OpenCode Hit by "Distillation Attack": API Keys of About 1.4% of Users Compromised
This time the attackers were not after passwords or credit card numbers. Open-source AI coding platform OpenCode admitted publicly on October 17 that attackers exploited a vulnerability in one of its legacy systems to gain unauthorized access to third-party model provider API keys stored in user workspaces.

What happened
OpenCode says the incident affected about 1.4% of its Console (hosted workbench) users who had linked external AI provider accounts. The keys were connected to models from Anthropic, OpenAI and Google. With the keys in hand, the attackers ran repeated queries against top-tier models; affected users reported their account balances draining quickly, with some bills climbing into the hundreds of dollars.
OpenCode characterized the incident as a large-scale "distillation attack": the attackers used the stolen keys to harvest high-quality outputs from top-tier models to train their own models, with the bills landing on the victims. Distillation describes a technique where one AI model learns by studying the answers of another.
OpenCode says it is working with Anthropic, OpenAI and Google on remediation, and advises all users who linked external provider accounts to revoke and rotate their API keys and review usage on each provider's dashboard. How the unauthorized charges will be handled has not been specified by OpenCode or the three providers.
Key facts
- Scope: About 1.4% of Console users — those who linked external model provider accounts
- Entry point: A vulnerability in a legacy system being retired, not the main service
- Keys in scope: API keys for Anthropic, OpenAI and Google models were all in scope
- Method: "Distillation attack": harvesting top-tier model outputs with stolen keys to train a competing model
- User impact: Some users' bills hit hundreds of dollars overnight; who covers the charges is unclear
Why it matters
What was stolen was not data but billable frontier-model access — a single key can yield training-data value. The convenience of storing keys for multiple providers in one workspace concentrates the risk along with it.
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