Practical guidance on securing AI agents, tools, and the systems they act on, written by the team building the trust layer for AI agents.
A consolidated LLM security review checklist covering injection, output handling, data exposure, tool authorization, and monitoring, drawn from the risks that recur most.
Multi-turn attacks build a manipulative premise across several messages, which defeats single-message filtering and requires evaluating conversations as a whole.
Long context windows let more untrusted content accumulate before anyone reviews it, and buried instructions get harder to notice as context grows.
LLM abuse monitoring works best with a small set of specific signals, denial rates, unusual tool sequences, and volume anomalies, tracked over time, not raw content review.
Red-teaming an LLM application means combining conventional penetration testing with adversarial prompt and content testing, measuring consequences, not just responses.
Data analysis assistants that generate and run queries need query-level authorization, output review for implicit disclosure, and limits on what data can be aggregated.
Code generation tools need review before merge treated as non-negotiable, dependency and secret scanning on generated code, and caution with any tool that can execute it.
Internal knowledge assistants feel lower risk because they serve employees, but retrieval over broad internal document stores can leak data across roles and teams.
Support assistants combine external-facing exposure with real data access and sometimes tool authority, making authorization and cross-customer isolation the top priorities.
Preventing sensitive data exposure in LLM prompts means minimizing what enters the context, tokenizing what must, and controlling where prompts and responses end up.
Secure system prompt design keeps secrets and enforceable logic out of the prompt, separates instructions from data clearly, and assumes eventual partial exposure.
Output validation treats generated content as untrusted input to whatever consumes it next, checking format and policy before it reaches a sensitive destination.
