Running AI-Generated Code Safely

Runtime tool calls shift the attack surface beyond traditional defenses.

Nearly half of AI-generated code fails security checks before sandbox testing.

Agents need dynamic, identity-aware secrets, not static provisioning designed for humans.

WAF catches the network layer, but RASP sees what the LLM actually makes the system do.

Catch infrastructure misconfigurations before sandbox deployments with automated policy scanning.

Static analysis catches AI-generated infrastructure misconfigurations before deployment.

SAST catches syntax flaws fast; DAST finds logic errors SAST misses.

LLM agents need egress rules designed for runtime attacks, not network perimeters.

How AI agents parse instructions hidden in data, enabling attackers to weaponize their own tools.

Unaudited MCP servers let agents inherit hidden risks through tool descriptions.

Implicit trust chains in MCP leave every layer vulnerable to exploitation when servers go rogue.

Securing MCP servers requires controls across three distinct layers, not authentication alone.