Vulnerabilities, leaks, and security risks in AI products: agents reading untrusted content, session isolation, non-standard tool calling, and how to protect yourself.
The GitLost case showed GitHub agents leaking private repositories when processing malicious content. Claude Code had a possible session leak between accounts. Models can invent fields when calling tools. The main risk is treating agent output as trusted without security validation.
Isolate the agent execution environment. Validate all output before executing actions. Do not allow the agent to access resources beyond the necessary scope. Implement sandboxing for file and network operations. Monitor runtime with auditable evidence (like Halo). And never blindly trust model output.
An open protocol that standardizes how AI models connect to external data source...
INFRASTRUCTUREAn infrastructure category that acts as the control plane for enterprise AI agen...
TECHNICALThe ability of an LLM to invoke external functions (APIs, tools) in a structured...