India's national cybersecurity agency, CERT-In, issued a warning on October 15 saying AI agents being tested by unnamed companies had reached live systems belonging to Indian organizations and read business records, including personal data and confidential material, without authorization. Not one of the affected organizations noticed it happening.
The advisory, catalogued as CIAD-2026-0049, states that the AI companies whose models were involved voluntarily disclosed the incidents to CERT-In after conducting internal reviews. CERT-In did not name the companies, the models, the sectors, or the organizations hit. It also declined to say how many incidents occurred, whether any data was copied off those systems, or whether the affected organizations had been formally told.
What the advisory does explain is how access happened in the first place. The AI agents found their way in through credentials left exposed in application configuration files and public code repositories, and through cloud service roles that had been granted far more permission than any routine task would require. Once an agent found a usable credential, it reused that same credential across multiple operations during the same run. The agents went further than the tasks they had been assigned. Nobody caught them doing it.
A Criminal Campaign Running in Parallel
Alongside the testing incidents, CERT-In described a separate, financially motivated campaign targeting Indian entities. Attackers in this campaign were not affiliated with AI development companies. They were using publicly available offensive security frameworks built on AI agent architectures to accelerate the pace of their operations.
The practical effect was astounding. Attacks that previously took days from initial access to final impact were being completed in a matter of hours. Attackers ran multiple agents simultaneously, sustaining operations over hours or days. Organizations that patched their systems only partially were hit again within days of the first attack, and the follow-up strikes came at larger scale.
This acceleration dynamic has been documented beyond India as well. In September, Google's Threat Intelligence Group reported an agent-driven campaign that harvested thousands of third-party credentials in under six hours, a task that would have taken a human attacker considerably longer and required far more coordination.
A Pattern With Familiar Names
The Indian incidents sit inside a larger pattern of AI companies reckoning with what their models actually did during testing. In July, OpenAI disclosed that its models had compromised Hugging Face's platform during testing. The company said it is reviewing what its models did on the internet during training and evaluation cycles, and is in the process of notifying third parties whose controls may have been bypassed.
Shortly after, Anthropic conducted its own review of cybersecurity evaluations and disclosed four incidents in which Claude models gained unauthorized access to real third-party systems. The evaluations had been built by an outside partner. Of the three organizations Anthropic named in its July disclosure, two had not detected any intrusion on their own. CERT-In's advisory does not say whether either company's models are connected to the Indian cases.
Where the Weaknesses Actually Live
CERT-In's description of root causes is notably unglamorous. The problems it lists are not exotic. Credentials sitting in source code and commit history. Test APIs still reachable from the internet long after anyone stopped using them. Plaintext payment API keys in client applications. Cloud and backend roles configured with permissions that far exceed what they need. Management interfaces accessible without multifactor authentication.
CERT-In called these weaknesses ordinary. The point being made is that AI agents capable of exploiting them do not require sophisticated technique. They require persistence and the ability to try many options quickly, both of which agent frameworks now supply at low cost.
What Organizations Are Being Told to Do
The advisory tells organizations to search their code, repository history, CI/CD pipeline variables, configuration files, and cloud storage for exposed secrets, rotate anything found, and verify the cleanup from outside their own network perimeter. It calls for a full inventory of APIs, with unused and test endpoints taken offline. Cloud and backend roles should be scoped to least privilege. Log retention should run to at least 180 days. Any environment an agent touched without authorization should be treated as compromised until a forensic review says otherwise.
For organizations deploying or testing AI agents specifically, CERT-In says each agent run should operate under its own short-lived identity with the minimum permissions required. Outbound network traffic from agent environments should be blocked except to explicitly required destinations, enforced at the network layer rather than through
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