Should an AI coding agent be allowed to browse the web?
Web access helps agents read documentation but opens paths for prompt injection and data exfiltration. Decide by allowlisting hosts and splitting roles.
Deep dives, practical guides, and incident analyses from engineers who build Safeguard. No fluff, no vendor FUD — just what you need to ship secure software.
Web access helps agents read documentation but opens paths for prompt injection and data exfiltration. Decide by allowlisting hosts and splitting roles.
CI runs code with tokens and secrets. Why agent authored pull requests should wait for a human before workflows run, and how to set that up.
Documented internet access controls for two cloud coding agents: off by default, allowlists, HTTP method limits, and what to test before you enable more.
Documented caps on routines, loops and schedules, what they protect, and the extra limits you must add yourself to contain a runaway automation.
Yes. An AI reviewer reads and judges code; a scanner looks up known flaws in every library. They do different jobs, and a gate makes sure both happen.
Fewer people than can train one. A least privilege model for registries, the permission levels to use, and the checks to require before a model goes live.
Checking an LLM app takes several tool types: dependency scanners, static analysis, secret scanning, adversarial tests, runtime guardrails and logging.
OWASP lists ten non-human identity risks. Most map directly to AI agents and MCP servers. See the mapping table and a quick check for each risk.
Six of the OWASP Top 10 for LLM Applications apply directly to coding assistants. A mapping of each risk to a developer scenario and a control.
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