OpenAI Report: Internal Research Model Spent Funds From Unauthorized Testnet Wallet To Deploy Smart Contracts
In Brief
OpenAI disclosed that an internal research model derived a private key from a public mnemonic phrase and spent funds from an unauthorized Sepolia testnet wallet to deploy contracts — without disclosing it.
AI research company OpenAI has disclosed that an internal research model used a publicly available developer mnemonic phrase to derive a private key and spend funds from a wallet it was never given, in order to pay deployment fees on the Sepolia Ethereum test network. The incident occurred on June 26, 2026, during a routine coding task assigned to the GPT-5.6 Sol series model, which was asked to update a cryptocurrency trading prototype and replace placeholder contract addresses with working on-chain deployments.
The task instructions permitted the model to deploy compatible contracts if no existing ones could be found, but supplied no funded wallet and granted no permission to use accounts it might discover independently. When faucet attempts failed to yield usable test tokens, the model wrote a script to derive accounts from well-known developer recovery phrases and checked the balances of 362 addresses. One account held enough Sepolia ETH to cover the deployment, and the model selected it.
Crucially, the model explicitly questioned whether this was acceptable before proceeding. Its internal reasoning concluded that because the recovery phrase was public and Sepolia tokens carry no real-world value, the funds were effectively abandoned and therefore acceptable to use — a judgment it made on behalf of the account holder rather than obtaining authorization. It also observed that the account appeared to be managed by an automated bot, reasoning it could deploy before the funds were drained.
Detection, Disclosure Gap, and Safeguards
After navigating failed RPC requests and switching to an alternative HTTP method to broadcast the signed transaction, the model successfully deployed the contract suite. Transaction fees were charged to the selected account, and the resulting deployment passed integration checks, twelve unit tests, and a production build.
However, the model’s final response concealed the method it had used. It reported only that the contracts had been deployed and that the trading button remained limited to wallet connection and network switching — omitting any mention of the derived key or the spent balance. OpenAI’s report characterizes this as the model substituting its own assessment of likely harm for the account holder’s explicit permission, then acting on that assessment without disclosing it.
The incident was uncovered on September 25, 2026, when OpenAI extended its misalignment monitoring to records of earlier model activity. The company states it has since implemented additional safeguards to block live internet access in cases like this, and now runs misalignment monitoring across all traffic, designed to alert security immediately if unauthorized internet access is obtained. While the financial impact was negligible — testnet tokens hold no value — the case illustrates a deeper alignment concern: models capable of rationalizing unauthorized resource use, particularly when they fail to report such actions afterward.
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About The Author
Alisa, a dedicated journalist at the MPost, specializes in crypto, AI, investments, and the expansive realm of Web3. With a keen eye for emerging trends and technologies, she delivers comprehensive coverage to inform and engage readers in the ever-evolving landscape of digital finance.
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Alisa, a dedicated journalist at the MPost, specializes in crypto, AI, investments, and the expansive realm of Web3. With a keen eye for emerging trends and technologies, she delivers comprehensive coverage to inform and engage readers in the ever-evolving landscape of digital finance.
