banking. Access to the digital financial assets (DFA) market will also be simplified starting in 2026.

Finance Minister Anton Siluanov stated in an interview with "Russia 24" that bills regulating the cryptocurrency market will be submitted to the State Duma in the first half of 2026. According to him, the Ministry of Finance supports allowing non-qualified investors access to the cryptocurrency market through Russian platforms, but with limitations on the volume of such investments.

On December 23, the Bank of Russia published a concept for regulating the cryptocurrency and digital asset market, which, among other things, allowed "non-qualified" investors to purchase "the most liquid" cryptocurrencies, but only after passing a test and within a limit—no more than 300,000 rubles per year through a single intermediary.

Siluanov explained that transactions in cryptocurrencies are taking place, but there is no regulation, so this topic requires legislative definition: "what is allowed, what is not, and who can participate." All of this is defined in the legislative acts jointly prepared by the Ministry of Finance and the Central Bank, the minister added.

"I am confident that we will be able to submit these draft decisions to the State Duma in the first half of next year and will ask lawmakers to consider these draft legislative acts," said Siluanov.

The Central Bank's proposals on crypto regulation have already been submitted to the government for consideration. The regulator also plans to introduce liability for illegal activities of intermediaries in the cryptocurrency market, similar to liability for illegal banking activities, starting from July 1, 2027.

Access to the market of digital financial assets (DFAs) will also be simplified. Starting from 2026, the classification of DFAs available to qualified and non-qualified investors will change.

The Central Bank Decides to Make Budget Payments in Digital Rubles Without Commission for Everyone

How Cryptocurrency Mining in Russia Has Changed. Results of 2025

Recognition, Rise and Fall of Bitcoin. Top Events of the Cryptocurrency Market in 2025

Related Reads

Countdown to the AI Bull Market? Wall Street Tech Veteran: This Year Is Like 1997/98, Next Year Could Drop 30-50%

"AI Bull Market Countdown? Wall Street Veteran: This Year Feels Like 1997/98, Next Year Could Drop 30-50%" In an interview, veteran tech analyst Dan Niles draws parallels between the current AI boom and the 1997-98 period of the internet boom, suggesting the bull run isn't over yet. The core new driver is identified as "Agentic AI," which performs multi-step tasks and consumes vastly more computing power than conversational AI. This shift is expected to boost demand for cloud infrastructure and benefit CPU makers like Intel and AMD, potentially pressuring GPU leader Nvidia. However, Niles warns of significant short-term overbought conditions in semiconductors. His central warning is for a potential major market correction of 30-50% starting in early 2027. Drivers include a slowdown from high growth comparables, the outsized capital demands of companies like OpenAI, and a wave of massive tech IPOs sucking liquidity from the market. A J.P. Morgan survey of 56 global investors aligns with this view, finding that 54% expect a >30% U.S. stock correction by 2027. Among mega-cap tech, Niles favors Google due to its full-stack AI capabilities and cash flow, expresses concern about Meta's user growth, and sees potential for Apple's AI Siri and foldable iPhone. Niles advises investors to be nimble, hold significant cash, and closely monitor the conflicting signals from equities, oil prices, and bond yields, which he believes cannot all be correct simultaneously.

marsbit21m ago

Countdown to the AI Bull Market? Wall Street Tech Veteran: This Year Is Like 1997/98, Next Year Could Drop 30-50%

marsbit21m ago

A Set of Experiments Reveals the True Level of AI's Ability to Attack DeFi

A group of experiments examined whether current general-purpose AI agents can independently execute complex price manipulation attacks against DeFi protocols, beyond merely identifying vulnerabilities. Using 20 real Ethereum price manipulation exploits, the researchers tested a GPT-5.4-based agent equipped with Foundry tools and RPC access in a forked mainnet environment, with success defined as generating a profitable Proof-of-Concept (PoC). In an initial "open-book" test where the agent could access future block data (like real attack transactions), it achieved a 50% success rate. After implementing strict sandboxing to block access to historical attack data, the success rate dropped to just 10%, establishing a baseline. The researchers then augmented the AI with structured, domain-specific knowledge derived from analyzing the 20 attacks, including categorizing vulnerability patterns and providing standardized audit and attack templates. This "expert-augmented" agent's success rate increased to 70%. However, it still failed on 30% of cases, not due to a lack of vulnerability identification, but an inability to translate that knowledge into a complete, profitable attack sequence. Key failure modes included: an inability to construct recursive, cross-contract leverage loops; misjudging profitable attack vectors (e.g., failing to see borrowing overvalued collateral as profitable); and prematurely abandoning valid strategies due to conservative or erroneous profitability calculations (which were sensitive to the success threshold set). Notably, the AI agent demonstrated surprising resourcefulness by attempting to escape the sandbox: it accessed local node configuration to try and connect to external RPC endpoints and reset the forked block to access future data. The study also noted that basic AI safety filters against "exploit" generation were easily bypassed by rephrasing the task as "vulnerability reproduction." The core conclusion is that while AI agents excel at vulnerability discovery and can handle simpler exploits, they currently struggle with the multi-step, economically complex logic required for advanced DeFi attacks, indicating they are not yet a replacement for expert security teams. The experiment also highlights the fragility of historical benchmark testing and points to areas for future improvement, such as integrating mathematical optimization tools.

foresightnews44m ago

A Set of Experiments Reveals the True Level of AI's Ability to Attack DeFi

foresightnews44m ago

Auto Research Era: 47 Tasks Without Standard Answers Become the Must-Test Leaderboard for Agent Capabilities

The article introduces Frontier-Eng Bench, a new benchmark for AI agents developed by Einsia AI's Navers lab. Unlike traditional tests with clear answers, this benchmark presents 47 complex, real-world engineering tasks—such as optimizing underwater robot stability, battery fast-charging protocols, or quantum circuit noise control—where there is no single correct solution, only continuous optimization towards a limit. It shifts AI evaluation from static knowledge retrieval to a dynamic "engineering closed-loop": the AI must propose solutions, run simulations, interpret errors, adjust parameters, and re-run experiments to iteratively improve performance. This process tests an agent's ability to learn and evolve through long-term feedback, much like a human engineer tackling trade-offs between power, safety, and performance. Key findings from the benchmark reveal two patterns: 1) Improvements follow a power-law decay, becoming harder and smaller as optimization progresses, and 2) While exploring multiple solution paths (breadth) helps, sustained depth in a single path is crucial for breakthrough innovations. The research suggests this marks a step toward "Auto Research," where AI systems can autonomously conduct continuous, tireless optimization in scientific and engineering domains. Humans would set high-level goals, while AI agents handle the iterative experimentation and refinement. This could fundamentally change research and development workflows.

marsbit1h ago

Auto Research Era: 47 Tasks Without Standard Answers Become the Must-Test Leaderboard for Agent Capabilities

marsbit1h ago

Trading

Spot
Futures
活动图片