Enterprise adoption of AI connectors is growing fast, but a recent analysis warns that their dynamic nature and reliance on external services could expose sensitive data and multiply security risks beyond current governance frameworks.
- Over a third of AI connectors changed in just six weeks, altering permissions and capabilities.
- Many connectors send data to multiple external AI subprocessors, increasing data exposure.
- Security evaluations often miss the broader network of services called by connectors.
What happened
Connectors, the integrations allowing AI agents like OpenAI's ChatGPT and Anthropic's Claude to interact with third-party services such as Gmail, Slack, or Dropbox, have seen significant changes in functionality over recent weeks. PromptArmor analyzed 2,517 connectors and found that 37% had been altered within a six-week timeframe, with numerous new tools added and existing tool descriptions rewritten. This dynamic evolution introduces new ways AI models can manipulate user data and perform actions that may not have been initially considered during security reviews.
Moreover, some connectors have increased their capacity to write and even destructively modify data, as seen with Dropbox’s connector, which expanded from three write-capable tools to ten and introduced permissions that were not present before. With nearly 1,700 new tools integrated and many connectors behaving similar to intrusive websites by sending data to multiple AI subprocessors, the ecosystem’s complexity and risk have grown remarkably.
Why it matters
The expansion of connectors massively enlarges the attack surface for AI systems by bringing in sensitive data, untrusted inputs, and numerous potential actions that AI agents can take autonomously. This combination, described as the 'lethal trifecta' of private data access, untrusted content exposure, and external communication pathways, heightens the probability and impact of data breaches or unintended data exposure.
This is compounded by the fact that many security teams are unaware that connectors often trigger calls to additional external AI services beyond the primary connector vendor. These subprocessors, which can be numerous and spread across different jurisdictions, operate under varied terms and conditions, limiting the effectiveness of conventional security controls. For example, Anthropic itself warns that their enforcement of US-only inference settings does not extend to these third-party services, highlighting gaps in governance and compliance.
What to watch next
Organizations adopting AI connectors should intensify scrutiny of the full connector ecosystem, not just the immediate integration points, as new subprocessors and tool capabilities frequently appear. Continuous monitoring of connector permissions, data flows, and subprocessors is essential to manage evolving risks effectively.
Security vendors and AI developers are likely to focus on providing enhanced transparency and controls around connector behavior, permissions, and third-party data handling. Enterprises may need to demand more stringent contractual terms and audit rights for third-party processors involved in AI service chains. Meanwhile, regulatory bodies could start examining these expanded data flows to consider new guidelines or compliance mandates for AI service interoperability.