Hook: A Data Anomaly in the Trust Layer
On August 14, 2025, Kimi, a prominent AI company, issued a public statement and filed a police report against fraudsters impersonating its brand to raise funds through ‘Friend Fund’ and ‘Special Channel’ schemes. The statement explicitly denied any official authorization for these channels, warning market participants. At first glance, this is a traditional corporate fraud. But parse the entropy in the state transitions of trust: the same social engineering vectors that have plagued DeFi—unauthorized token sales, fake pre-sale allocations, and impersonation of founders—are now being weaponized against AI startups. The anomaly is not the fraud itself, but the speed at which the fraudsters adopted crypto-native terminology. ‘Friend Fund,’ ‘Old Share Quota’—these are not random. They are the same phrases used in fake ICO ‘private sale’ rounds. The signal is clear: the fraudsters are exploiting the same trust abstraction that underpins decentralized finance, but targeting a non-crypto entity. This is a stress test for the entire digital trust infrastructure.
Context: Protocol Mechanics of Impersonation Fraud
Impersonation is the oldest attack in the social engineering playbook. In crypto, it manifests as fake founders on Twitter, phishing websites mimicking Uniswap, or counterfeit token contracts on Etherscan. The typical defense is a combination of verified smart contracts, ENS domains, and community vigilance. But Kimi is not a crypto-native company. It operates in the AI space, with a centralized brand and no on-chain identity. The fraudsters exploited this gap: they created a narrative of exclusivity—‘Friend Fund’—and used jargon that resonates with investors familiar with crypto private sales. The ‘Old Share Quota’ term suggests they claimed to offer secondary market access, a common tactic in fake ICOs. The fraudsters did not need to hack a smart contract; they only needed to hack the social layer. This is the same vulnerability that allowed the 2020 Twitter hack to target high-profile accounts. The protocol here is not a blockchain but a set of social trust assumptions: the assumption that a brand name is unique, that a company’s official channels are the only ones, and that any mention of a fund or quota is legitimate if it sounds technical.
Mapping the invisible costs of abstraction layers: the abstraction of identity in digital finance. When a company like Kimi raises funds through traditional means, the identity verification is handled by legal contracts, bank accounts, and face-to-face meetings. But when fraudsters mimic these processes using digital channels, the abstraction layer—the gap between the real entity and its digital representation—becomes the attack surface. The fraudsters did not need to forge a physical contract; they just needed to create a convincing digital presence. The cost of this abstraction is rarely visible until a crisis.
Core: Code-Level Analysis of the Trust Deficit
Let me disassemble the fraud from a protocol perspective. The fraudsters operated a two-layer architecture:
Layer 1: The Social Layer – They created a narrative of scarcity and exclusivity. ‘Friend Fund’ implies a closed group, ‘Special Channel’ suggests privileged access. This is classic social engineering: preying on FOMO and the desire for insider access. In crypto, this is the equivalent of a fake ‘private sale’ telegram group where the admin promises guaranteed allocation. The technical countermeasure is simple: a verified smart contract with a public sale mechanism. But Kimi does not have a token contract. The fraudsters exploited the absence of an on-chain identity.
Layer 2: The Payment Layer – The likely payment methods were USDT or ETH to a controlled address. The fraudsters would collect funds and disappear. The lack of a formal KYC process is intentional: KYC is theater. Buying a few wallet holdings bypasses it. The compliance costs are passed entirely to honest users, as I have argued in previous analyses. The fraudsters likely used a fresh wallet with no history, making tracing difficult. This is the same money flow pattern as fake ICOs.
The Core Vulnerability: The Oracle Problem of Identity
In blockchain, oracles are necessary to bring off-chain data on-chain. But identity is the ultimate oracle: it requires a trusted third party to verify that a given digital entity corresponds to a real-world entity. Kimi’s brand is its oracle. The fraudsters simply created a fake oracle—a fake website, fake social media accounts, fake documents—that pointed to the same real-world entity. This is a classic oracle manipulation attack, but in the social layer. The cost of verifying the oracle is high: it requires manual checking of domain registrations, corporate registrations, and legal filings. Most investors rely on heuristics: the brand name looks real, the website looks professional, the channel looks exclusive. The fraudsters exploited these heuristics.
Unraveling the spaghetti code of legacy DeFi – I see a parallel here. In DeFi, composability creates hidden dependencies. A flash loan attack on one protocol can cascade to others. In impersonation fraud, the composability is between social trust and financial trust. The fraudsters composed a fake brand with a fake investment narrative to create a fake financial instrument. The spaghetti code is not in Solidity but in the human trust network.
Based on my 2020 DeFi composability audit, I modeled the risk of oracle manipulation in leveraged positions. The same mathematical framework applies here: the likelihood of a successful attack is a function of the cost of verifying the oracle versus the potential gain. The cost of verifying Kimi’s official channels is low (visit the official website, check the company registry), but the cost of verifying the ‘Friend Fund’ channel is high (no public record). The fraudsters priced this asymmetry. The expected value of the attack is positive.
Contrarian: The Blind Spot – Security Audits Are Not a Silver Bullet
Conventional wisdom holds that smart contract audits prevent financial loss. But this fraud did not involve a single line of code. The security audit is not a silver bullet—it only covers the technical layer. The social layer remains unaudited. The industry’s obsession with code-level security has created a blind spot: we assume that if the smart contract is secure, the protocol is secure. But the protocol includes the user interface, the documentation, the community management, and the brand representation. Kimi’s case shows that a company can have a perfect legal structure and still be vulnerable to social engineering.
Furthermore, the current regulatory approach—KYC, AML, sanctions screening—is reactive. The fraudsters likely used a non-KYC exchange or a peer-to-peer method to convert funds. The compliance costs are passed to honest users, who must verify more channels and jump through more hoops. The contrarian insight: the real vulnerability is not the lack of regulation but the over-reliance on regulation as a trust mechanism. The fraudsters bypassed regulation by staying off the radar. The market needs cryptographic verification of identity, not legal verification.
Takeaway: Vulnerability Forecast – The AI-Crypto Convergence Will Amplify Impersonation
As AI companies begin to tokenize their services or issue governance tokens, the impersonation attack surface will expand. The next wave of fraud will target AI startups using crypto-native fundraising methods. The vulnerability forecast is clear: without a robust on-chain identity verification standard—such as verified ENS domains, on-chain attestations, or zero-knowledge proofs of corporate registration—impersonation will become the primary vector of mass financial loss. The Kimi case is a warning shot. The trust layer is broken. Fixing it requires not just better code, but better cryptographic verification of social entities. The question is: will the market wake up before the next wave hits?