LLMjacking Campaigns Abuse Stolen Cloud and API Credentials to Hijack LLM Services, Google Warns
Google Threat Intelligence Group (GTIG) has reported a growing wave of LLMjacking attacks, in which threat actors steal cloud and API credentials to hijack corporate access to large language model (LLM) services and transfer usage costs to victim organizations. Attackers typically obtain access via stolen IAM credentials, service account keys, GitHub personal access tokens, and active session tokens, or by finding publicly exposed LLM endpoints and API keys. One actor tracked as UNC6993 generated new credentials and enabled additional cloud services in victim projects, racking up over US$100,000 in charges. Other groups enrolled compromised accounts in pay-per-use plans, ran high-volume inference on premium models, and used a "burn and rotate" technique—creating and deleting temporary service accounts—to evade usage limits while monitoring billing dashboards to gauge financial impact.
Detalhes técnicos
Beyond direct abuse, some actors resell stolen LLM access through an emerging criminal role called LLM Mixers, who aggregate, proxy, and resell unauthorized model access to underground market customers using open-source API gateways and custom relay frameworks. GTIG also observed a criminal deploying publicly accessible reverse proxies and custom Node.js frameworks to offer multi-model compatible services, as well as a container-based backdoor that queried an external LLM to interpret instructions and execute commands locally.
Remediação
GTIG recommends organizations increase visibility into their AI platforms, monitor for unauthorized changes to accounts and associated services, treat any unauthorized resource activation as a critical security incident, and enforce the principle of least privilege to limit permissions for users and service accounts.
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LLMjacking Campaigns Abuse Stolen Cloud and API Credentials to Hijack LLM Services, Google Warns
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Google Threat Intelligence Group (GTIG) has reported a growing wave of LLMjacking attacks, in which threat actors steal cloud and API credentials to hijack corporate access to large language model (LLM) services and transfer usage costs to victim organizations. Attackers typically obtain access via stolen IAM credentials, service account keys, GitHub personal access tokens, and active session tokens, or by finding publicly exposed LLM endpoints and API keys. One actor tracked as UNC6993 generated new credentials and enabled additional cloud services in victim projects, racking up over US$100,000 in charges. Other groups enrolled compromised accounts in pay-per-use plans, ran high-volume inference on premium models, and used a "burn and rotate" technique—creating and deleting temporary service accounts—to evade usage limits while monitoring billing dashboards to gauge financial impact.
Technical Details
Beyond direct abuse, some actors resell stolen LLM access through an emerging criminal role called LLM Mixers, who aggregate, proxy, and resell unauthorized model access to underground market customers using open-source API gateways and custom relay frameworks. GTIG also observed a criminal deploying publicly accessible reverse proxies and custom Node.js frameworks to offer multi-model compatible services, as well as a container-based backdoor that queried an external LLM to interpret instructions and execute commands locally.
Remediation
GTIG recommends organizations increase visibility into their AI platforms, monitor for unauthorized changes to accounts and associated services, treat any unauthorized resource activation as a critical security incident, and enforce the principle of least privilege to limit permissions for users and service accounts.