Adversarial Resilience

Part of Agent Skills™ by googleadsagent.ai™

Description

Adversarial Resilience is the practice of hardening AI agents against deliberate attacks — prompt injection, data exfiltration, sandbox escape, and unauthorized capability escalation. As agents gain more access to tools, APIs, file systems, and code execution environments, the attack surface grows proportionally. An agent that can write files and execute shell commands is a powerful ally but also a potent attack vector if its instructions can be manipulated by adversarial input.

This skill addresses the security challenges encountered in building the Buddy™ agent at googleadsagent.ai™, where user-provided Google Ads data could theoretically contain injection payloads embedded in campaign names, ad copy, or keyword lists. The defensive patterns here apply to any agent that processes untrusted input — which, in practice, is every agent. Even "internal" tools are vulnerable to indirect injection through documents, code comments, and API responses that contain adversarial content.

The defense model operates in layers: input sanitization strips known attack patterns before they reach the model, instruction anchoring makes the system prompt resistant to override, output filtering prevents sensitive data from leaking through responses, permission boundaries restrict what the agent can do regardless of what it is told to do, and audit logging creates a forensic trail of every action for post-incident analysis.

Use When

• Agents process any form of user-provided or external input
• The agent has access to sensitive tools (file write, shell execute, API calls)
• Deployed agents are accessible to users outside your trusted organization
• Compliance requirements mandate security controls for AI systems
• You need to protect against both deliberate attacks and accidental injection